Patrones de introducción deliberada, predicción de invasiones y análisis de impactos de especies de plantas ornamentales en España

2021 Tesis Doctoral Álvaro Bayón Medrano PATRONES DE INTRODUCCIÓN DELIBERADA, PREDICCIÓN DE INVASIONES Y ANÁLISIS DE IMPACTOS DE ESPECIES DE PLANTAS ORNAMENTALES EN ESPAÑA Álvaro Bayón Medrano Tesis Doctoral 2021 Universidad de Sevilla No debe sorprendernos que las especies de una región cualquiera sean derrotadas y suplantadas por las producciones naturalizadas procedentes de otra tierra. Charles Darwin, El Origen de las Especies. Sugerencia de cita: Bayón, Á., 2021. Patrones de introducción deliberada, predicción de invasiones y análisis de impactos de especies de plantas ornamentales en España. Tesis Doctoral. Universidad de Sevilla, España. Las ilustraciones de portada y de presentación de cada capítulo fueron realizadas por Leire García “Irashila”, @N_trophy, quien es propietaria intelectual. Queda prohibida cualquier forma de reproducción, distribución, comunicación pública o transformación de las mismas sin la autorización expresa y previa de la autora. TESIS DOCTORAL ❦ PATRONES DE INTRODUCCIÓN DELIBERADA, PREDICCIÓN DE INVASIONES Y ANÁLISIS DE IMPACTOS DE ESPECIES DE PLANTAS ORNAMENTALES EN ESPAÑA Álvaro Bayón Medrano Sevilla, febrero de 2021 Estación Biológica de Doñana, CSIC Dpto. de Ecología Integrativa Universidad de Sevilla Facultad de Biología Programa de doctorado de Biología Integrada PATRONES DE INTRODUCCIÓN DELIBERADA, PREDICCIÓN DE INVASIONES Y ANÁLISIS DE IMPACTOS DE ESPECIES DE PLANTAS ORNAMENTALES EN ESPAÑA Memoria presentada por el Licenciado en Biología y Master en Valoración de Riesgos Naturales Álvaro Bayón Medrano para optar al título de Doctor por la Universidad de Sevilla. Fdo. Álvaro Bayón Medrano Sevilla, 2021 Firmado digitalmente por BAYON MEDRANO ALVARO el día 04/02/2021 con un certificado emitido or AC FNMT 1139157228007045297580178 Índice RESUMEN 1 SUMMARY 5 INTRODUCCIÓN GENERAL 9 El proceso de introducción de especies no nativas 9 Consecuencias de la introducción de especies no nativas 12 Prevención, priorización y listado de especies no nativas 13 Factores determinantes de la introducción de especies 15 OBJETIVOS DE LA TESIS 17 CAPÍTULO 1. HORIZON SCANNING TO IDENTIFY INVASION RISK OF ORNAMENTAL PLANTS MARKETED IN SPAIN 19 CAPÍTULO 2. PROPORTION OF NON-NATIVE PLANTS IN URBAN PARKS CORRELATES WITH CLIMATE, SOCIOECONOMIC FACTORS AND PLANT TRAITS 47 CAPÍTULO 3. INVASION RISKS AND SOCIAL INTEREST OF NON-NATIVE WOODY PLANTS IN URBAN PARKS 75 DISCUSIÓN GENERAL 99 Especies ornamentales invasoras y con potencial invasor disponibles en España 99 Priorización de especies invasoras 104 Impactos potenciales de las especies invasoras y sus interacciones 104 Rasgos determinantes en la introducción de especies no nativas 106 CONCLUSIONES GENERALES 109 GENERAL CONCLUSIONS 111 BIBLIOGRAFÍA 113 APÉNDICES 131 Apéndices del Capítulo 1 131 Apéndices del Capítulo 2 145 Apéndices del Capítulo 3 153 AGRADECIMIENTOS 159 1 Resumen La introducción de especies no nativas invasoras es de gran preocupación en el contexto del cambio global debido a los impactos ambientales y socioeconómicos (por ejemplo, sobre la salud humana, sobre infraestructuras, sobre agricultura y silvicultura) que pueden causar. Para las especies de plantas no nativas, la introducción deliberada de ornamentales es una de las vías de entrada más importantes. En el proceso de intoducción de plantas ornamentales, los valores estéticos y de utilidad son muy relevantes para seleccionar ciertos rasgos, pero también hay características que pueden ser seleccionadas para hacer frente a las condiciones climáticas del área de plantación. Por lo tanto, para evitar la introducción y propagación de las especies ornamentales más dañinas es importante identificar las especies que pueden causar mayores impactos, así como sus rasgos biológicos en relación con las características geográficas y socioeconómicas de las ciudades en que están plantadas. En esta tesis, analizamos los rasgos morfológicos y funcionales asociados con el uso de plantas ornamentales no nativas. También hemos evaluado el potencial invasor de estas especies y los impactos que pueden causar. Con esta información presente, hemos establecido listas para la priorización de especies para el manejo, que tienen como objetivo establecer protocolos para prevenir invasiones biológicas. Las especies del estudio fueron 914 especies de plantas comerciales de 21 grandes viveros, y 474 especies de plantas leñosas de 46 parques y jardines urbanos de España. Como resultado principal de nuestra evaluación, hemos agrupado las especies en seis listas, de la siguiente manera: Listas de Prioridad. Compuestas por ocho especies invasoras reguladas, que no deberían venderse en viveros, cuatro de ellas plantadas de manera irregular en parques urbanos. Destacamos una especie, Ailanthus altissima, debido a que aparece en casi la mitad de los parques y está disponible en el comercio, pero también está regulada como especie invasora tanto por la legislación española como europea. La presencia de estas especies a la venta en viveros o plantadas en parques urbanos indica que la regulación no se está aplicando con la suficiente eficiencia. Listas de Atención. Compuestas por especies invasoras y potencialmente invasoras con alto nivel de causar impactos potenciales, tanto ambientales como socioeconómicos. La lista de atención está compuesta por 68 especies vendidas en viveros y 47 especoes presentes en parques y jardines. Ambas listas de atención —la de los viveros y la de los parques— tienen especies en común, como Robinia pseudoacacia y Eucalyptus globulus, ambas especies con un elevado potencial de invasión. Listas de Vigilancia. Estas listas contienen especies invasoras y potencialmente invasoras con pocos o ningun impacto potencial. Las listas de vigilancia tienen 27 especies en viveros y 12 especies en parques. Ambas tienen especies en común. Listas Verdes. Formadas por especies que no tienen potencial invasor. Solo seis especies han sido identificadas, y solo una especie leñosa, Ficus benjamina, está en la lista verde de parques. El resto de las especies fueron incluidas, ya fuera en Listas de Incertidumbre (es decir, especies cuyo potencial de invasión no pudimos analizar, y que requieren de una revisión adicional una vez que se disponga de mejores datos) o en Listas de Datos Deficientes (es decir, especies para las que no había suficiente información que analizar). 2 Además, para clasificar las especies de las listas de atención y vigilancia, proporcionamos un sistema novedo semicuantitativos denominado “Índice de Prioridad”, basado en cuatro factores: los impactos ambientales y socioeconomicos que la especie puede causar, su potencialidad de invasion cuantificada por una evaluación de riesgos de malezas (WRA, por sus siglas en inglés ‘Weed Risk Assessment’) establecido por primera vez en Australia, y un indicador del interés de la especie para las personas, el valor de tendencia estándar (STV, por sus siglas en inglés ‘Standard Trending Value’) cuantificado mediante Google Trends. Hemos comprobado que para las especies de viveros, no existe relación entre su estado de invasion (es decir, invasora, naturalizada, subespontánea o aún ausente en la naturaleza) en España y su STV, ni con su puntuación de WRA, ni con la puntuación del Índice de Prioridad. Esto implica que el estado de una especie potencialmente invasora no influye en ninguno de los factores del Índice de Prioridad. Sin embargo, para las especies en los parques, encontramos que las especies con mayor STV tienen una mayor ocurrencia. Este resultado sugiere que la población puede realizar más búsquedas en línea de aquellas plantas que ven con mayor frecuencia en su entorno urbano. Las especies no nativas plantadas en parques urbanos pueden causar gran número de impactos en los ecosistemas naturales. Hemos documentado que la competencia y la modificación de los habitats pueden ser los impactos más comunes, seguido del daño a la salud humana. Cada especie con potencial de causar estos impactos están presentes, de media, en más de siete parques, y desafortunadamente, muchas especies pueden causar varios impactos a la vez. El grupo de impactos más relevante que puede causar una especie no nativa es la competencia, la toxicidad, la alteración de la sucesión natural y los daños en las estructuras del hábitat. También puede haber una alta correlación entre la toxicidad y la alelopatía, la agricultura y los daños a la salud humana. Hemos hallado que los parques con mayores temperaturas en el mes más frío tienen más plantas no nativas con flores conspicuas, lo que está relacionado con la polinización entomófila. Además, los parques con mayor rango de temperaturas anuales tienen menor tolerancia a la sombra y mayores valores de perfil de rusticidad. La rusticidad es un valor que cuantifica la habilidad para sobrevivir a la adversidad climática; menores valores de rusticidad implica supervivencia a menores temperaturas; el perfil de rusticidad de un parque es el valor mediano de la rusticidad de todas las especies de ese parque. Esas correlaciones muestran un posible sesgo de selección en la introducción de especies no nativas basada en sus rasgos, dependiendo de las condiciones climáticas de la ciudad en que han sido introducidas. Es probable que los rasgos de las plantas y los factores climáticos y socioeconómicos asociados con la introducción de las especies de plantas sean diferentes a los que se relacionan con el proceso de invasión. Por tanto, en esta tesis analizamos cómo la proporción de especies no nativas respecto al total de especies, y la proporción de especies establecidas (naturalizadas e invasoras) respecto al total de especies no nativas, se relacionan con las variables climáticas y socioeconómicas, y con los perfiles de rasgos de plantas de los parques. Hemos encontrado una menor proporción de especies no nativas en parques donde el rango mediano de rusticidad de las especies que los componen es mayor. Esto es probablemente debido a un sesgo en la introducción de especies, en el que la estética y otras características son más relevantes que la tolerancia climática. Además, hemos encontrado mayor proporción de especies no nativas en ciudades donde los hogares son mayores —relacionado con grupos familiares— y donde los habitantes 3 son más ancianos, probablemente debido a que la gente joven está más concienciada sobre el medio ambiente. Sin embargo, el efecto de los grupos familiares es inverso, probablemente debido a la limitada cantidad de información, a una posible percepción positiva de la presencia de especies no nativas, o al hecho de que estas personas carezcan de voz directa en la toma de decisiones. Respecto a la proporción de especies establecidas, encontramos una correlación negativa con el rango de temperatura anual. Esto indica que las especies no nativas plantadas en ambientes poco continentales tienen una mayor propensión a establecerse, y por lo tanto, a invadir. Por lo tanto, se debe tener mucha precaución en los parques ubicados en áreas costeras y valles fluviales. En general, esta tesis destaca las dimensiones multivariantes que actúan cuando la sociedad selecciona especies ornamentales no nativas. Muchas de esas introducciones pueden terminar en invasiones y causar impactos. Esta tesis ha desarrollado una herramienta de base científica para evaluar estos riesgos. Esta herramienta puede ser utilizada por los administradores ambientales para priorizar y seleccionar qué especies de plantas no nativas se pueden plantar como ornamentales sin causar efectos nocivos a la sociedad y a los sistemas naturales. 5 Summary The introduction of non-native invasive species is of great concern in the context of global change because the environmental and socioeconomic impacts (e.g. on human health, on infrastructures, on agriculture and forestry) they can cause. For non-native plant species, the deliberate introduction of ornamentals is one of the most important pathways of entry. In the process of introduction ornamental plants, aesthetic and utility values are very relevant to select for certain traits, but there are also traits that might be selected to cope with the climatic conditions of the planting area. Therefore, to prevent the introduction and spread of the most harming ornamental species it is important to identify the species that might cause the greatest impacts as well as their biological traits in relation to geographical and socioeconomic characteristics of the towns where they are planted. In this thesis, we have analyzed the morphological and functional traits associated with the use of ornamental non-native plants. We have also assessed the invasive potential of these species and the impacts they might cause. With this information in hand, we have established lists for prioritizate of species for management which aim to establish protocols to prevent biological invasions. The species of study were 914 commercial plant species from 21 large nurseries, and 474 woody plant species from 46 urban parks and gardens in Spain. As the main result of our assessment, we have grouped the species in six lists as follow: Priority lists. Composed by eight invasive regulated species, that should not be sold by nurseries, four of them undudly planted in urban parks. We highlight one species, Ailanthus altissima, because it appears in almost half of the parks and is available in commerce, but is also regulated as invasive species by both the Spanish and European legislation. The presence of these species sold in nurseries or planted in urban parks indicates that the regulation is not being applied efficiently enough. Attention lists. Composed by invasive and potential invasive species with high level of causing potential impacts, both environmental and socioeconomic. The attention list is composed by 68 species sold in nurseries and 47 species present in parks and gardens. Both attention lists —the one of nurseries and the one of parks— have species in common, such as Robinia pseudoacacia and Eucalyptus globulus, both species showing a high potential of invasion. Watch lists. This lists contains invasive and potential invasive species with few or no potential impacts. The watch lists have 27 species in nurseries and 12 species in parks. Both lists have species in common. Green lists. Formed by species with no invasion potential. Only six species in nurseries were identified, and only one woody species, Ficus benjamina, is in the green list of parks. The rest of species where included, either in uncertainty lists (i.e. species whose potential of invasion we were unable to analyze, and which require further review once better data becomes available) or in data deficient lists (i.e. species for which there was not enough information to analyze). Furthermore, to rank species from the attention and watch lists we provide a novel semi-quantitative system named Priority Index, based on four factors: the environmental and socioeconomic impacts the species can cause, their invasive potential 6 quantified by a weed risk assessment (WRA) first stablished in Australia and an indicator of the interest of the species to people, the standard trending value (STV) quantified by Google Trends. We have shown that for nursery species, there is no relation between their invasion status (i.e. invasive, naturalized, casual or are yet not present in the wild) in Spain and STV, nor with the WRA score or the Priority Index score. This implies that the status of a potential invasive species does not influence on any of the factors of the priority index. However, for species in parks we found that species with higher STV have a higher occurrence. This result suggests that population can perform more online searches for those plants that they see most frequently in its urban environment. Non-native species planted in urban parks could cause many impacts in natural ecosystems. We documented that competition and modification of habitats could be the most common potential impacts, followed by harm to human health. Each species with potential to cause these impacts are present on average in more than seven parks and unfortunately many species could cause several impacts at a time. The most relevant cluster of impacts a non-native species can cause is competition, toxicity, alteration of natural succession, and damages on habitat structures. There can also be a high correlation between toxicity and allelopathy with agriculture, and with damages to human health. We found that parks with highest temperatures in the coldest month have more non-native plants with conspicuous flowers, which is related to entomophilic pollination. Moreover, parks with the highest range of annual temperatures have lower tolerance to shadow and higher values of hardiness profile. The hardiness is a value that quantifies the ability to survive to climatic adversity; lower values of hardiness implies survival at lower temperatures; the hardiness profile of a park is the median value of the hardiness of all the plant species of this park. These correlations show possible selection biases in the introduction of non-native species based on their traits depending on the climatic conditions of the town where they have been introduced. The plant traits and the climatic and socioeconomic factors associated with the introduction of plant species are likely to be different to those related to the invasion process. Therefore, in this thesis, we analyzed how the proportion of non-native species respect to the total of species, and the proportion of established (naturalized and invasive) species respect to the total of non-native species, is related to climatic and socioeconomic variables, and to the plant trait profile of the parks. We found lower proportion of non-native species in parks where the median hardiness range of the species that compose them is higher. It is probably due to a bias in the introduction of species, in which aesthetics and other characteristics are more relevant than climatic tolerance. Furthermore, we found higher proportion of non-native species in cities where households are larger —related to family groups— and where inhabitants are older, probably due to the fact that younger people have higher awareness about environment. However, the effect on family groups is the reverse, probably due to the limited amount of information, a positive view of the presence of non-native species, or the fact that these people lack a direct voice in decision-making. Regarding the proportion of established species, we found a negative correlation with the annual temperature range. This indicates that non-native species planted in low continental environments have higher propensity to establish and, therefore, to invade. Therefore, high caution should be taken in parks located in coastal areas and river valleys. Overall, this thesis highlights the multivariate dimensions acting when society select ornamental non-native species. Many of these introduced species could become invasive and cause impacts. The thesis has developed a scientific based tool to assess 7 these risks. This tool can be used by environmental managers to prioritize and select which species on non-native plants can be planted as ornamental without causing harming effects to the society and to natural systems. 15 que se puede utilizar para pronosticar el consumo y el comercio en distintos sectores de la sociedad (Vosen and Schmidt, 2011). Google Trends proporciona información sobre con qué frecuencia se ha buscado una palabra clave o grupo de palabras clave en Internet a escala global o en un marco geográfico o temporal específico. Esta herramienta, aplicada a la jardinería, podría relacionarse con la presión de los propágulos de plantas no nativas. Por lo tanto, el valor cuantitativo obtenido para cada especie ornamental mediante Google Trends puede proporcionar información sobre el interés social de una especie en particular El valor resultante no está necesariamente compuesto solo por personas interesadas en comprar la planta. De hecho, es posible que algunas de estas búsquedas estén promovidas por el interés en controlar o manejar especies ya invasoras. En esta tesis doctoral, utilizamos los datos de Google Trends para evaluar la "popularidad" o el "interés" en sentido amplio, ya que la herramienta no permite distinguir si las búsquedas en Google de los nombres de plantas fueron motivadas por las opiniones positivas o negativas de los usuarios de Internet. El uso de esta herramienta como fuente de datos para análisis de riesgo de invasiones es novedosa ya que no se había utilizado antes dentro de el contexto de las invasiones biológicas. Factores determinantes de la introducción de especies Como se ha indicado previamente, en la constante búsqueda de novedades, seleccionamos de manera persistente y activa aquellos rasgos de las plantas que aumenten el valor estético de los parques y jardines según el contexto socioeconómico y geográfico (Kendal et al., 2012). Además, la introducción de especies no nativas en los parques y jardines constituye una fuente de servicios ecosistémicos y culturales, y un aporte de “biodiversidad” al ecosistema urbano (Schlaepfer et al., 2020). Por otro lado, y aparte de esos factores de interés que los entornos urbanos generan por la introducción de nuevas especies, existe también cierta relajación de los factores ambientales de estrés, como la sequía o la inundación, debido al efecto ejercido por las actividades de jardinería como el riego, la presencia de sistemas de drenaje, etcétera. Sin embargo, unas condiciones climáticas severas que superen la capacidad de regulación humana podrían limitar la importancia de las variables socioeconómicas a favor de las ambientales y las propias de la planta. Se espera, por tanto, que exista la posibilidad de que las distintas condiciones climáticas (i.e. temperaturas, precipitaciones) y socioeconómicas (i.e. tamaño de la ciudad, ingresos por habitante) estén influyendo en la selección de determinados rasgos de las plantas, que perfilen de algún modo el aspecto general de los parques y jardines (Castro-Díez et al., 2019). El conocimiento de estos perfiles de rasgos del ensamblaje de plantas ornamentales en un lugar determinado, junto con variables climáticas y socioeconómicas correlacionadas, aumentará la comprensión global de los procesos que impulsan la introducción y composición de las floras no nativas urbanas. Es necesario también discernir también entre especies nativas y no nativas, y en estas últimas, entre cuáles están establecidas —ya sean de subespontáneas, naturalizadas o invasoras— y cuáles no lo están. Se espera que los perfiles de rasgos de las plantas de los parques, las condiciones climáticas y socioeconómicas del lugar, por tanto, no solo determinen qué preferencias hay sobre el origen de las especies que se plantarán, sino que se relacionen también con su potencial para establecerse en el medio natural (Thuiller et al., 2005). Estas comparaciones resultan esenciales para identificar si aquellos rasgos que confieren a las especies capacidad de invasión están o no sesgados por los rasgos que el ser humano tome en cuenta para su introducción. 17 Objetivos de la tesis El objetivo general de esta tesis es analizar los patrones espaciales y rasgos asociados a la introducción de especies de plantas ornamentales no nativas, su potencial invasor y los impactos potenciales que pueden causar. Con esta información queremos establecer listas que permitan priorizar la gestión y la toma de decisiones sobre estas especies. Los objetivos específicos son los siguientes: 1. Conocer qué especies no nativas, invasoras o potencialmente invasoras se encuentran disponibles a la venta en viveros, o en los principales parques y jardines de España. 2. Identificar qué especies tienen el potencial de ocasionar una mayor cantidad de impactos en caso de establecerse e invadir el medio natural. 3. Establecer listas de atención y de vigilancia de especies de plantas disponibles en viveros y especies leñosas de parques y jardines urbanos basándonos en criterios de riesgo de invasión según los posibles impactos y el interés que despiertan en la sociedad. 4. Establecer listas de especies que puedan ser seguras, es decir, sin potencial invasor. 5. Comprobar cuáles son los rasgos más relevantes en la selección de plantas leñosas de los parques y jardines, en función de las condiciones socioeconómicas y climáticas de las ciudades de España donde se han plantado. 6. Comprobar las relaciones entre las proporciones de especies según su origen — nativas o no nativas— y, en las no nativas, según su estatus —establecidas o no establecidas—, con los perfiles de los rasgos de las plantas y las condiciones climáticas y socioeconómicas. 7. Analizar las relaciones existentes entre la ocurrencia de especies leñosas invasoras y potencialmente invasoras en parques españoles con factores relevantes del proceso de invasión. Estos objetivos se tratan en 3 capítulos de la tesis como sigue: Capítulo 1: ‘Horizon scanning para identificar riesgo de invasión en especies de plantas ornamentales comercializadas en España. Se resolverán los objetivos 1, 2, 3 y 4 referido a especies disponibles en viveros. Capítulo 2: ‘Proporción de plantas no nativas en parques urbanos correlacionada con el clima, factores socioeconómicos y rasgos de plantas’. Se resolverán los objetivos numerados como 5 y 6. Capítulo 3: ‘Riesgos de invasión e interés social en plantas leñosas no nativas en parques urbanos’. Se resolverán los objetivos 1, 2, 3 y 4 referidos a especies presentes en parques urbanos, así como el objetivo 7. 19 Capítulo 1. Horizon scanning to identify invasion risk of ornamental plants marketed in Spain Bayón, Á., Vilà, M., 2019. Horizon scanning to identify invasion risk of ornamental plants marketed in Spain. NeoBiota 52, 47–86. https://doi.org/10.3897/neobiota.52.38113 21 Resumen La horticultura es una de las principales vías de introducción deliberada de plantas no autóctonas, algunas de las cuales pueden volverse invasoras. De las 914 especies de plantas ornamentales comerciales de exterior que se venden en España, 700 (77 %) son especies comercializadas no autóctonas (excluidas las arqueófitas). Las hemos clasificado en seis listas diferentes en función de su estado de invasión en España y en otros lugares, su idoneidad climática en España y sus posibles impactos ambientales y socioeconómicos. Encontramos información suficiente para 270 especies. Proporcionamos una Lista de Prioridad de ocho especies invasoras reguladas que aún estaban disponibles en el mercado. También establecimos una Lista de Atención con 68 especies invasoras y potencialmente invasoras no reguladas que podrían causar diversos impactos. Para priorizar las especies dentro de la Lista de Atención, evaluamos más profundamente el riesgo de invasión de estas especies mediante el uso de una adaptación del protocolo WRA australiano y el nivel de interés social estimado a partir de los valores de la herramienta Google Trends. También proponemos una Lista Verde de siete especies que probablemente no tienen potencial para convertirse en invasoras, una Lista de Vigilancia con 27 especies potencialmente invasoras con pocos impactos potenciales y una Lista de Incertidumbre con 161 especies de estado conocido, pero con información insuficiente para incluirlas en cualquiera de las listas anteriores. No encontramos información suficiente para 430 (61 %) de las especies de plantas no nativas comercializadas, que fueron compiladas en una Lista de Datos Insuficientes. Nuestros hallazgos sobre especies prohibidas para la venta resaltan la necesidad de una aplicación más estricta de las regulaciones sobre especies de plantas invasoras en España. Además, nuestros resultados destacan la necesidad de información adicional sobre los impactos potenciales y la idoneidad climática de las plantas hortícolas que se venden en España, ya que no se pudo encontrar información suficiente para evaluar el riesgo de invasión para la mayoría de las especies. Palabras clave: Comercio electrónico, Google Trends, exploración de horizonte, evaluación de impacto, plantas no nativas, vivero, lista de prioridad, evaluación de riesgo 23 Abstract Horticulture is one of the main pathways of deliberate introduction of non-native plants, some of which might become invasive. Of the 914 commercial ornamental outdoor plant species sold in Spain, 700 (77 %) are non-native (archaeophytes excluded) marketed species. We classified these into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain and their potential environmental and socioeconomic impacts. We found sufficient information for 270 species. We provide a Priority List of eight regulated invasive species that were still available on the market. We also established an Attention List with 68 non-regulated invasive and potentially invasive species that might cause various impacts. To prioritise the species within the Attention List, we further assessed the risk of invasion of these species by using an adaptation of the Australian WRA protocol and the level of societal interest estimated from values of the Google Trends tool. We also propose a Green List of seven species with probably no potential to become invasive, a Watch List with 27 potentially invasive species with few potential impacts and an Uncertainty List with 161 species of known status but with insufficient information to include them in any of the previous lists. We did not find sufficient information for 430 (61 %) of the marketed non-native plant species, which were compiled into a Data Deficient List. Our findings of prohibited species for sale highlight the need for stronger enforcement of the regulations on invasive plant species in Spain. In addition, our results highlight the need for additional information on potential impacts and climate suitability of horticultural plants being sold in Spain, as insufficient information could be found to assess the invasion risk for most species. Keywords: e-commerce, Google trends, horizon-scanning, impact assessment, non-native plants, nursery, priority list, risk assessment 31 value —Robinia pseudoacacia—. The highest value for this species is set equal to 100, and this was assigned as our control species. Then, in a second systematic consultation of Google Trends we obtained the trend data for the rest of species comparing each one with the control, in order to standardize the values. For each species, we obtained a standard trend value (STV) as the highest value of the monthly trend in the complete temporal range of each species, relative to the optimal value of 100 of the control species. After that, we also performed a systematic consultation of Google Trends for the species in the Green List and the Priority List. We compared differences in STV of the Attention List species across the different invasion status groups of species within the list (i.e. not in the wild, casual, naturalized and invasive). We used R software to perform a multiple comparison using Tukey’s range test fitted in generalized linear model (glm) by quasi-Poisson regression. We also compared the STV of the species in the Priority list and the Green list, in order to check whether the STV index correlates with invasion itself. Invasion risk assessment of Attention List species We used an adaptation of the invasion risk assessment (WRA) protocol (Pheloung et al., 1999) for Spain (Gassó et al., 2010) in order to rank the species in the Attention list. The WRA scores range from -14 (benign species) to 29 (maximum risk). Three levels of invasion risk were considered: rejected, species likely to be high risk (score >6); accepted, species with a low score (<1); and species that need further evaluation, those with intermediate scores (1–6). We compared the scores of the WRA of the Attention List species across the different invasion status groups of species within the list (i.e. not in the wild, casual, naturalized and invasive). We used R software to perform a multiple comparison using Tukey’s range test fitted in generalized linear model (glm) by quasi-Poisson regression. Prioritization of Attention List species We calculated a Priority Index for each species in the Attention list based on impact assessment, WRA score and STV according to the following equation: "#$= & '((×*+ '' +'((×-+ .+'((×/01+ 23 +456$ 7 /4 ; where: PIi = Priority Index for species i; Ei = number of environmental impacts for species i; Si = number of socioeconomic impacts for species i; WRAi = Weed Risk Assessment score for species i; STVi = Standard Trend Value for species i. The impact factors were relative to the 11 environmental and 4 socioeconomic impacts which represent the maximum possible impacts in the assessment. The WRA- factor was relative to 29, which is the maximum possible value in the WRA protocol. The STV is already represented as a percentage, and thus no conversion is needed. Within each invasion status group, we listed species in decreasing order of their Priority Index, and highlighted those with a Priority Index at or above the median. Data resources The data underpinning the analysis, reported in this paper, are deposited in the Zenodo repository at https://doi.org/10.5281/zenodo.3367257 (Bayón and Vilà, 2019a) 32 Results Of the 914 taxa identified to species, 199 were native to Spain and 15 were archaeophytes. Of the 700 remaining non-native species, we did not find sufficient information on invasion status, climatic suitability or invasiveness elsewhere for 430 species (Data deficient list; Appendix 1.4) in the consulted databases (CABI, 2018; Invasive Species Specialist Group, 2015). For the remaining 270 non-native species, 71 taxa were not in the wild, 99 were casual, 70 naturalized, and 30 invasive in Spain. Of the invasive species in Spain, eight are regulated by the Spanish Catalogue of Non-na- tive Invasive Species (BOE, 2013) and one of them, Pennisetum setaceum, is also regulated by the List of Invasive Alien Species of Union Concern (European Commission, 2016, 2017). Nineteen species not in the wild, 30 casual and 24 naturalized species are climatically suitable and invasive elsewhere, and thus considered potential invaders. These species, in addition to the 22 already invasive non-regulated species, were assessed for impact (Table 1.1). Impact assessment We assessed the potential impact of the above-mentioned 19 not in the wild, 30 casual, 24 naturalized and 22 invasive species that are climatically suitable and invasive elsewhere. The global median value for environmental impacts was three and the median for socioeconomic impacts was one. Therefore, species with impacts at or above these values were included in the Attention List. This included eleven not in the wild (58 %), 22 casual (73 %), 20 naturalized (83 %) and 15 invasive species (68 %). We did not find significant differences in the number of environmental or socioeconomic impacts across any pair of species status groups (Fig. 1.2). Within each invasion status, the species with the highest number of environmental impacts included the invasive Robinia pseudoacacia (7), Agave sisalana (6) and Elaeagnus angustifolia (6); the naturalized Ficus pumila (7), Ficus rubiginosa (6), Lupinus polyphyllus (6) and Sansevieria trifasciata (6); the casual Wisteria sinensis (8) and Grevillea robusta (7); and the not in the wild Nymphaea odorata (7). Species with the highest number of socioeconomic impacts were the invasive Robinia pseudoacacia (3), Acacia longifolia (3) Eucalyptus globulus (3) and Lantana camara (3); the naturalized Lupinus polyphyllus (3), Rhus typhina (3) and Tagetes minuta (3); the casual Miscanthus sinensis (3), Portulaca oleracea (3) and Sesbania punicea (3); and the not in the wild Allamanda cathartica (3). Species listing The 270 non-native species with available data were classified into the following five lists: The Priority List contains eight regulated invasive species that were still commercially available in nurseries (Table 1.2). The Attention List (Table 1.3) is composed of 68 species, including 11 not in the wild, 22 casual, 20 naturalized and 15 invasive. 33 Figure 1.2. Environmental (a) and socioeconomic (b) impacts of invasive and potentially invasive ornamental plant species. P-values for Tukey’s range tests for environmental impacts: not in the wild – casual: p = 0.833; not in the wild – naturalised: p = 0.498; not in the wild – invasive: p = 0.926; casual – naturalised: p = 0.904; casual – invasive: p = 0.997; naturalised – invasive: p = 0.845. P-values for Tukey’s range tests for socioeconomic impacts: not in the wild – casual: p = 0.790; not in the wild – naturalised: p = 0.526; not in the wild – invasive: p = 0.916; casual – naturalised: p = 0.947; casual – invasive: p = 0.994; naturalised – invasive: p = 0.875. Dashed red line represents the global median of impacts and the threshold for species in the Attention List (at and above the line, Table 1.3) and Watch List (below the line, Appendix 1.2) 34 The Watch List contains 27 species: eight not in the wild, eight casual, four naturalized and seven invasive but below the threshold for environmental and socioeconomic impacts (Appendix 1.2). The Green List is represented by only seven species: one casual and six not in the wild taxa that are not climatically suitable nor invasive elsewhere (Table 1.4). Finally, the Uncertainty List was formed by 161 species, in which: 46 are not in the wild (27 with low confidence on climatic suitability, 15 not invasive elsewhere but climatically suitable, four not climatically suitable but invasive elsewhere), 69 are casual (4 with low confidence on climatic suitability, 58 with no available information on climatic suitability, six not invasive elsewhere but climatically suitable, and one not climatically suitable but invasive elsewhere) and 46 are naturalized not invasive elsewhere (Appendix 1.3). Societal interest analysis of Attention List species In the Google Trends systematic examination of the 68 Attention List species, maximum trend values were observed for Robinia pseudoacacia —March, 2004— and therefore we used this record as our control species. Within the Attention list, the most noteworthy species (higher STV) included: the invasive Robinia pseudoacacia (100) and Lantana camara (67); the naturalized Phoenix canariensis (71) and Melia azedarach (71); the casual Cestrum nocturnum (80) and Zantedeschia aethiopica (70); and, far from the previous groups, the not in the wild Leptospermum scoparium (43). Complete results of the STV analysis are shown in Table 1.3. There were no significant differences in STV between any pair of invasion status groups of species (Fig. 1.3). Similarly, there were no differences between the species in the Priority list and the Green List (p=0.967). Invasion Risk Assessment of Attention List species In the WRA, all species in the Attention List were rejected except for 11 that required further evaluation and only one, Annona cherimola, that was accepted (Table 1.3). Within each invasion status, he highest WRA scores were for the invasive Lantana camara (25), Acacia longifolia (23), Acacia saligna (22), Elaeagnus angustifolia (21), Eucalyptus globulus (21) and Leucana leucocephala (21); the naturalized Phragmites australis (27), Lupinus polyphyllus (27), Pennistum villosum (25), Canna indica (24) and Imperata cylindrica (24); the casual Kalanchoe daigremontiana (22), Pawlownia tomentosa (19) and Eugenia uniflora (18); and the not in the wild Cotoneaster horizontalis (26), Cornus sericea (22) and Physalis angulata (22). For every invasion status, the species requiring further evaluation accounted for less than 25 %. There were no significant differences in WRA scores between any pair of invasion status groups of species (Fig. 1.4). Prioritization of Attention List species The median value of Priority Indexes was 35. Species with a Priority Index ≥ 35 are highlighted in Table 1.3. Within each invasion status, the highest Priority Indexes in invasive species were found for Robinia pseudoacacia (73), Lantana camara (68), and Eucalyptus globulus (55); in naturalized species Lupinus polyphyllus (56) and Canna indica (54); in casual species Portulaca oleracea (54), Cestrum nocturnum (49), and 35 Wisteria sinensis (49); and in not in the wild species Cinnamimum camphora (45) and Cotoneaster horizontalis (45). There were no significant differences between any pair of invasion status groups of species (Fig. 1.5). N Climatic suitable Not Climatic suitable Low confidence in likelihood on climatic suitability Not enough information about climatic suitability Invasive elsewhere Not invasive elsewhere Total taxa in nurseries 1036 Taxa excluded 122 Total species listed 914 Native 199 Archeophyte 15 Non-native (non-archeophyte) 700 Invasive - Regulated 8 Invasive - Not Regulated 22 Naturalized 70 24 46 Casual 100 36 2 4 58 Casual - Climatic Suitable 30 6 Casual - Not Climatic Suitable 1 1 Not in the wild 71 34 10 27 Not in the wild - Climatic Suitable 19 15 Not in the wild - Not Climatic Suitable 4 6 Data defficient 430 Table 1.1. Classification of commercial ornamental plant taxa in Spain according to their invasion status and climatic suitability. Colour codes correspond to those in Fig 1: Priority List (red, Table 1.2), Impact Risk Analysis (orange), Green List (green, Table 1.4) and Uncertainty List (grey, Appendix 1.3). Species Family Native distribution Regulated in Spain Regulated in EU Acacia dealbata Fabaceae Aus Yes No Agave americana Agavaceae SAm Yes No Ailanthus altissima Simarubaceae As Yes No Buddleja davidii Scrophulariaceae As Yes No Cortaderia selloana Poaceae SAm Yes No Opuntia ficus-indica Cactaceae NAm Yes No Pennisetum setaceum Poaceae Afr Yes Yes Tradescantia fluminensis Commelinaceae SAm Yes No Table 1.2.!Priority List. Includes invasive species regulated by the Spanish Catalogue of Non-native Invasive Species (BOE 2013) or the List of Invasive Alien Species of Union Concern (European Commission 2016, 2017). Native distribution: Afr: Africa; As: Asia (Temperate); Aus: Australia; NAm: North America; SAm: South and Central America. a) Species not in the wild Family Native distribution Num. Impacts WRA Trends Prior. Index Environment Socioeconomics Score Evaluation STV Evaluation Cinnamomum camphora* Lauraceae As 4 2 17 Reject 35 Less Interesting 45 Cotoneaster horizontalis* Rosaceae As - AT 3 1 26 Reject 37 Less Interesting 45 Physalis angulata* Solanaceae NAm - SAm - Pac 4 2 22 Reject 0 Not Interesting 41 Allamanda cathartica* Apocynaceae SAm 4 3 11 Reject 0 Not Interesting 37 Nymphaea odorata* Nymphaeaceae NAm 7 2 9 Reject 0 Not Interesting 36 Leptospermum scoparium* Myrtaceae Aus 3 1 13 Reject 43 Less Interesting 35 Cornus sericea Cornaceae NAm 4 1 22 Reject 0 Not Interesting 34 Berberis thunbergii Berberidaceae As 4 1 12 Reject 27 Less Interesting 32 Alocasia macrorrhizos Araceae AT 4 0 13 Reject 0 Not Interesting 20 Euonymus fortunei Celastraceae As 4 0 7 Reject 0 Not Interesting 15 Archontophoenix cunninghamiana Arecaceae Aus 4 0 4 Evaluating 0 Not Interesting 13 b) Casual species Family Native distribution Num. Impacts WRA Trends Prior. Index Environment Socioeconomics Score Evaluation STV Evaluation Portulaca oleracea* Portulacaceae Afr - EUr 4 3 15 Reject 54 Interesting 54 Cestrum nocturnum* Solanaceae SAm 4 2 9 Reject 80 Very Interesting 49 Wisteria sinensis* Fabaceae As 8 2 9 Reject 43 Less Interesting 49 Kalanchoe daigremontiana* Crassulaceae Afr 5 1 22 Reject 37 Less Interesting 46 Pinus radiata* Pinaceae NAm 3 2 12 Reject 60 Interesting 45 Nandina domestica* Berberidaceae As - AT 5 2 9 Reject 43 Less Interesting 42 Casuarina equisetifolia* Casuarinaceae Aus 5 2 7 Reject 45 Less Interesting 41 Miscanthus sinensis* Poaceae AT 6 3 9 Reject 0 Not Interesting 40 Paulownia tomentosa* Paulowniaceae As - AT 4 1 19 Reject 27 Less Interesting 38 Zantedeschia aethiopica* Araceae Afr 0 1 15 Reject 70 Interesting 37 Physalis peruviana* Solanaceae SAm 5 2 8 Reject 22 Not Interesting 36 Grevillea robusta* Proteaceae Aus 7 1 2 Evaluating 45 Less Interesting 35 Sesbania punicea* Fabaceae SAm 2 3 13 Reject 0 Not Interesting 35 Gypsophila paniculata Caryophyllaceae As - Eur 6 1 6 Evaluating 17 Not Interesting 29 Eugenia uniflora Myrtaceae SAm 5 0 18 Reject 0 Not Interesting 27 Spiraea japonica Rosaceae As - AT 4 0 11 Reject 28 Less Interesting 26 Tecoma stans Bignoniaceae NAm - SAm 4 1 11 Reject 0 Not Interesting 25 Prunus serotina Rosaceae NAm 6 0 12 Reject 0 Not Interesting 24 Morus nigra Moraceae As 3 0 4 Evaluating 45 Less Interesting 22 Eucalyptus sideroxylon Myrtaceae Aus 3 0 14 Reject 0 Not Interesting 19 Yucca aloifolia Agavaceae NAm 3 1 4 Evaluating 0 Not Interesting 17 Cereus uruguayanus Cactaceae SAm 3 0 3 Evaluating 0 Not Interesting 9 c) Naturalized species Family Native distribution Num. Impacts WRA Trends Prior. Index Environment Socioeconomics Score Evaluation STV Evaluation Lupinus polyphyllus* Fabaceae NAm 6 3 27 Reject 0 Not Interesting 56 Canna indica* Cannaceae SAm 8 1 24 Reject 35 Less Interesting 54 Rhus typhina* Anacardiaceae NAm 5 4 15 Reject 0 Not Interesting 49 Phragmites australis* Poaceae NAm 2 2 27 Reject 35 Less Interesting 49 Tagetes minuta* Asteraceae NAm 4 3 22 Reject 0 Not Interesting 47 Imperata cylindrica* Poaceae AT 7 1 24 Reject 0 Not Interesting 43 Ficus pumila* Moraceae AT 3 3 5 Evaluating 35 Less Interesting 39 Phoenix canariensis* Arecaceae Afr 4 1 6 Evaluating 71 Interesting 38 Melia azedarach* Meliaceae AT - Aus 4 0 12 Reject 71 Interesting 37 Psidium cattleianum* Myrtaceae SAm 6 1 20 Reject 0 Not Interesting 37 Albizia julibrissin Fabaceae As 3 0 14 Reject 62 Interesting 34 Ficus rubiginosa Moraceae Aus 6 1 7 Reject 26 Less Interesting 32 Broussonetia papyrifera Moraceae As 5 2 2 Evaluating 27 Less Interesting 32 Ziziphus jujuva Rhamnaceae As - AT - Aus 5 1 17 Reject 0 Not Interesting 32 Pennisetum villosum Poaceae Afr 3 0 25 Reject 0 Not Interesting 28 Sansevieria trifasciata Asparagaceae Afr 4 0 12 Reject 35 Less Interesting 28 Bacopa monnieri Plantaginaceae NAm - SAm - As - Eur 2 1 16 Reject 10 Not Interesting 27 Adiantum raddianum Pteridaceae SAm 3 1 13 Reject 7 Not Interesting 26 Atriplex semibaccata Amaranthaceae Aus 3 0 15 Reject 0 Not Interesting 20 Annona cherimola Annonaceae SAm 1 1 0 Accepted 0 Not Interesting 9 d) Invasive species Family Native distribution Num. Impacts WRA Trends Prior. Index Environment Socioeconomics Score Evaluation STV Evaluation Robinia pseudoacacia* Fabaceae NAm 7 3 15 Reject 100 Very Interesting 73 Lantana camara* Verbenaceae SAm 5 3 25 Reject 67 Interesting 68 Eucalyptus globulus* Myrtaceae Aus 4 3 21 Reject 35 Less Interesting 55 Acacia longifolia* Fabaceae Aus 4 3 23 Reject 0 Not Interesting 48 Acacia saligna* Fabaceae Aus 5 1 22 Reject 23 Not Interesting 42 Leucaena leucocephala* Fabaceae NAm 5 0 21 Reject 35 Less Interesting 38 Elaeagnus angustifolia* Elaeagnaceae As 6 0 21 Reject 19 Not Interesting 36 Lonicera japonica* Caprifoliaceae As - AT 3 1 14 Reject 39 Less Interesting 35 Agave sisalana* Agavaceae SAm 6 2 10 Reject 0 Not Interesting 35 Psidium guajava Myrtaceae NAm 4 1 19 Reject 0 Not Interesting 32 Gleditsia triacanthos Fabaceae NAm 4 0 10 Reject 41 Less Interesting 28 Phormium tenax Xanthorrhoeaceae Pac 3 0 10 Reject 35 Less Interesting 24 Bidens aurea Asteraceae NAm 1 2 5 Evaluating 18 Not Interesting 24 Stenotaphrum secundatum Poaceae Afr 5 0 13 Reject 0 Not Interesting 23 Pasiflora caerulea Passifloraceae SAm 3 0 6 Evaluating 0 Not Interesting 12 Table 1.3. Attention List. Includes all invasive and potentially invasive species with ≥3 environmental or ≥1 socio-economic potential impacts, classified as: a) not in the wild, b) casual, c) naturalized and d) invasive species, presented in decreasing Priority Index order. Native distribution: Afr: Africa; As: Asia (Temperate); AT: Asia (Tropical); Aus: Australia; Eur: Europe; NAm: North America; Pac: Pacific; SAm: South and Center America. Weed Risk Assessment (WRA): scores 1-6 indicate that the species needs further evaluation; scores >6 indicate that the species is rejected. STV: Standard Trend Value (0-100). Priority Index is calculated following the equation: !"#$"%#&'() = + ,--×/0 ,, +,--×20 3+,--×456 78 +9:; < /4 , where: Ei = environmental impacts; Si = socioeconomic impacts. *Species with Priority index ≥35 has been highlighted with a red asterisk. 40 Species Family Native distribution Status in Spain Averrhoa carambola Oxalidaceae AT N Celosia argentea Amaranthaceae AT N Ficus benjamina Moraceae AT N Mangifera indica Anacardiaceae As - AT N Nelumbo nucifera Nelumbonaceae AT N Pogostemon helferi Lamiaceae AT N Senna corymbosa Fabaceae SAm C Table 1.4. Green List. Includes non-native non-invasive species with very low invasion potential. Native distribution: As: Asia (Temperate); AT: Asia (Tropical); SAm: South and Center America. Status in Spain: N: Not in the wild, C: Casual. Figure 1.3. Society interest in ornamental non-native plant species classified by their invasion status. STV: Standard Trend Value. P-values for Tukey’s range tests: not in the wild – casual: p = 0.373; not in the wild – naturalised: p = 0.783; not in the wild – invasive: p = 0.436; casual – naturalised: p = 0.794; casual – invasive: p = 1; naturalised – invasive: p = 0.860. 47 Capítulo 2. Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits Bayón, Á., Godoy, O., Maurel, N., van Kleunen, M., Vilà, M., 2020. Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits. Urban Forestry and Urban Greening; Under review. 49 Resumen Los parques y jardines urbanos brindan servicios culturales y estéticos críticos para el bienestar humano. Sin embargo, representan una de las principales razones de la introducción deliberada de especies ornamentales, algunas de las cuales pueden escapar y establecerse en la naturaleza. Los factores climáticos y socioeconómicos pueden modular qué especies se plantan. Aquí, probamos cómo se relacionan los rasgos de 486 especies leñosas ornamentales de 46 parques urbanos españoles con las variables climáticas y socioeconómicas. Evaluamos si los rasgos de las plantas, los factores climáticos y socioeconómicos distinguen a las especies no nativas según se hayan establecido o no en la región. En general, encontramos asociaciones claras entre los rasgos de las especies y las variables climáticas: más notablemente, los parques con mayor temperatura en el mes más frío tienen más plantas con flores llamativas; y estos, así como los de mayor rango de temperaturas anuales, tienen valores más altos de rusticidad y menor tolerancia a la sombra. El 82 % de las especies no son nativas. Un mayor tamaño de las viviendas y una mayor edad media de los habitantes de la ciudad se asociaron positivamente con una mayor proporción de especies no nativas en los parques urbanos, y se encontró una menor proporción para mayores rangos de rusticidad de las plantas. Además, entre las especies no nativas, un rango anual más alto de temperaturas se asoció con una menor proporción de especies establecidas en la naturaleza. Nuestros resultados muestran que los parques urbanos españoles tienen una proporción abrumadora de especies leñosas no nativas, algunas de las cuales tienen el potencial de establecerse, y que la variación en su proporción puede explicarse por factores socioeconómicos y climáticos. Palabras clave: Rusticidad, Especies invasoras, Plantas ornamentales, Perfiles de rasgos de plantas. 51 Abstract Urban parks and gardens provide cultural and aesthetic services critical for human well-being. Yet, they represent one of the main reasons for the intentional introduction of ornamental species, some of which can escape and establish in the wild. Climate and socioeconomic factors can modulate which species are planted. Here, we test how traits of 486 ornamental woody species from 46 Spanish urban parks relate to climatic and socioeconomic variables. We evaluated whether plant traits, climatic and socioeconomic factors distinguish non-native species according to whether or not they have established in the region. Overall, we found clear associations between species traits and climatic variables: most notably, parks with higher temperature in the coldest month have more plants with conspicuous flowers; and these ones, as well as these with higher range of annual temperatures have higher values of hardiness and lower tolerance to shade. Eighty-two percent of the species are non-native. Higher home sizes and a higher median age of the town inhabitants was positively associated with a higher proportion of non-native species in urban parks, and a lower proportion was found for the higher range of hardiness of the plants. Moreover, among non-native species, a higher annual range of temperatures was associated with a lower proportion of species established in the wild. Our results show that Spanish urban parks have an overwhelming proportion of non-native woody species, some of which have the potential to establish, and that the variation on their proportion can be explained by socioeconomic and climatic factors. Keywords: Hardiness, Invasive species, Ornamental plants, Plant trait profile. 53 Introduction Urban parks and gardens provide cultural and aesthetic services critical for human well-being (Bolund and Hunhammar, 1999; Hulme, 2007; Kendal et al., 2012), yet they are one of the main reasons for the intentional introduction of ornamental non-na- tive plant species (Mayer et al., 2017; van Kleunen et al., 2018). This source of nonnative species introduction is because in our continuous search of novelty, we are actively looking for plant features that increase the aesthetic value of urban parks while creating a sense of place. Previous work has shown that such search is modulated by the socioeconomic context of the towns within a country. For instance, towns with higher average incomes and more densely populated spend highly on planting a wide diversity of novel species with high aesthetic value (Vaz et al., 2018). Nevertheless, the decorative value is only one reason for planting non-native species in urban park. Other important reasons relate to the ability of some non-native species to cope with environmental stress. It has been well-documented that the distribution of tree species along climatic gradients is modulated by the particular organs and whole-plant traits that allow species to tolerate environmental stressors such as frost, shade, and drought (Rueda et al., 2017, 2018). Therefore, when a non-native species is introduced in a novel area, the interaction of climatic conditions with the species traits determine the likelihood that the plants will persist (Dullinger et al., 2017; Haeuser et al., 2017, 2018; van der Veken et al., 2008). This interactive process is not different in the case of urban floras, although gardening practices (e.g., watering, drainage systems for excess water, pruning) can overcome or compensate for some of these climatic limitations. The introduction of non-native woody species in urban parks can pose a conservation problem because some of those species can escape and establish populations (i.e. naturalization) in the wild and become invasive. Some of those have the potential to disrupt the functioning of natural ecosystems causing impacts, not only on the environment, but also on the socio-economy and human health (Hulme, 2007; Niinemets and Peñuelas, 2008; Parker et al., 1999). Although the number of naturalized species has increased exponentially during the last decades (Seebens et al., 2017)2/23/21 11:40:00 AM, we still have a poor understanding of the drivers influencing the introduction of non-native plant species into the urban flora compared to the drivers influencing the the latter stages of the invasion process. Along the invasion process from transport to spread and invasion (Blackburn et al., 2011; Colautti and MacIsaac, 2004), it has been well documented that species traits, climatic conditions and socioeconomic factors influence invasion success. These factors are also likely to influence the likelihood that a species has been introduced somewhere in the first place. However, the species traits determining invasion success might not necessarily be the same as those determining their deliberate introduction, since the latter depends directly on human perception, preferences and uses. With this knowledge at hand, what remains to be explored is how these three main components (i.e. species traits, climate and socioeconomic context) explain variation in the proportion of non-native ornamental species (non-native species number/total species number) in urban parks. Indeed, the relative importance of each factor is likely to vary across a large territory if there is variation in climatic and socioeconomic variables. For instance, severe climatic conditions might limit the importance of socioeconomic variables in determining the proportion of non-native ornamental species 54 due to direct environmental filtering processes. However, with a relaxation of stressful conditions due to mild climatic conditions or indirect effects mediated by human gardening activities, human population size or their income is expected to influence the proportion of non-native ornamental species planted in urban parks. Critically, these extrinsic factors are going to be mediated by plant traits (Vaz et al., 2018). Therefore, knowledge of the trait profiles of ornamental plant communities in concert with climatic and socioeconomic correlates will increase our understanding of the processes driving the introduction and composition of non-native floras in urban parks, and the establishment of these non-native species. In this regard, previous work has shown that socioeconomic factors explain as much of the variation of the distribution of non-native species established in the wild as climatic factors (Essl et al., 2011a; Pyšek et al., 2010). However, we do not know to what extent the proportion of tree species naturalized in the wild is associated with median species trait values of the parks, and their climatic and socioeconomic variables (Thuiller et al., 2005). In this study, we analyze 486 woody species planted across 23 towns in Spain to assess how 46 urban parks that vary in size and founding year differ in their traits. We specifically ask (1) how climatic and socioeconomic characteristics of the parks determine the plant-trait profile of the parks and (2) whether plant origin (native vs. nonnative) and the invasion status (established vs. not established in the wild) of the species is associated with plant traits, climatic and socioeconomic variables at the park level? To answer these questions, we focused our study on the peninsular territory of Spain (hereafter Spain) for several reasons. First, the ornamental use of non-native plant species in Spain is one of the main pathways of introduction of potential invasive species (Sanz Elorza et al., 2004). Recently, we identified 83 invasive or potentially invasive species that are available in nurseries (Bayón and Vilà, 2019b). Therefore, we can assess the trait profiles of the communities of non-native urban floras that are more likely to escape from parks and become invasive in natural landscapes. Second, Spain has a broad range of climatic conditions (from Mediterranean and semiarid to temperate and continental climates). We expect that inland cold conditions will impose a strong filter to non-native woodie species, while drought effects in some coastal areas in southern Spain will be ameliorated by irrigation during summer. Moreover, the drought periods characteristic for most climates in Spain has evolutionary limited the trait profile of native flora to develop species with a summer flowering period and dense foliage (Godoy et al., 2009), and we would expect this to be reflected in non-native species. Consequently, we expect a broader variation of trait profiles of non-native plants in coastal towns. Third, the towns selected are not equally wealthy across the country. In general, the wealth of a town and its unemployment rates might act as important drivers differentiating the non-native floras of towns (Schwartz et al., 2006; Trentanovi et al., 2013; Vaz et al., 2017). Methods Urban parks dataset We obtained floristic catalogs of 46 urban parks and gardens distributed in 23 towns across the Spanish peninsular territory (Appendix 2.1) from the ‘Vivirlosparques’ database (www.vivirlosparques.es/; last accessed 15 May 2017), an online tool by the ‘Spanish Association of Public Parks and Gardens’ (AEPJP, 2010). This database provides the most updated and complete information on the species planted, total park area, 55 the founding year and spatial coordinates of the parks. Missing information of area was completed by using Google Maps tools and, of year, by city hall websites. From these catalogs, we selected the complete list of woody plant species (i.e. trees, shrubs, arboreal cacti and palm trees). Herbs were excluded because their plantings can vary radically from year to year. Once the species list was obtained, we used The Plant List (2013) to harmonize scientific species names (www.theplantlist.org; last accessed 5 October 2018). Non-specific taxa, such as genera with unspecified species epithets or hybrids were excluded. In the case of infraspecific taxa (e.g. subspecies, varieties), we only kept the binomial species names. Then, each species was classified as native or non-native. For non-native species, we consulted their establishment status in Spain in (Sanz Elorza et al., 2004), which follows the Richardson et al. (2000) status definitions: established (including invasive, naturalized and casual, as this information is not very precise for introduced species in Spain) and not established in the wild. Socioeconomic and climatic dataset Climatic variables collected from WorldClim 2 (Fick and Hijmans, 2017) were calculated for each park with QGIS software (QGIS Development Team, 2009). Raster maps with 1 km resolution were obtained for 11 temperature variables and eight precipitation variables, as well as altitude (Table 2.1a). Finally, we obtained information of numerous socioeconomic variables for the total area of the 23 towns selected. Specifically, the Spanish Statistics Office (INE, 2017) provided information for mean values between 2010 and 2016 (Table 2.1b) of eleven variables associated with population demography, four with urbanization, and six with employment and economy. Plant traits dataset We used several sources of information to obtain species traits. The majority of plant traits were extracted from the TRY Database (Kattge et al., 2011). We performed a second search in several internet databases (Table 2.2). For each species, we obtained 21 traits (17 continuous and 4 binary) related to whole-plant characteristics, leaves, reproductive organs (flowers and dispersal units), plant flowering and fruiting phenology, and tolerances to several environmental stressors such as frost (minimum absolute temperature tolerated and number of frost days per year), shade, drought, waterlogging and fire, as well as the minimum and maximum hardiness values and its range (Table 2.2). Hardiness zones are cartographical standardizations of the average annual minimum winter temperatures divided into 10-degree F zones (USDA, 2012). Thus, the hardiness values of the plants, in this way, are defined as the ability to survive and grow in a specific range of hardiness zones. Spain includes hardiness zones 7a (-17.8ºC to -15ºC) to 11b (7.2ºC to 10ºC; Fick and Hijmans, 2017; USDA, 2012). This comprehensive search did not render trait information for all species. Therefore, we excluded for further analyses five traits with data missing for more than 50 % of the species (Table 2.2). For the remaining traits, missing data was imputed. Previous work has shown that imputing missing values is better than simply removing missing-data variables or missing-data species, as removing data could bias the results (Penone et al., 2014). We applied nonparametric missing value imputation using random forests implemented in the R package ‘missForest’ (Stekhoven and Buhlmann, 2012), which uses a random forest trained on the observed values to predict the missing values. This imputation method can handle multivariate data consisting of continuous and categorical variables simultaneously, as was the case in our study. Overall, after 56 seven iterations, the out-of-bag normalized root mean squared error was low (1.1608; PFC 0.0295), indicating a good imputation performance (Stekhoven and Buhlmann, 2012). Statistical analyses Overview Due to the high number of plant traits obtained and their high collinearity, we first built a database of the plant-trait profile of the parks, calculating the median value of each quantitative trait and the mean value of each binary trait (frequency) for each park. Then, we performed a correlation matrix. From from each pair of traits that showed a high correlation in absolute value (Pearson’s |r| > 0.70) we excluded the most correlated variable with the rest (Appendix 2.2). Similarly, due to the high number of climatic and socioeconomic variables and their high collinearity, we also excluded highly correlated variables following the same procedure. Once eliminated those highly correlated variables, we then performed three principal component analyses (PCA) to reduce the number of variables in each category (i.e. climatic variables, socioeconomic variables, and trait profile of the park) respectively, in order to avoid overfitting when performing further statistical tests. For the PCA, we used the ‘fviz_pca_biplot’ function of the R package ‘factoextra’ (Kassambara and Mundt, 2020). Therefore, after performing each PCA we used a broken stick criterion for each group of variables, using the ‘fviz_pca_contrib’ function of the same package, to differentiate how many significant and meaningful axes explain the variation observed across urban parks for each group of variables (Appendix 2.2). For the PCA of climatic variables, the first and second principal axes explained 39 % and 35 % of the variation, respectively (Fig. 2.1a); we selected five climatic variables (annual mean temperature, precipitation in driest quarter, minimum temperature in coldest month, and annual range of temperatures; Table 2.1a). Finally, for the PCA of socioeconomic variables, the first and second principal axes explained 37 % and 25 % of the variation, respectively (Fig. 2.1b). We selected five socioeconomic variables (mean home size, median age of inhabitants, percent of native population, percent of working population between 20 and 64 years old in active, and average incoming money per home; Table 2.1b). The first and second principal axes of the PCA involving traits of park profiles explained 28 % and 19 % of the variation, respectively (Fig. 2.1c). With this overall selection procedure of correlation and PCA analyses together with a broken stick criterion, we finally selected five tree traits (minimum, maximum and range of hardiness, tolerance to shade, and having conspicuous flowers; Table 2.2). PCA significantly reduced the number of variables, yet we perform complementary approaches to reduce even more the final set of variables. To do so, we performed Lasso regression models (Friedman et al., 2010; Tibshirani, 1996) with different performance for each question (see below). We used the R package ‘glmnet’ (Simon et al., 2011). Lasso regression is a method of variable selection that excludes non-informative explanatory variables for multiple linear regression models (Hesamian and Akbari, 2019). The ‘lasso’ works by minimizing the residuals, trying to adjust the sum of the absolute value of the coefficients to be less than a constant value, and tends to produce exact zero estimates for negligible variables (Tibshirani, 1996). It adds a penalty for non-zero coefficients, which is represented by the λ parameter, so a high value of λ forces many coefficients to be zero. The best λ value was obtained by performing a Type of trait Trait Code Kind of variable Unit % real data % imputated data First PCA selection Data sources beyond TRY (Kattge et al., 2011) Tolerances Hardiness min. hardiness_min quantitative 1-12 67 % 33 % * 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Hardiness max. hardiness_max quantitative 1-12 67 % 33 % * 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Hardiness range hardiness_range quantitative 1-12 Calculated directly: difference between max. and min. values * Calculated Data sources of Table 2.2 beyond TRY (Kattge et al., 2011) Num. Source reference 1 Brines R, Tejuelo I, Beltrán P and Balaguer Á. (1997) Guía Verde. https://www.guiaverde.com/. Accessed 1 Sep 2017 2 CSIC Arbolapp. (2014) In: Arbolapp. http://www.arbolapp.es/. Accessed 1 Sep 2017 3 Morales J. (2002) Infojardín. http://www.infojardin.com/. Accessed 1 Sep 2017 4 Sánchez de Lorenzo-Cáceres JM. (1999) Árboles Ornamentales. http://arbolesornamentales.es/. Accessed 1 Sep 2017 5 Universidad de Málaga. (2011) Jardín Botánico de la Universidad de Málaga. http://www.jardinbotanico.uma.es/jardinbotanico/index.php. Accessed 1 Sep 2017 6 Wyse-Jackson P. (2006) Missouri Botanical Garden. https://www.missouribotanicalgarden.org/. Accessed 1 Sep 2017 7 Botánica Y Jardines.(2014) In: Botánica Y Jardines. http://www.botanicayjardines.com/. Accessed 1 Sep 2017 8 Bärtels A. and Schmidt P. A. (2014)Enzyklopädie der Gartengehölze, 2nd edn. Ulmer E. Verlag 9 Plants For A Future (PFAF) (2010). In: Plants For A Future. https://pfaf.org/user/Default.aspx. Accessed 1 Jun 2017 10 Wheatley, R. (2009) B and T World Seeds. http://b-and-t-world-seeds.com/. Accessed 1 Jun 2017 11 USDA (USDA, 2008) Plants Database. https://plants.usda.gov/java/. Accessed 1 Jun 2017 12 Hintze C, Heydel F, Hoppe C, et al (2013) D3: The Dispersal and Diaspore Database – Baseline data and statistics on seed dispersal. Perspectives in Plant Ecology, Evolution and Systematics 15:180–192. 13 Centraal Bureau voor de Statistiek (2003) BioBase In: Centraal Bureau voor de Statistiek. https://www.cbs.nl/nl-nl/onzediensten/methoden/classificaties/overig/biobase-2003/biobase-2003. Accessed 1 Jun 2017 14 Czerepanov SK (1995) Vascular plants of Russia and adjacent states (the former USSR). Cambridge University Press, New York 15 Zanne AE, Tank DC, Cornwell WK, et al (2014) Three keys to the radiation of angiosperms into freezing environments. Nature 506:89–92. 16 Moles AT, Falster DS, Leishman MR, Westoby M (2004) Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. Journal of Ecology 92:384–396. 17 Gallagher RV, Leishman MR (2012) A global analysis of trait variation and evolution in climbing plants. Journal of Biogeography 39:1757–1771. 18 Panchen ZA, Primack RB, Nordt B, et al (2014) Leaf out times of temperate woody plants are related to phylogeny, deciduousness, growth habit and wood anatomy. New Phytol 203:1208–1219. Table 2.2. Traits used to describe woody plant species in 46 urban parks in 23 towns across Spain. A first compilation of plant traits was performed through a systematic search in the TRY Database (Kattge et al., 2011). A second search was performed to complete the database —see Data Sources. Then, those variables for which we had more than 50 % of the data were selected, and, on them, we made a nonparametric missing value imputation by random forest (Stekhoven and Buhlmann, 2012). ‘Var.Ex.’ indicates variables excluded from imputation because not enough data. Finally, we made a selection using a Pearson correlation analysis (Appendix 2.2, Fig. A2.3) and PCA (Fig. 2.1c) in which we applied a broken-stick criterion (Appendix 2.2, Fig. A2.6) as described in the Methods. *Variables marked with an asterisk were finally selected by the PCA and broken-stick method. 64 Trees Shrubs Palms Arboreal Cacti Total Native 59 26 1 0 86 Non-native not established 154 85 30 1 270 Non-native established 95 30 4 1 130 Total 308 141 35 2 486 Table 2.3. Number of Woody ornamental plant species by status and growth form in 46 urban parks in 23 towns of Spain Among socioeconomic variables (Appendix 2.2, Fig. A2.2), the highest correlation was between number of residents and number of homes (Pearson's r = 1). Average home sizes in the towns of the parks are directly related to the proportion of kids and adult inhabitants (Pearson’s r = 0.62 for kids under 14 years old, and r = 0.60 for adults between 15 and 64 years old), and inversely to proportion of people over 64 years old (r = -0.70). This proportion of people over 64 years old is directly related with the median age of the population (Pearson’s r = 0.97). In the socioeconomic PCA (Fig. 2.1b), home size and median age are both distributed along the first axis (36.7 %) and in opposite directions. Median age of the population is also distributed along the first axis, and in the same direction as the percentage of workers in the service sector. The percentage of employed people, as well as the average income per home, are both distributed along the second axis (25 %) and in the same direction. Regarding to the plant-trait profiles of the parks (Appendix 2.2, fig. A2.3), there is a high correlation between some of the variables. Some of the tightest correlations were between entomophilic pollination and several other traits such as the presence of conspicuous flowers (Pearson's r = 1), maximum height (Pearson's r = -0.82) and the tolerated minimum temperature (Pearson's r = 0.52). As expected, there was also a high correlation between cold tolerance and the minimum value of hardiness (Pearson's r = 0.79). Other correlations that we can highlight are those found between the type of leaf (conifera needle - broad leaf) and whether or not species are gymnosperm (Pearson's r = 0.98). Leaf size variables were all closely correlated with each other (Pearson's r = 0.76 for width vs area; 0.76 for area vs length; 0.73 for width vs length). Plant trait PCAs (Fig. 2.1c) show that the minimum hardiness and the presence of conspicuous flowers increases in largely the same direction, as both variables have great weight on the main axis (28 %) and in opposite direction of the hardiness range. The maximum hardiness is distributed in the same direction as leaf width, with the first variable having much more statistical weight on the main axis than the second one. Shade tolerance and drought tolerance appear in the same dimensions (diagonally to both axes) and in opposite directions. 65 Figure 2.2. Generalized linear models of plant traits versus min temperature in coldest month and annual range of temperature. The continuous trait variables (i.e. minimum hardiness, maximum hardiness, range of hardiness [scale 1–12], and shade tolerance [scale 1–5]) is shown on the main y axis (left side of the graph), while the scale of the unique binary trait variable (i.e. presence of conspicuous flower), converted to proportion of species having per park, is shown on the secondary y axis (right side). * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001, otherwise non significant. P-values in Table 2.4 ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************** 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**************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************** 2.5 5.0 7.5 10.0 0.25 0.50 0.75 1.00 −2.5 0.0 2.5 5.0 Min temperature in coldest month (º C) Plant trait (continuous variables) Plant trait (Conspicuous flowers) A 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C) Plant trait (continuous variables) Plant trait (Conspicuous flowers) Variables: Min. hardiness Max. hardiness Hardiness rg. Tolerance to shade Conspicuous flowers B 66 A) Minimum hardiness Estimate Std. Error t value Pr(>|t|) (Intercept) 1.5667 0.3541 4.4244 0.0001 *** Minimum temperature of the coldest month 0.0456 0.0071 6.4694 < 0.0001 *** Annual range of temperature 0.0191 0.0048 3.9906 0.0003 *** Mean home size -0.0271 0.0448 -0.6033 0.5497 Founding year of the park -0.0002 0.0002 -1.1550 0.2549 . Area of the park -1.9019E-07 1.2108E-07 -1.5708 0.1241 B) Maximum hardiness Estimate Std. Error t value Pr(>|t|) (Intercept) 2.1459 0.1665 12.8890 < 0.0001 *** Minimum temperature of the coldest month 0.0180 0.0033 5.4110 < 0.0001 *** Annual range of temperature 0.0082 0.0023 3.6049 < 0.0001 *** Mean home size -0.0216 0.0209 -1.0313 0.3086 Founding year of the park -0.0001 0.0001 -0.9207 0.3628 Area of the park -1.8047E-08 5.3817E-08 -0.3353 0.7391 C) Range of hardiness Estimate Std. Error t value Pr(>|t|) (Intercept) 1.2575 0.4182 3.0068 0.0045 ** Minimum temperature of the coldest month -0.0288 0.0084 -3.4244 0.0014 ** Annual range of temperature -0.0080 0.0057 -1.3926 0.1714 Mean home size -0.0304 0.0513 -0.5929 0.5566 Founding year of the park 0.0001 0.0002 0.6964 0.4902 Area of the park 6.0555E-08 1.2795E-07 0.4733 0.6386 D) Tolerance to shade Estimate Std. Error t value Pr(>|t|) (Intercept) 2.0327 0.5283 3.8473 0.0004 *** Minimum temperature of the coldest month -0.0376 0.0107 -3.5268 0.0011 ** Annual range of temperature -0.0320 0.0072 -4.4644 0.0001 *** Mean home size 0.0829 0.0687 1.2063 0.2348 Founding year of the park -0.0003 0.0002 -1.3355 0.1893 Area of the park -1.2227E-07 1.6867E-07 -0.7249 0.4727 E) Presence of conspicuous flowers Estimate Std. Error t value Pr(>|t|) (Intercept) -3.3205 1.1389 -2.9156 0.0058 ** Minimum temperature of the coldest month 0.0815 0.0220 3.7109 0.0006 *** Annual range of temperature 0.0273 0.0146 1.8666 0.0693 . Mean home size 0.1330 0.1388 0.9586 0.3435 Founding year of the park 0.0008 0.0005 1.5061 0.1399 Area of the park -3.0225E-07 3.8541E-07 -0.7842 0.4375 Table 2.4. General Linearized Models of the five plant traits selected as dependent variables, versus climatic and socioeconomic variables preselected by lasso regression (Appendix 2.3, Table 2.3.1) as independent variables. * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001, otherwise non significant. Graphical representation in Figure 2.2. Relationships between plant-trait profiles and climatic and socioeconomic characteristics From the various predictors selected after performing techniques to reduce model dimensionality, there are two variables, both climatic, that are retained: the minimum temperature in the coldest month, and the range of temperatures. The remaining predictors, home size, founding year of the park and park area, did not explain any of the traits selected as dependent variables (Table 2.4). Minimum and maximum plant hardiness as well as the presence of conspicuous flowers were positively related to minimum temperature in the coldest month. On the other hand, hardiness range and shade tolerance were negatively related to minimum temperature in the coldest month (Fig. 2.2a). Similarly, there is a significant positive relationship between the annual range of temperatures and the minimum and maximum hardiness (indicating plants less tolerant to cold), while its relationship with tolerance to shade was negative, showing in this 67 case, the same trade-off between variables. However, there is no significant relationship for the annual range of temperatures with the range of hardiness, nor with the presence of visual and conspicuous flowers (Fig. 2.2b). Figure 2.3. Number of species per park by status. a) number of native and non-native species per park; b) number of not established and established non-native species per park. 68 Figure 2.4. Relationship between climatic and socioeconomic variables and proportion of non-native species (red) and established non-native species (blue). * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001, otherwise non significant. P-values in Table 2.5. Influence of plant-trait profiles, climatic and socioeconomic characteristics on the proportion of non-native species In general, in the parks studied, there is a greater presence of non-native (39.2 ± 4.15; mean ± se) than of native species (14.2 ± 1.27; paired t-test; t = 7.475; pvalue < 0.01; Fig. 2.3a). Among the non-native species in the parks, the number of species that are not known to have established in the wild (21.15 ± 2.06) exceeds the number of established species (18.02 ± 2.27; paired t-test; t = 2.4393; p-value = 0.0187; Fig. 2.3b). The proportion of non-native species and the proportion of established species are both significantly varying across parks (Chi-squared = 132.98, and 148.17, respectively; p < 0.01 in both cases). From the various predictors selected after the reduction of model dimensionality, only one climatic variable was retained (annual range of temperature), two socioeconomic variables (mean of home size, and median age of inhabitants) and one plant trait of the park profile (range of hardiness). The remaining predictors, the percent of active population, maximum hardiness, founding year of the park, and park area, did not explain any of the proportions selected as dependent variables in lasso regressions. The GLM analysis (Table 2.5) revealed a significant negative relationship between annual range of temperature and the proportion of established species, while its association with the proportion of non-native species was not significant (Fig. 2.4a). ******************************************************************************************** 0.00 0.25 0.50 0.75 1.00 20 25 30 Annual range of temperature (º C) Proportion of species A ************************************************************************************************************************************************************************************************************************************************************************************ 0.00 0.25 0.50 0.75 1.00 2.0 2.4 2.8 3.2 Mean of home size (num.inhabitants) B **************************************************************************************************************************************************************************************** 0.00 0.25 0.50 0.75 1.00 35.0 37.5 40.0 42.5 45.0 47.5 Median age of inhabitants (years) Proportion of species C ************************************************************************************************************************************************************************************************************************************************************************************ ............................................................................................ 0.00 0.25 0.50 0.75 1.00 3.00 3.25 3.50 3.75 4.00 Range of hardiness (USDA) Proportions: Non−Native / Total spp Established / Total Non−Native D 69 With regard to socioeconomic variables, mean of home size and median age were both positively associated with the proportion of non-native species, while none of them was related to the proportion of established species (Fig. 2.4b, c). Furthermore, the range of hardiness of the park profile was negatively related to the proportion of non-native species, whereas it was not related to the proportion of established species (Fig. 2.4d). A) Proportion of non-native species among all species Estimate Std. Error z value Pr(>|z|) (Intercept) -4.5147 3.2670 -1.3819 0.1670 Annual range of temperature 0.0196 0.0167 1.1726 0.2409 Mean home size 0.7053 0.1853 3.8066 0.0001 *** Median age 0.1095 0.0370 2.9615 0.0031 ** Working population in active age 0.0178 0.0102 1.7501 0.0801 · USDA hardiness max. 0.1739 0.1114 1.5613 0.1185 USDA hardiness range -0.7591 0.1194 -6.3557 < 0.001 *** Founding year of the park -0.0010 0.0005 -1.8860 0.0593 · Area of the park -5.6369E-07 3.7766E-07 -1.4926 0.1355 B) Proportion of established species among non-native species Estimate Std. Error z value Pr(>|z|) (Intercept) -0.1086 3.4393 -0.0316 0.9748 Annual range of temperature -0.0406 0.0173 -2.3522 0.0187 * Mean home size 0.3793 0.2313 1.6400 0.1010 Median age 0.0149 0.0403 0.3694 0.7118 Working population in active age 0.0067 0.0107 0.6246 0.5322 USDA hardiness max. -0.0293 0.1126 -0.2598 0.7950 USDA hardiness range -0.2301 0.1363 -1.6887 0.0913 · Founding year of the park -3.5122E-05 0.0005 -0.0713 0.9431 Area of the park -4.3274E-07 4.2891E-07 -1.0089 0.3130 Table 2.5. General Linearized Models on the proportion of non-native species and on the proportion of established species as dependent variables, versus climatic and socioeconomic variables and plant traits preselected by lasso regression (Appendix 2.3, Table A2.3.2) as independent variables. * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001, otherwise non significant. Graphical representation in Figure 2.4. Discussion We currently have a poor understanding of the combination of plant traits, climate variables and socioeconomic factors guiding the planting on non-native woody plant species in urban parks. Obtaining this knowledge is essential to better understand how different drivers are related to the success of non-native species from introduction to invasion. Here, we present insights from a large representation of the main urban parks in Spain showing that they harbor far more non-native than native species. Interestingly, this high proportion of non-native tree species (82.3 %) is not associated with a particular set of taxa. Rather, Spanish parks present a high species turnover, meaning that the vast majority of woody plant species appear in one or very few parks, while very few species are present in half of the parks or more. The average percentage of non-native tree species in Spanish urban parks is much higher than in Central European urban floras (Pyšek, 1998), and many other regions (Dangulla et al., 2019; de Freitas et al., 2019; Jha et al., 2019; Templeton et al., 2019), where the average representation of non-native species is between 40 % and 50 %. In line with this finding, the percentage of non-native species available in Spanish nurseries is 76 % approximately (Bayón and Vilà, 2019b)2/23/21 11:40:00 AM. The small difference between the percentage in parks and nurseries is probably not significant, and can be due to the fact that the study 70 of Spanish nurseries includes all types of growth forms, while this study in parks includes only woody species, and many plants in parks are probably no longer sold since decades. We have shown that the trait composition of urban parks can be explained by a combination of socioeconomic and climatic variables. We observed variation in the proportion of species with conspicuous flowers (which is a direct proxy of the pollination syndrome) across urban parks, and this variation was explained by minimum temperature in the coldest month. Species with entomophilous pollination tend to be poorly tolerant to frost, probably due to the dependence of their pollinating insects on milder temperatures. Meanwhile, wind-pollinated plants can show a wider range of tolerance to frost. For instance, oak species are known to be frost tolerant, whereas other species like palms do not show the same adaptations. The latter are therefore predominantly found in oceanic climates where frost is a rare phenomenon. The pollination syndrome may change in urban floras when comparing native and non-native species. For instance, Aronson et al. (2007) documented that conspicuous flowers, that tends to be beautiful, differ in their pollination requirements, however, this is not the case in our study. Differences between results might be related to the fact that Aronson et al. (2007) study was conducted under similar environmental conditions, while our study includes a wide range of climates from continental to Mediterranean, to oceanic. Besides cold acting as a filter for reproduction syndromes, we found that the tolerance to shade increased with higher minimum temperature in the coldest month and with wider thermal range. In regions with an oceanic climate, such as the northern coast, winters are milder and cloudiness is more frequent and lasts longer, especially in the summer months, than in the rest of Spain. However, in regions where the climate is more continental, as in the plateaus, winters are colder and summers are not only hotter but also very sunny (AEMET, 2019). These differences are likely forcing plant species selected to be planted in these regions to be more tolerant to insolation, reducing leaf area to avoid water loss, while plants with larger leaves tend to be distributed in areas with higher water availability and lower irradiance (Holmgren et al., 2012; Markesteijn and Poorter, 2009). Finally, the minimum and maximum hardiness values of the plants are directly related to both minimum temperature in the coldest month and higher annual ranges of temperature. This is indeed an obvious relationship, as Hardiness is a proxy of the tolerance to low temperatures (USDA, 2012), and it represents a primary factor in selecting which species are planted. Specifically, those species with high tolerance to cold are primarily selected in cold places and although they could also be planted in warmer places, gardeners tend to prefer other more attractive species, which may be less tolerant. This explanation is supported by the fact that the range of hardiness decreases with the minimum temperature in the coldest month. These results suggest there is a trade-off between species that tolerate shade and those that have conspicuous flowers, as well as with those that have higher hardiness values (bearing in mind that higher hardiness values mean less tolerance to cold). With respect to our second question, we were expecting that certain climatic and socioeconomic variables, as well as certain plant traits, explain differences across parks between the proportion of non-established and established non-native species. Spain is characterized by a low annual range of temperatures along the coast, while continental areas have strong temperature range variations. Larger differences between temperatures in summer and winter can impede the establishment of species with low tolerance to continentality (Lockwood et al., 2009). On the other hand, because Spain is climatically highly heterogeneous, the establishment of species is not always the same 71 throughout the territory, and established species in some regions are not the same as in others. Moreover, we should not forget that temperature in urban parks might be altered by the presence of buildings and human activity (e.g. urban heat island effect), and that drought may be reduced by artificial irrigation. These factors might explain why in our GLM analysis we found that the annual range of temperature is neither hindering nor favoring the presence of non-native species, whereas greater continentality is hindering the establishment of non-native species. Indeed, coastal areas, which in Spain are closely associated with a lower range of temperatures, host more established plant species than inland areas. Comparing the trait profiles of the parks in our study with those of previous studies, we can say that some urban floras present more drought tolerance, unspecialized nutrient requirements, and taller plants with bigger seeds than wild floras (Palma et al., 2017; Williams et al., 2015). Other studies have shown differences in pollination syndrome between non-native and native species in woody park species (Aronson et al., 2007). However, in Spain, none of these traits is relevant in explaining differences in the proportion of non-native species, indicating that they are equally selected in planting native and non-native species. We only found an inverse relationship between the range of plant hardiness profiles (the median range of minimum temperatures at which the plants of the park are tolerant) and the proportion of non-native species (Fig. 2.4d). This tendency is somehow related to the effect of the annual range of temperatures described above. Since more non-native plants appear in regions with low continentality, the requirements of these plants are not linked to a broad thermal range inland. That is, plants with a wide range of hardiness are not required in these areas. Interestingly, our GLM results showed that the proportion of non-native species increases as towns are characterized with older human populations and bigger homes. In contrast, we found no trend of the establishment of the non-native plants in the wild with any of the socioeconomic variables evaluated. Home sizes are directly correlated to the proportion of kids and adult inhabitants in the town and inversely to the proportion of people over 64 years old. This is coincident with family groups. Social interest in ecology and environment is in general greater in younger than in older people in Spain (metadata from FECYT, 2018); more specifically, median age is directly related with the number of inhabitants older than 64 years old, which are the group of people least interested in the environment and ecology (27.6 % ‘not interested’, compared to 19.9 % in average of population; FECYT, 2018). Although younger people are environmentally more aware than older people (FECYT, 2018), we found that towns with more inhabitants per house have a higher proportion of non-native species. This may be because (i) a limited awareness of the fact that a majority of woody species seen around in urban Parks are not native, (ii) when people know this fact, they most often do not see it as an environmental or ecological issue, and (iii) in most cases they do not have a direct say in the decisions as to what species should be planted. Indeed, for kids and adults (≤ 64 years old), from all the list of topics analized in FECYT (2018) study, the environment and ecology is more frequently selected as ‘third option’ (5.3 %) than ‘second option’ (4.9 %), and more than as ‘first option’ (2.7 %; FECYT, 2018). A limitation of our study is that the year in which the species were introduced in each park is not available. The year the park was opened is an available data, but there is no evidence that all the species were planted since its inauguration, or how many have been planted afterwards. Many woody species that we can find today in our parks may have been planted decades or even centuries ago; if that is the general case, then the relationship between the proportion of non-natives and human demographic data 72 would have to be explained differently. However, as the relationships are nevertheless present, this invites us to suggest that implementing family programs in favor of social awareness on biological invasions can generate the social pressure necessary to change these trends. It is known that the presence of non-native species decreases the cultural value of several ecosystem services, including those of urban parks (Vaz et al., 2018), causing them to be less aesthetic. This kind of awareness raising action can therefore drive to the urban parks to increase its cultural value, in agreement with the notion of ‘more naturalistic urban landscapes’ (Muratet et al., 2015). In sum, it is well known that ornamental plants in urban environments is one of the major ways of introducing non-native species (Gaertner et al., 2017b). Here we provide insights about how climatic and socioeconomic variables determine the proportion of non-native tree species in urban parks and their plant trait composition; this information coming from three contrasting sources: climatic variables, socioeconomic variables and plant-trait profiles, help to explain the differences between parks in their proportion of non-native species that are established in the wild. These results provide new ways of understanding and preventing future establishments of non-native plant species. 79 Abstract Urban parks and gardens are one of the most important pathways for the deliberate introduction of non-native plant species, some of which have potential to become invasive and cause environmental and socioeconomic impacts. To inform management and policy, risk assessment protocols offer semi-quantitative methods to identify nonnative species prone to invade and cause impact. We conducted a horizon scanning on 388 non-native woody plant species from 46 urban parks of Spain to classify them in lists of species based on their invasion status in the country, their capacity for being invasive elsewhere with a similar climate than in Spain and their potential to cause impacts. Only Ficus benjamina has no potential to invade (Green list). Four species are invasive and regulated (Priority List); 47 invasive or potential invasive species can cause many impacts (≥ 4 environmental or ≥ 1 socioeconomic impact; Attention list), and 12 can cause few impacts (< 4 environmental or no socioeconomic impacts; Watch list). There is no park without any invasive or potentially invasive species. In addition to this information, our assessment shows that the most common potential impacts of invasive and potentially invasive species are competition with native species (79.7 % species) and the physical modification of the habitats (71.2 %). We identified 31 species with potential to cause human health impacts with a high occurrence across parks. On average, each of these species with potential to cause human health impacts are present in 7.29 ±1.53 parks (mean ± SE). The most frequently planted invasive and potentially invasive woody species were also the ones with the largest standard trending value from Google Trends; but their occurrence was not related to their weed risk assessment score Furthermore, our analysis indicates that there are significant positive correlations between mechanisms of impact. The most correlated impacts are among competition, toxicity, alteration of natural succession and habitat structure. Toxic and allelopathic plants are also to the ones that can cause damages on human health and agriculture. Overall, impacts on human health, competition and physical modification of the habitat are the most prevalent hazards that non-native woody species present in urban parks in Spain could cause if they invade natural areas. Keywords: climate matching, Google trends, horizon-scanning, impact assessment, invasive species, non-native trees, ornamental plants, priority list, risk assessment 81 Introduction Biological invasions by the establishment and spread of introduced non-native species has many negative environmental and socioeconomic consequences. Because the establishment of non-native species has increased exponentially during the last decades due to the amplified connection of infrastructures and global commerce (Seebens et al., 2017), it is of paramount importance to identify those with the potential to become invasive and cause harm. The introduction of non-native species may be accidental or deliberate. Deliberated introductions of ornamental plants in urban parks and gardens is one of the most important introduction pathways of non-native plants around the globe (Haeuser et al., 2018; Mayer et al., 2017). Despite that ornamental plants provide cultural and aesthetic services to people (Bolund and Hunhammar, 1999; Kendal et al., 2012; Vaz et al., 2018), many ornamental introduced species have simultaneously established (i.e. naturalized) in the wild, causing impacts to biodiversity and ecosystems (Hulme, 2007). The most effective way to manage the impacts of non-native species is prevention through early warning techniques, as well as to perform invasion risk assessments (Convention on Biological Diversity, 2010). Development of risk assessment protocols has progressed considerably in the last decades because they have been proved as an essential tool to identify and predict potential invasions at the country level (Andreu and Vilà, 2010; Roy et al., 2014). Some classic examples such as the Australian weed risk assessment (WRA) (Pheloung et al., 1999) have widely used information of species traits, their biogeography and their potential harm to accurately predict potential invaders in many different regions including Spain (Andreu and Vilà, 2010; Gassó et al., 2010; Gordon et al., 2008). However, when focusing on the specific case of non-native plant species introduced for aesthetic purposes such as ornamental trees in urban parks and gardens, it is likely that additional factors such as the socioeconomic interest of introduced species or their planting prevalence are relevant to consider in a risk assessment. The Google Trend tool offer good proxies of the societal interest that the people have on particular items. The Google Trend tool has been used to evaluate the level of interest and queries of the population on conservation topics (Davies et al., 2018; Schuetz and Johnston, 2019; Żmihorski et al., 2013) and epidemiology (Seo and Shin, 2017). Recently, it has been applied to the study of biological invasions. For example, in Japan, the Google search volume of 31 major invasive species is a good predictor of their spatial distribution and the number of newspaper articles featuring the species (Fukano and Soga, 2019). Likewise the Google Trends has been previously used in Spain to rank non-native plants sold in nurseries (Bayón and Vilà, 2019b). A useful metric we can derive from this tool is a standard trend value (STV) ranging from 0 to 100, which indicates the highest value of each species relative to the optimal value of a preselected control species, setup at 100 STV. Because we interpret that STV values are positively correlated to their socioeconomic interest, we expect to find that non-native woody species showing higher STV (close to 100) are more widely planted in urban parks than non-native woody species with low STV. Most of the times, human resources for the prevention of biological invasions are scarce, and the list of non-native species to be risk assessed is very large as the case mentioned above on ornamental plants. Therefore, in these circumstances, the risk analysis to be performed need to be user-friendly, efficient and transparent without losing 82 the scientific based evidence (Vilà et al., 2019). Horizon-scanning risk analysis based on the following criteria has proven to be effective: being invasive elsewhere, having the potential to stablish in the wild, climate matching with the area of origin, and potential to cause impacts (Otfinowski et al., 2007; Roy et al., 2019; Weber and Gut, 2004). Impacts of non-native species are of major concern to prioritize management of non-native species (Kumschick et al., 2015; Novoa et al., 2020). Many species cause socioeconomic impacts that are preceded by impacts on native species and on ecosystem functioning (Vilà et al., 2010). Therefore, an understanding on how the impacts interact with each other may provide knowledge on the association between impacts on the environment and on socioeconomic activities. A clear example could be the case of toxicity. Plant toxicity can be related to competition because direct influences the performance of native species, and indirectly by the availability of nutrients (Inderjit and del Moral, 1997). The alteration of nutrient cycles, for its part, may influence to the structure of the habitats (Schoener, 1974). In addition, the toxicity may be also related to impacts on human health, because the risk of direct consumption, and with the agriculture, because the inhibitory effect of by allelopathy (Qasem and Foy, 2001). Moreover, plant species assemblages made up by different stocks of non-native species may have different impact profiles. That is, in a particular plant community, some impacts are most common, other being less or not representative at all (Zengeya et al., 2020). Similarly, because plants are deliberately introduced into urban parks, it is important to identify what are the most common and frequent impacts they can cause, both at the species level and at the park (“community”) level. Therefore, an analysis of the proportion of species with the potential to cause each impact type and the species occurrence in the parks allows for defining the potential non-native impact profile of a particular park. To our knowledge, this analysis has not been done before despite that it is of paramount importance to guide best practices for avoiding urban parks and gardens disservices. In this paper, based on the Priority Index protocol developed by (Bayón and Vilà, 2019b) that account for WRA scores, STV values and potential to cause harm, we performed a horizon-scanning to classify 486 non-native woody species planted in 46 major urban parks from 26 cities across Spain in various lists of priorization. Furthermore, for all the invasive and potentially invasive species included in the Attention and Watch lists (see Methods), we answered the following questions: 1) How frequent are each of these species across parks? 2) Is species occurrence (i.e. number of parks where present) associated with their interest to society, and with the magnitude of their risk to invade? 3) Which are the most common potential impacts of these species? 4) How frequent species with these potential impacts are found in parks? and finally, 5) Which impacts show higher correlations and how? The occurrence of non-native species across parks can be a proxy of their propagule pressure, which is a direct driver to establishment success (Blackburn et al., 2013) and because they are perceived to provide cultural ecosystem services, such as aesthetics and cultural heritage (Castro-Díez et al., 2019). Therefore, we expect risk invasion scores and societal interest values to be associated with the occurrence of the species across parks. In addition, we expect to find high correlations among impacts even among impacts on native species, on ecosystems and on socioeconomics. 83 Material and methods Urban parks dataset The complete species list of woody and arboreal-like plant (i.e. trees, shrubs, arboreal cacti and palm trees) present in 46 parks from 23 peninsular Spanish cities was obtained from the database provided by the ‘Spanish Association of Public Parks and Gardens’(AEPJP, 2010) (www.vivirlosparques.es/; last accessed 15 May 2017; Appendix 1). The cultivation of herbaceous plants in urban parks and gardens is highly variable each year, so they have been excluded from this research. We used the Taxonstand package of R (Cayuela et al., 2019) to select all the accepted scientific names (consulted 22 May 2020). Plant listed at the genus level, subspecies, hybrids and other non-specific taxa, as well as species with invalid names were excluded from analyses. The species were classified as native or non-native. Archaeophytes (i.e. species introduced before 1500 A.D.) were not included in the analysis because they are poorly recorded and, for many species, their non-native status is under discussion (Pyšek et al., 2004). We check for the invasion status of non-native species in Spain (BOE, 2019; CABI, 2020; Sanz Elorza et al., 2004) following Richardson et al. (2000) classification as invasive, established, casual, and not cited as present in the wild. We also consulted their regulatory status by the Spanish Catalogue of Non-native Invasive Species (BOE, 2019) and the List of Invasive Alien Species of Union Concern (The European Commission, 2019). These regulations involve the ban of possession, transport and commerce of living beings and their propagules. Once we had the five groups of species according to its invasion status in Spain, we proceeded to classify the species into 6 lists following Bayón & Vilà (2019b) methodology: • Priority list: regulated (by Spain or the EU) invasive non-native species still present in the Spanish urban parks. • Attention list: invasive (not regulated) and potentially invasive species (climatic suitable and invasive elsewhere) with many potential impacts. • Watch list: invasive (not regulated) and potentially invasive species with none or few potential impacts cited. • Green list: non-invasive nor potential invasive species with no climatic suitability and probably no potential to be invasive in Spain, pretended to be safe for socioeconomics and environment. • Uncertainty list: non-invasive species with probably no potential to be invasive and that do not meet the requirements to be included in the Green List. It also includes species with known invasion status but with insufficient information on climatic suitability or invasiveness elsewhere. • Data Deficient list: species without enough information about its invasion status, nor climatic suitability, nor invasive elsewhere. Therefore, they cannot be classified in any other list. Briefly, information on being invasive elsewhere was consulted in CABI datasheets (2020) and the Global Invasive Species Database (2015). Furthermore, the climatic suitability was defined as the tolerance to both, highest historical absolute minimum temperature in Spain, and highest mean minimum temperature in the coldest 84 month in Spain (AEMET, 2019). The tolerances were consulted in the CABI datasheets (2020). More detail can be found in Methods from Chapter 1. Impact assessment of invasive and potential invasive species For the invasive and potentially invasive species, we assigned binary scores (yes/no) to six potential impact types on native species, following!Blackburn et al (2014): (1) competition, (2) hybridization, (3) disease transmission, (4) parasitism, (5) poisoning, toxicity and allelopathy, and (6) interaction with other invasive non-native species, —biofouling was excluded because the nature of the woody plant species selected—; and to four impacts on ecosystems: (7) nutrient cycling, (8) physical modification of the habitat, (9) modification of natural succession and (10) disruption to food webs. We also assessed potential socioeconomic impacts on (11) human health (such as allergenic pollen), (12) infrastructures, (13) agriculture and forestry and (14) to other sectors (e.g. livestock, domestic animals, and fish farming). The vast majority of the data concerning impacts was retrieved from the!CABI Invasive Species Compendium (Blackburn et al., 2014). For cases in which the CABI compendium did not provide enough information, the Global Invasive Species Database (2015) was consulted. Information on pollen allergenicity, was consulted in the Allergome database (Mari et al. 2009). Therefore, for each species we aim for 14 impact types: ten environmental, and four socioeconomic. Within each group of invasion status species, we calculated the number of potential impacts on environment (on species and ecosystems; 0–10) and socioeconomics (0–4). The threshold for the classification between species with a high and low number of impacts was the median value for both environmental and socioeconomic impacts. Invasive and potentially invasive species in Spain with environmental or socioeconomic impacts at or above the threshold were included in the Attention List, while species with both environmental and socioeconomic impacts below the threshold formed the Watch List. Society interest analysis of invasive and potential invasive species The Google Trends tool provides monthly data of up to five keywords, in a predefined geographic and temporal range. The output values of Google Trends are always relative to the maximum absolute value of all the keywords imputed, which is set as 100. To standardize the relative values of Google trends, we used the R pack "gtrendsR" v. 1.4.2 (Massicotte and Eddelbuettel, 2020), carrying out a paired systematic consultation between each species and a control species for Spain as the geographic region, from the 1st of January of 2004 to the 31st of December of 2019. In order to standardize in relative terms values of the Google Trends queries and make them comparable, we selected the species with the highest Google Trend value (Robinia!pseudoacacia) according previous approaches (Bayón & Vilà 2019). Therefore, for each invasive and potential invasive species of the Attention and the Watch lists we obtained the standard trend value (STV) as the highest value of the monthly trend in the complete temporal range of each species, relative to the optimal value of 100 of the control species (R.!pseudoacacia). However, the new group of species consider here can result in at least one with a higher trending value than our species control. Rather to change the species control, we would prefer to be consistent with previous approaches in order to make comparable studies, Therefore, in the case we find a species with a higher trending value, relative values will not change (range between 0 and 100) 85 but R.pseudoacacia) will show a trending value < 100 instead of being considered as the species with the highest relative trending value. In order to be consistent with previous works, in these particular cases, the final standard trending value (STV) was calculated as: 456$=56$×100 J56$A Where while 56$ is the calculated trending value for the species I (usually, 56$<100;A in that particular cases, 56$ =100); J56$ is the trending value of control species compared with species i, (usually, J56$=100; in that particular cases, J56$< 100 ); and 456$A is the final standard trending value for the species a (usually, 456$= 56$<100 ; in that cases, 456$>100 ), round to the nearest whole number. Invasion risk assessment of invasive and potential invasive species We used an adaptation of the invasion risk assessment (WRA) protocol for Spain (Gassó et al., 2010; Pheloung et al., 1999) on all the invasive and potential invasive species. The WRA scores range from -14 (benign species) to 29 (maximum risk). WRA assess species as rejected for species likely to be of high risk (score > 6); accepted, species with a low score (< 1); and species that need further evaluation, those with intermediate scores (1–6). Some of the WRA values were obtained from several previous studies (Álvarez et al., 2016; Bayón and Vilà, 2019b; Gassó et al., 2010; Pino et al., 2015). For the species for which WRA had not been performed before, it was carried out 'de novo'. Most of the scientific information to calculate the WRA was from the main invasive species databases (CABI, 2020; Invasive Species Specialist Group, 2015) Priority Index of invasive and potential invasive species For each species in the Attention list, we calculated a Priority Index (PIi) based on Bayón and Vilà (2019) according to the following equation: "#$= & 100×F$ 11 +100×4$ 4+100×NOC$ 29 +456$ 7 4 Where: PIi = Priority Index for species i; Ei = number of environmental impacts (on species + on ecosystems) for species i; Si = number of socioeconomic impacts for species i; WRAi = Weed Risk Assessment score for species i; STVi = Standard Trend Value for species i. The potential impacts on environment in our paper are 10 (biofouling was deleted, as it was not applicable to woody species). However, for the Priority Index to be compatible with the obtained in previous works (Bayón and Vilà, 2019b), we maintained this value over 11, and not over 10. Statistical analyses To test if invasive and potentially invasive species occurrence (i.e. number of parks present) is related to the STV and the WRA, we calculated the Pearson’s correlation coefficient, and performed dispersion dot plots by the R ggplot2 package (Wickham, 2016); smooth line has been performed by ‘lm’ method. For the non-native species included in the categories of invasive or potential invasive species, we seek to describe the occurrence of each potential impact, as well as the associations among impacts and their relative statistical weight. To answer how frequently species with potential impacts are found in parks, we related how many species 86 are causing each type of impact with their occurrence (mean ± SE) across urban parks. These pair of values were represented in a scatter plot, including the average values for each variable as reference dash lines. Data Resources The complete species database!underpinning the analysis, with their impacts, reported in this paper, are deposited in the Zenodo repository at https://doi.org/10.5281/zenodo.4295845 (Bayón et al., 2020b). Results Non-native species lists From the 486 plants of the database, eight were not at the species level, or had not valid names. Of the remaining, there were 84 native species, 6 archaeophytes and 388 non-native species (Table 1). From the non-native species 16 were invasive; three are regulated by the Spanish catalogue (BOE, 2019): Acacia dealbata, present in four parks (8.7 %); Buddleja davidii, present in two parks (4.3 %); and Opuntia ficus-indica, present in two parks (4.3 %); Ailanthus altissima, present in 20 parks (43.5 %), is regulated by both, the Spanish catalogue and the European list (BOE, 2019; European Commission, 2019). These four species are thus conforming the Priority List. N Climatic suitable Not Climatic suitable Low confidence in likelihood on climatic suitability Not enough information about climatic suitability Invasive elsewhere Not invasive elsewhere Total taxa in nurseries 486 Taxa excluded 8 Total species listed 474 Native 84 Archeophyte 6 Non-native (non-archeophyte) 388 Invasive - Regulated 4 Invasive - Not Regulated 12 Naturalized 39 13 26 Casual 72 21 0 1 50 Casual - Climatic Suitable 18 3 Casual - Not Climatic Suitable 1 0 Not in the wild 33 21 1 11 Not in the wild - Climatic Suitable 19 15 Not in the wild - Not Climatic Suitable 4 6 Data defficient 228 Table 3.1. Classification of ornamental Woody plant taxa in urban parks and gardens in Spain according to their invasion status and climatic suitability. Colour codes indicate the List where the species were classified: red: Priority List (see Results); orange: Attention or Watch list according to the Impact Risk Analysis (Table 3.2 and 3.3), green: Green List (see Results); grey: Uncertainty List (Appendix 3.3); blue: Data Deficient List (Appendix 3.4) 87 Only one species, Ficus benjamina, was classified in the Green list. From the rest of species, 39 were established, 72 casual and 33 not in the wild (Table 3.1). However, we found information of potential impacts for only 59 invasive and potential invasive species (15.21 % of total). Sixteen potential invasive species were not in the wild, 18 were casual and 13 were established, while 12 were non-regulated invasive species (Table 3.1). The median value for environmental impacts was four and the median for socioeconomic impacts was one. Forty-seven species (12.11 % of total non-native and 79.66 % of the invasive and potential invasive species) had potential impacts at or above these threshold number of potential impacts, conforming the Attention list (Table 3.2) and 12 species (3.09 % of total non-native and 20.34 % of the invasive and potential invasive species) did not reach the threshold of impacts, and thus conformed the Watch list (Table 3.3). Finally, the Uncertainty list (Appendix 3.2) has 96 species (24.74 % of total nonnative species). Unfortunately, we could not find information on the 228 remaining species (58.76 %), and thus, they were classified in the Data Deficient list (Appendix 3.3). Invasive and potential invasive species occurrence On average, parks have 0.57 ± 0.11 (mean ± SE) invasive regulated species from the Priority List; more concretely, two parks have three regulated species, one park has two regulated species, and 18 parks (39 %) have one regulated species each one. There are 25 parks (54 %) without any regulated species. In addition, parks have 8.65 ± 0.97 invasive or potential invasive species (i.e. species listed in the Attention and Watch Lists) from which 3.15 ± 0.39 are casual, 1.98 ± 0.24 are established, and 2.09 ± 0.27 non-regulated invasive in Spain. A very relevant result in this regard is that there is no park without any invasive or potentially invasive species. There is a significant positive correlation between the occurrence of these 59 invasive and potential invasive species with the Standard Trending Value (Pearson’s R = 0.52; p-value < 0.001; fig. 3.1A). However, the correlation between species occurrence and their WRA score was not significant (Pearson’s R = -0.23; p-value = 0.085; fig. 3.1B). Potential impacts of invasive and potential invasive species Almost the 80 % of the 59 invasive and potential invasive species have the potential to compete with native species (Fig. 3.2). At the ecosystem level, the most relevant potential impact is the physical modification of the habitat (71 % species), followed by impacts on natural succession (56 %). The most common potential socioeconomic potential impact is on human health, with 53 % species. The least represented potential impacts are disruption of the food webs (15 %), impacts on infrastructures (15 %), parasitism (10 %) and hybridization (10 %). On average, impacts are caused by 19.21 ± 3.59 species, and species with potential to cause impact are present in 6.02 ± 1.59 parks. By relating the number of species causing each impact type with their occurrence, according to their average values, we found the following patterns (Fig. 3.3). First, there are three impacts (i.e. human health, physical modification of habitats and competition) that are caused by more species than average. The most frequent impact is on human health, with species causing this impact present in 7.29 ± 1.26 parks. Second, there is one impact (i.e. natural succession) caused by more species than average (33), but they are present in less parks than average 88 (5.58 ± 0.94). Third, there are five impacts (i.e. disease transmission, interaction with other invasive species, disruption of food webs, on nutrient cycling and on agriculture) that are also caused by less species than average, but they are present in more parks than average. Finally, five impacts (i.e. hybridization, parasitism, poisoning, toxicity and allelopathy, impacts on infrastructures and other socioeconomic impacts) are caused by less species than average and are present in less parks than average. Figure 3.1. Correlation between occurrence of invasive and potential invasive species in Spanish urban parks and: A) their Standard Trending Value; (Pearson’s R = 0.59, pvalue < 0.001; B) their Weed Risk Assessment score (Pearson’s R = -0.22, p-value = 0.085). Reference smooth lines performed by ‘lm’. 0 50 100 150 200 0 4 8 12 16 20 24 28 STV A) −5 0 5 10 15 20 25 0 4 8 12 16 20 24 28 Num. of Parks WRA B) 95 2013). Moreover, the species A. altissima (tree of heaven), present in 43.5 % of the parks studied, is also listed as invasive by the European Regulation (European Commission, 2019). These species shall not be kept, bred nor permitted to reproduce or grown, unless it is done in contained holding, where are physically isolated and cannot escape or spread by unauthorized persons (European Commission, 2014). Therefore, these species should be removed from the Spanish urban parks. On the other hand, our analysis identified only one species with no risk to be invasive. Ficus benjamina is the only species in our Green list. Thus, based on the consulted scientific evidence (CABI, 2020), this species can be planted safely. However, the propagule pressure is an important factor determining invasion (Lockwood et al., 2005, 2009), so it is worth considering that planting species in large quantities and in many locations can change this scenario. Based on the precautionary principle, we encourage planting native species as the best alternative. One of the highest interests for management are the species belonging to the Attention and the Watch lists. These are invasive and potentially invasive species in which we performed a detailed analysis of the impacts they can cause according to!the mechanisms proposed in!Blackburn et al (2014). We recommend that species in the Attention List should be considered for regulation, especially all the species with high Priority Indexes such as Ricinus communis (PI = 73), Robinia pseudoacacia (PI = 73), Parkinsonia aculeata (PI = 67) and Eucalyptus globulus (PI = 65). The remaining species of the Attention list should be at least part of a monitoring program to prevent their spread in natural areas adjacent to parks. Moreover, the 12 species in the Watch List should also be included in early warning and active surveillance programs such as the proceeding of lists of non-native species potentially capable of competing with native wild species, altering their genetic purity or ecological balances, set out by the Ministry of Ecological Transition of Spain (BOE, 2020). Following our initial hypothesis, we found a positive correlation between the standard trending values (STV) of the species and their park occurrences. This result reinforces the notion that a greater prevalence of species at urban parks generates greater interest in people. This can cause a fashion-creating effect that can act as a snowball process influencing in turn the interest of some particular species to be planted for instance in private gardens. It has been proven that an increase in Google, reflected in the Google Trends statistics, is directly related to an increase in private commerce (Vosen and Schmidt, 2011). Thus, the presence of invasive and potential invasive species may be generating an interest, which translates into a greater use of these species, increasing their trade and commerce, and so the dispersion of propagules through the private breeding and cultivation, favoring invasions (Hulme et al., 2018; Lenda et al., 2014). On the other hand, promoting the use of ornamental native species, can generate an awareness effect that favors people’s interest on the native flora, and urban parks can also be considered of conservation interest (Alvey, 2006; Vaz et al., 2018). The use of non-native species in urban parks is worrisome for the impacts they can cause if they escape and invade natural ecosystems. On average, each of the 14 impact types can be caused by 19 species, and each of these species is present, on average, in 6 parks. Eighty percent of the species can compete with native species. The ability to modify the habitat (71 % species) and causing human health impacts (52 %) are also highly potential impacts to occur. The species with the potential to cause these impacts have higher occurrence than average. In particular, there are more than 30 species that can cause human health problems, and each of these species appear in more than 7 parks. Considering that these species cause these impacts due to their traits (i.e. toxicity, 96 allergenicity, have thorns, etc.) even if they do not establish in the wild, we recommend these species to be removed from parks. The ability to modify native habitats, to compete with native species, to modify the natural succession and to be toxic are impacts highly correlated to each other. Our analysis does not demonstrate causality, however, some direct links might take place. For example, allelopathy, included as a toxicity impact, might increase plant competition by influencing soil nutrient availability (Inderjit and del Moral, 1997; Medina- Villar et al., 2017); competition with native plants also changes species composition and diversity, and thus, succession and habitat structure (Schoener, 1974). Moreover, as this category of impact includes not only allelopathy, but also toxicity and poisoning, they can also cause impacts on human health. In fact, poisoning, allelopathy and toxicity are the impacts with highest number of correlations, being also highly related to the impacts on agriculture because allelopathy can have an inhibitory effect on crop production (Qasem and Foy, 2001), and on other socioeconomic impacts such as livestock poisoning. Hybridization and disruption of the food webs are not correlated to any other impact. That the disruption of the food webs is not correlated to other impacts such as competition with native species, the physical modification of the habitats or changes in nutrient cycling is an unexpected result. We think that a deeper analysis of species impact profiles is needed, to test whether these unexpected result holds in other species assemblages, or whether our non-significant correlations are due to limited information from the pool of species analyzed. Finally, it is worth mentioning that we did not found cases of counterbalancing impacts, thus synergies are more common than trade-off between pairs of impacts (Vilà and Hulme, 2017). Possible positive effects of introduced non-native species have not been considered in this paper. From a cultural ecosystem service perspective, the presence of nonnative trees are highly valued by aesthetics, eco-tourism and as cultural heritage (Vaz et al. 2018). Furthermore, non-native trees can also enhance regulating ecosystem services such as climate regulation, soil fertility and erosion control. However, in the context of, not only non-native, but invasive or potential invasive species, the valuation of impacts in ecosystem services are strongly context dependent varying across tree types, climatic and socioeconomic conditions of the area of introduction (Castro-Diez et al. 2019). For this reason, analyses as the one carried out in this paper are of great scientific, legislative and managerial value. Because our method is reproducible, new available information has served to provide here some updates compared to a previous risk analysis on nursery plants (Bayón and Vilà, 2019b). Specifically the species Cupressus arizonica and Jacaranda mimosifolia, that were in the Uncertainty List are now classified in the Watch List because the low number of impacts according to CABI (2020). However, there are still many species (96) in the Uncertainty List, and up to 228 species (more than the half non-native species of the whole dataset) in the Data Deficient List, with no data available at all. This is a recurring problem that deserves to be addressed as soon as possible (Bayón and Vilà, 2019b). We think that periodic updates of species listing are needed, as new information is available (Gallardo et al. 2016), a task that requires a collective effort of a larger team of experts (González-Moreno et al., 2014; Roy et al., 2019). Overall, our results show that despite invasive and potential invasive species are a minority of the species present at Spanish urban parks, some are present in all parks. 97 In particular from the 160 non-native tree species analyzed, only 16 were invasive (including regulated) and 47 were potential invasive. In addition, there is a logical bias in which the higher is the species’ degree of invasion, the higher the quantity and quality of the data. Therefore, the number of non-identified invasive or potential invasive species in the 228 species of the Data deficient list should be even minor. Because the introduction of plant species for ornamental purposes is one of the major pathways of invasion (Haeuser et al., 2018; Mayer et al., 2017), our horizon scanning approach indicates that more research is needed on the constrains of ornamental plants to establish in natural areas and to cause impact. 98 Acknowledgements This study was funded by the project of the Spanish Ministerio de Ciencia e Innovación EXARBIN (RTI2018-093504-B-100). Á. Bayón had a Predoctoral Contract for the Training of Doctors 2015 awarded by the Ministerio de Ciencia e Innovación of Spain, and co-financed by the European Social Fund (BES-2015-072929). O. Godoy acknowledges financial support by the Spanish Ministry of Economy and Competitiveness (MINECO) and by the European Social Fund through the Ramón y Cajal Programm (RYC-2017-23666). We thank J. Arroyo for tutoring A. Bayón’s thesis at the University of Sevilla. 99 Discusión general La entrada de plantas con fines comerciales u ornamentales es la mayor vía de introducción deliberada de especies no nativas (van Kleunen et al., 2018), e incluye algunas de las especies más invasoras del mundo (Hulme, 2007). En esta tesis se han realizado estudios que se centran, principalmente, en cuatro aspectos clave que son de gran relevancia en el manejo y la gestión de las especies de plantas ornamentales no nativas. Por un lado, se han realizado análisis de riesgo que identifican, entre las especies de plantas comerciales o presentes en parques urbanos de España, aquellas que son invasoras o potencialmente invasoras, así como los impactos que podrían causar si se establecen en el medio natural. También se ha elaborado un sistema de priorización para aquellas invasoras o potencialmente invasoras, basado en la capacidad invasora, los impactos que pueden causar y el interés que despiertan estas especies en la población. Por otro lado, centrado en especies leñosas, se han analizado cuáles son los rasgos biológicos más relevantes en el proceso de introducción, así como las interacciones entre dichos rasgos y las condiciones climáticas y socioeconómicas de las ciudades donde estas especies se han introducido. Finalmente, se han analizado cuáles son los principales impactos potenciales de estas especies y las relaciones entre ellos. Especies ornamentales invasoras y con potencial invasor disponibles en España La elaboración de listados de especies en función de su estado de invasión es una de las herramientas más útiles para la gestión de estas especies. En este estudio, hemos realizado estos listados en función del estado actual de las distintas especies, de la potencialidad invasora que tienen y de los impactos que podrían generar. Este tipo de listas se ha recomendado tanto desde la ciencia (Pyšek and Richardson, 2010) como desde órganos legislativos (BOE, 2020). Frecuentemente, las metodologías para llevar a cabo estas listas han planteado distintos protocolos semicuantitativos estandarizados y transparentes que puedan ser reproducibles y no se basen únicamente en conocimiento experto (Gassó et al., 2010; Kolar and Lodge, 2001; Leung et al., 2012; National Research Council, 2002; Pheloung et al., 1999; Roy et al., 2014, 2015). En esta tesis hemos trabajado en el contexto de las especies de plantas ornamentales disponibles a la venta en los viveros, así como en especies leñosas presentes en parques y jardines urbanos de la región peninsular de España. La formulación de las listas se ha sostenido en criterios de ‘escaneo de horizontes’, basados en el criterio de que las especies sean invasoras en entornos climáticamente similares o que dispongan de una tolerancia al clima de la región de introducción (Roy et al., 2014, 2015). También se ha realizado un análisis de impactos potenciales entre las especies invasoras y potencialmente invasoras que aún no están incluidas en la legislación vigente. Este, junto con el análisis de su potencial invasor según el WRA ideado en Australia (Gassó et al., 2010; Pheloung et al., 1999) y un novedoso sistema de análisis de preferencia social basado en la herramienta Google Trends (Vosen and Schmidt, 2011), nos ha permitido establecer un Índice de Prioridad. Este índice ofrece un valor para cada especie y permite ordenarlas. Esta priorización puede ser muy útil para tomar decisiones sobre su gestión y regulación (Gaertner et al., 2017a). Los resultados que hemos encontrado en este aspecto muestran que especies que están reguladas como invasoras siguen presentes en viveros y parques; lugares donde 100 también aparecen multitud de especies invasoras no reguladas y potencialmente invasoras. Por un lado, estos hechos ponen de manifiesto una deficiente aplicación de las normativas vigentes (BOE, 2013); un problema que ya se ha analizado en ciertas regiones del país como Galicia (Touza et al., 2014), y que compartimos con otras regiones del mundo como Sudáfrica (Cronin et al., 2017) o Florida (Wirth et al., 2004). Por otro lado, también muestran una falta de previsión de cara a regular especies que deberían estarlo, y no lo están, ya sea por ignorancia, por no disponer de recursos suficientes o por falta de interés (Comité de Flora y Fauna Silvestres, 2017; Sunder, 1993). Estos aspectos tanto de insuficiente aplicación de la normativa vigente, como de lo incompleto de sus listas, pueden ser objeto de análisis en futuros estudios. Se sugiere por lo tanto que investigaciones relacionadas con la percepción pública o de ciencia ciudadana pueden ser buenos sistemas para avanzar en esta línea. De hecho, la presión social derivada de la concienciación y de los proyectos de ciencia ciudadana se ha probado como efectiva a la hora de reducir la demanda de comercio de especies no nativas y su presencia en parques y jardines (Dehnen-Schmutz and Conroy, 2018; Reichard, 2004; Robinson, 2019), por lo que fomentar actuaciones de concienciación social puede redundar en una mejor aplicación de la normativa. Además, estos resultados deberían suponer una declaración de alarma para los legisladores, quienes tienen la responsabilidad última de endurecer la normativa, hacerla cumplir, y actualizar el Catálogo mediante la inclusión de las especies faltantes. A continuación, desgranaremos las distintas listas obtenidas y sus implicaciones particulares. Listas Prioridad Atención Vigilancia Verde Incertidumbre Datos deficientes Viveros 8 68 27 6 161 430 Parques 4 47 12 1 96 228 Tabla D. 1 Número de especies en las listas de Prioridad (invasoras reguladas), Atención (invasoras no reguladas y potencialmente invasoras con altos impactos potenciales), Vigilancia (invasoras no reguladas y potencialmente invasoras con bajos o nulos impactos potenciales), Verde (no invasoras, ni potencialmente invasoras, consideradas seguras), Incertidumbre (no invasoras con datos ambiguos que no permiten discriminar su potencialidad invasora), y Datos deficientes (especies para los que no hay datos suficientes como para iniciar el análisis); para plantas de 21 viveros de España, y para plantas leñosas de 46 parques urbanos de España. Listas de Prioridad Durante la realización de los capítulos 1 y 3 de esta tesis, se han revisado las legislaciones vigentes para España, siendo estas, el Catálogo Español de Especies Exóticas Invasoras (BOE, 2013) y la Lista de Especies Exóticas Invasoras Preocupantes para la Unión Europea (European Commission, 2014). Es conveniente recordar que la propia normativa española establece que «los poseedores (…) no podrán comercializar, reproducir ni ceder los ejemplares» de estas especies, y que en el caso de los parques y jardines públicos, son las administraciones competentes las que «eliminarán progresivamente, en los casos en que esté justificado, estas especies» (BOE, 2013). Se han encontrado disponibles a la venta en viveros o presentes en los parques especies que no solo presentan comportamiento invasor, sino que además están reguladas. Estas especies se han incluido en sendas listas de Prioridad (Tabla D. 1). Específicamente, se han localizado un total de ocho especies invasoras reguladas de las cuales cuatro están disponibles a la venta en viveros: Agave americana, Cortaderia selloana, Pennisetum 101 setaceum y Tradescantia fluminensis; mientras que las otras cuatro se encuentran en venta en viveros y están presentes en parques: Acacia dealbata, Buddleja davidii, Opuntia ficus-indica y Ailanthus altissima. De ellas, durante la publicación del primer capítulo, Pennistetum setaceum ya se encontraba regulada por la legislación europea, y más recientemente se ha añadido a esta regulación el árbol Ailanthus altissima. Hasta un 46 % de los parques presenta una o más especies invasoras reguladas, siendo la más habitual, Ailanthus altissima, con presencia en el 43.5 % de los parques estudiados. Concretamente, consideramos que esta especie tiene una gran importancia, no solo por su elevada presencia en parques, sino también por el hecho de aparecer disponible a la venta en los viveros españoles, por estar regulada en ambos listados — español y europeo—, y por su muy elevada capacidad invasora (Bayón and Llamas- García, 2011). De estos hechos se deriva que, tanto en el contexto del comercio como de los parques urbanos, la ley vigente en España no se está cumpliendo, lo que pone de manifiesto la necesidad de que se refuercen los sistemas de información e inspección. Los resultados arrojados por estas Listas de Prioridad muestran una profunda ausencia de atención por parte de las autoridades en dos aspectos importantes. Uno es la carencia a la hora de establecer inspecciones en los viveros, el otro es la gestión de los parques urbanos. Es necesario reforzar los sistemas de detección y eliminación de las especies recogidas en las Listas de Prioridad, así como incrementar el nivel de difusión y divulgación de la ley y de los conocimientos científicos sobre las invasiones biológicas, tanto hacia los sectores implicados —legisladores, gestores, comerciantes…— como a la sociedad general. Especies invasoras y potencialmente invasoras Entre las especies invasoras que aún no están reguladas, y las especies potencialmente invasoras, encontramos un total de 95 especies disponibles a la venta en viveros —de las cuales, 22 son invasoras— y un total de 59 especies leñosas presentes en parques —de las cuales, 12 son invasoras—; con cierto solapamiento entre ambos grupos. Esas cifras son variables a nivel geográfico, y aunque son claramente inferiores a las que encontramos en otras regiones como Beijing, China, que presenta en sus parques hasta 48 especies invasoras (Wang et al., 2011) o de las 37 halladas en Portland, Oregón (Talal and Santelmann, 2019), no dejan de ser cifras preocupantes sobre las que conviene actuar. Estas especies formarían lo que en otros trabajos se han denominado ‘listas rojas’ o ‘listas negras’ (Blackburn et al., 2014; Essl et al., 2011b; Gederas et al., 2012; Kumschick et al., 2015). Sin embargo, en este trabajo se ha realizado una priorización, disgregándolo en dos listas separadas. Para ello, se han discriminado aquellas que tienen mayores capacidades de generar impactos ambientales o socioeconómicos, de aquellas cuyos impactos en cualquier aspecto pueden ser escasos o nulos —que se conozca—. Como ya se ha mencionado, tomando la totalidad de los parques, de un total de 478 especies presentes, encontramos que 59 especies son invasoras no reguladas o potencialmente invasoras, y 4 especies más están reguladas por el Catálogo de Especies Invasoras (BOE, 2013). Sin embargo, analizando los parques de forma pormenorizada, hallamos que cada uno presenta en promedio 8.65 ± 0.97 especies invasoras no reguladas o potencialmente invasoras, más 0.57 ± 0.11 especies reguladas, de una media de 53.37 ± 5.14 especies en total. Esto implica que en el global de los parques haya una especie invasora o potencial invasora por cada aproximadamente 7.6 especies, mientras 102 que al analizar los parques de forma pormenorizada hallamos una por cada 5.8 especies. Hablando de especies reguladas, en el global de los parques encontramos una de cada 120 especies, dato que asciende a una de cada 94 al analizar los parques de forma pormenorizada. Este cambio de cifras para cada parque individual respecto al conjunto general de parques es preocupante, dado que implica que las especies invasoras y potencialmente invasoras están sobrerrepresentadas en los parques. Además, no existe ni un solo parque que esté libre de especies invasoras o potencialmente invasoras. Listas de atención Las Listas de Atención recopilan aquellas especies invasoras o potencialmente invasoras con capacidad para causar un gran número de impactos. En los viveros españoles, esta lista está compuesta por un total de 68 especies —de las cuales, 15 son invasoras—, mientras que en los parques encontramos 47 especies leñosas —de las cuales, 11 son invasoras—, con cierto solapamiento entre ambos grupos (Tabla D. 1). Entre algunas de las especies invasoras no reguladas que encontramos en las Listas de Atención se encuentran algunas con gran importancia como Robinia pseudoacacia, Elaeagnus angustifolia o Eucalyptus globulus. Estas especies ya se propusieron para su inclusión en la Lista Preliminar de Especies Exóticas Invasoras para España (Ministerio de Medio Ambiente, 2007) realizada para la primera entrada en vigor de la regulación de un Catálogo Nacional de Especies Exóticas Invasoras (BOE, 2007), pero a pesar de ello, ninguna de ellas ha sido, hasta la fecha, incluida aún en la normativa vigente (BOE, 2019). De hecho, la última de ellas, E. globulus, es una de las más polémicas junto a otras especies del género; están incluidas en un sólido informe del Comité Científico del Ministerio español de Agricultura, Pesca, Alimentación y Medio Ambiente que reitera la elevada importancia de su regulación (Comité de Flora y Fauna Silvestres, 2017), y sin embargo su inclusión se está decidiendo en los tribunales, a causa de una serie de conflictos de intereses movidos por el rédito económico de su explotación. Los resultados mostrados en este trabajo redundan en la necesidad de que estas especies invasoras, incluido E. globulus, presentes en las Listas de Atención sean incluidas en el Catálogo de Especies Exóticas Invasoras de España y se refuercen los métodos para su cumplimiento. En estas Listas de Atención también se incluyen numerosas especies que, ya estén naturalizadas en el medio natural, aparezcan de manera casual, o no se encuentren aún escapadas, presentan potencial invasor y muchos impactos potenciales. Es recomendable, dada esa alta importancia que tiene la prevención de futuras invasiones, que estas especies se incluyan en sistemas de vigilancia y monitorización tales como el plan propuesto por el Ministerio de Transición Ecológica y el Reto Demográfico en su Real Decreto 570/2020 (BOE, 2020). Listas de vigilancia Las Listas de Vigilancia, por otro lado, incluyen especies invasoras y potencialmente invasoras que, al contrario de las especies de las Listas de Atención, presentan bajos o nulos niveles de impacto tanto ambiental como socioeconómico. En estas listas se han recopilado un total de 27 especies disponibles a la venta en viveros —de las cuales, 7 son invasoras— y un total de 12 especies de parques —de las cuales, solo una, Phormium tenax, es invasora—, con cierto solapamiento de especies entre ambas listas (Tabla D. 1). Si bien estas especies son menos preocupantes que las presentadas en las 103 Listas de Atención, siguen siendo especies potencialmente invasoras con riesgo de impactos en el medio natural que no deben obviarse. Consideramos que su inclusión en programas de prevención es algo que debe considerarse (BOE, 2020). Es llamativo el hecho de que algunas de las especies que, durante el primer capítulo, relativo a los viveros, aparecían en esta Lista de Vigilancia, en el desarrollo del tercer capítulo, relativo a los parques, saltaron a la Lista de Atención. Entre ellas, destacan las especies invasoras Acer negundo y Eucalyptus camaldulensis, así como otras potencialmente invasoras como Parthenocissus quinquefolia, Salix babylonica y Fraxinus americana. Este cambio de lista se debe a que durante la realización del último de los análisis, y en término posterior a la publicación científica del primer capítulo, la información disponible sobre impactos potenciales de estas especies en las principales bases de datos consultadas (CABI, 2020; Invasive Species Specialist Group, 2015) había aumentado, y por tanto, se disponía de más información al respecto. Estos cambios de criterio destacan lo importante que es mantener actualizada la información disponible, y refuerza lo acertado del nombre que le hemos puesto a este listado en particular: “de Vigilancia”. En el caso concreto de E. camaldulensis, tal y como sucedía con el ya citado E. globulus y otras especies del género, está presente también en informe del Comité Científico del Ministerio español de Agricultura, Pesca, Alimentación y Medio Ambiente (Comité de Flora y Fauna Silvestres, 2017) que reitera la importancia de su regulación a pesar de los intereses económicos que puedan existir para su cultivo (Sunder, 1993). Listas verdes y otras listas Respecto a las Listas Verdes —también denominadas “listas blancas” en otros trabajos (Simberloff, 2006)—, disponemos de información suficiente para establecer como segura la introducción de siete especies (Tabla D. 1): Averrhoa carambola, Celosia argentea, Mangifera indica, Nelumbo nucifera, Pogostemon helferi, Senna corymbosa y Ficus benjamina, siendo esta última la única presente en el estudio realizado en parques. Dada la información disponible en la base de datos (CABI, 2020) podemos asumir que estas especies pueden ser introducidas sin riesgos ambientales ni socioeconómicos. Por otro lado, es muy importante que se tengan siempre en cuenta las Listas de Incertidumbre y las Listas de Datos Deficientes, que contienen en conjunto el mayor número de especies en cada estudio (Tabla D. 1). Estos dos listados incluyen especies para las que la información no era suficientemente buena como para ser incluidas en otra lista —en el caso de las Listas de Incertidumbre— o que, directamente, no existían datos para siquiera considerar esas especies en el ‘escaneo de horizonte’ —en el caso de las Listas de Datos Deficientes—. Estas especies no suelen incluirse en los estudios sobre análisis de riesgo. Sin embargo, en la tesis le damos mucha importancia a estas especies, cuya información es vaga o ausente, pues entre ellas puede haber especies potencialmente invasoras que no estamos detectando, o tal vez, especies seguras que no estamos considerando. De hecho, cabe destacar que en el tiempo transcurrido entre el establecimiento de los listados de viveros y el de los listados de parques, ha habido dos especies Cupressus arizonica y Jacaranda mimosifolia, que han cambiado de la Lista de Incertidumbre a la Lista de Vigilancia, a causa también del incremento de información disponible en la base de datos de CABI (2020) para estas especies. De nuevo, este hecho es un indicativo claro de la importancia de continuar ampliando y mejorando la información científica disponible, con el fin de reducir este alto 104 nivel de incertidumbre y mejorar la calidad de los listados finales. Algo que puede ser objeto de futuras investigaciones. Priorización de especies invasoras El uso de sistemas de priorización en este tipo de listados ha demostrado ser una herramienta útil (Gassó et al., 2010; Pheloung et al., 1999; Weber and Gut, 2004). Sin embargo, consideramos que, especialmente en referencia a las plantas ornamentales y con valor comercial, estos índices están incompletos, debido a que no incluyen un aspecto muy relevante, que es el interés que la gente puede tener por estas especies. Hemos desarrollado un Índice de Prioridad que tienen en cuenta el interés de la especie según Google Trends donde hemos escaneado las especies de las Listas de Atención. Este valor de interés, que es un aspecto novedoso en invasiones biológicas, es medido mediante la búsqueda pareada y sistemática de cada especie, junto a una especie control, a través de la herramienta Google Trends y estandarizando los valores después respecto a dicha especie control. Esta herramienta on-line ya se ha empleado en otros campos como una valiosa fuente de información primaria que se puede utilizar para pronosticar el consumo y el comercio y otras formas de medir el interés del público (Burivalova et al., 2018; Vosen and Schmidt, 2011). Con el fin de mantener la coherencia, la especie control que se ha empleado en ambos casos, el estudio en parques y jardines, y el estudio en viveros, ha sido la misma, Robinia pseudoacacia. Dado que los valores de interés pueden cambiar en términos geográficos, es esperable que en otras regiones la especie control sea otra; sin embargo, en próximos estudios se recomienda utilizar la misma especie control, para que los futuros resultados sean comparables con los obtenidos en este trabajo. Este índice de prioridad resulta muy versátil: permite al usuario acotar la evaluación de forma pormenorizada tanto en el tiempo como en el espacio. Además, al poder seleccionar un rango de fechas específico en el STV, también permite observar cómo cambia esa prioridad a lo largo del tiempo en base al cambio de interés de la población. Este índice de prioridad puede, por tanto, ser de gran interés y utilidad para las administraciones y para los servicios de gestión ambiental. Puede ser empleado, tal y como se expone en este trabajo, para identificar especies potencialmente invasoras y con alto riesgo, incluso aunque aún no estén presentes en la naturaleza. Impactos potenciales de las especies invasoras y sus interacciones Entre los impactos más relevantes que pueden causar las especies invasoras se encuentra la competencia, potencialmente causada por el 80 % de las especies invasoras y potencialmente invasoras estudiadas, cada una de ellas presente, en promedio, en más de 6 parques; la modificación física del hábitat, potencialmente causada por el 71 % de las especies invasoras y potencialmente invasoras estudiadas, cada una de ellas presente, en promedio, también en más de 6 parques; y el impacto sobre la salud humana. Este impacto potencial incluye riesgos de intoxicación, de causar heridas, o de alergias al polen. Puede ser causado por hasta el 53 % de las especies, cada una de ellas presente, en promedio, en 7.3 ± 1.26 parques. Presencia superior a la que encontramos en el global de especies causantes de impactos, presentes en 6.02 ± 1.59 parques, y muy superior al valor general en todas las especies, que están presentes en 5.04 ± 0.26 parques. Estos impactos potenciales son importantes a tener en cuenta de cara a la posible regulación de estas especies. 111 General conclusions 1. This thesis has documented which non-native plant species available in Spanish nurseries and present in the main urban parks and gardens are invasive or they can be considered a high risk of becoming invasive. Overall, we have screened 914 species sold in 21 nurseries and 474 woody species present in 46 urban parks across the country. However, for the majority of these species we could not find enough information to perform the assessment (47 % from nurseries and 48 % from parks, classified in the Data Deficient Lists). In urban parks, there is a high level of species’ substitution: while 2.3 % of species are in more than half of the parks, up to 60 % are present in three parks or less. 2. The most common potential impacts of these species, if they invade, are competition with native species, modification of the habitat structure and damage to human health. The assessment of the impacts of these species has important implications for their management and policymaking. 3. A large number of these species can cause several potential impacts simultaneously. The most relevant group of related potential impacts would be competition, toxicity, physical modification of the habitats, impacts on natural succession and impacts on agriculture. 4. Invasive and potentially invasive species have higher occurrence, on average, than the rest of the non-native species. This tendency is also the case of the regulated invasive species. This shows a very poorly application of the regulations and normative both national and regional. 5. We have identified eight invasive species already regulated in Spain or in the EU that are still traded and, four of them still present in urban parks. This “Priority list” includes: Acacia dealbata, Agave Americana, Ailanthus altissima, Buddleja davidii, Cortaderia selloana, Opuntia ficus-indica, Pennisetum setaceum and Tradescantia fluminensis. Up to 46 % of the parks have one or more regulated invasive species. The most common is Ailanthus altissima, present in 43.5 % of the studied parks. 6. We have identified invasive and potential invasive species with high level of potential impacts, enlisted in the Attention Lists. The list was composed by 68 species in Spanish nurseries, and 47 woody species in Spanish urban parks. We founded invasive not regulated species with high relevance, such as Robinia pseudoacacia, Elaeagnus angustifolia or Eucalyptus globulus, presents in urban parks and nurseries. We consider that these species should be included in monitoring and surveillance systems. There is not a single park free of invasive or potentially invasive species. 7. We have developed a novel semi-quantitative prioritization system of invasive and potential invasive species, called Priority Index, which includes in its calculation the species invasive potential, their potential environmental and socioeconomic impacts and their societal interest in Internet webpages. This Priority Index can be very useful for administration and environmental management services. 8. We have also identified seven non-native plant species with no invasion potential and thus that it may be safe to be planted outdoors. This “Green list” includes: Averrhoa carambola, Celosia argentea, Ficus benjamina, Mangifera indica, Nelumbo nucifera, Pogostemon helferi and Senna corymbosa. 112 9. We found correlation between climatic variables, and the presence of conspicuous flowers, the tolerance to shade and the values and range of hardiness —the ability to survive to low temperature—. Values of hardiness is positively related to the minimum temperature of the coldest month, and the annual range of temperature. Meanwhile tolerance to shade is negatively related to both climatic variables. Presence of conspicuous flowers are positively related to the minimum temperature in the coldest month; while range of hardiness are negatively related to this climatic variable. Therefore, these traits are the most relevant features in selection of nonnative woody species to be planted in urban parks according to the climatic conditions. 10. 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Oikos 122, 183–190. https://doi.org/10.1111/j.1600- 0706.2012.20486.x 131 Apéndices Apéndices del Capítulo 1 Appendix 1.1 Information about the 21 study nurseries from Spain The list of taxa was compiled through the systematic consultation of Spanish nursery catalogs which provide information on plants for sale. As shown in the graph below, the number of taxa did not increase after the 15th!catalog was consulted. Our database included a total of 1036 taxa from 21 nurseries. Notice that these nurseries are distributed across all biogeographic regions of mainland Spain. 132 Nursery Source Access date Province Num. taxa 1 Viveros Plantamus https://plantamus.com 11/12/15 A Coruña 286 2 Viveros Sanchez http://viverossanchez.com 01/04/16 Guadalajara 638 3 Viveros Maiplant http://www.maiplant.com 02/23/16 Alicante 66 4 Alberola Viveros http://www.alberolaviveros.com 02/23/16 Valencia 510 5 Viveros Bargues http://www.viverosbargues.com 02/24/16 Valencia 97 6 Viveros Rucat http://www.viverosrucat.es 01/26/16 Madrid 189 7 Viveros Veron http://viverosveron.com 01/26/16 Zaragoza 92 8 Garden Center Campo Grande http://www.campogrande.es 01/31/16 Valladolid 214 9 Plantas del Sueve http://www.delsueve.com 02/01/16 Asturias 217 10 Viveros Urkiondo http://www.urkiondo.com 02/09/16 Guipuzkoa 174 11 Viveros Barra http://viverosbarra.es 05/01/16 Leon 410 12 Viveros Coplant http://www.coplant.es 05/20/16 Pontevedra 137 13 Viveros Borrazas http://www.viverosborrazas.com 06/11/16 A Coruña 231 14 Viveros Zuaime http://www.viveroszuaime.es 06/14/16 Granada 238 15 Viveros Sevilla http://www.viverossevilla.com 06/16/16 Sevilla 180 16 Viveros Ferca http://viverosferca.com 07/28/16 Ciudad Real 162 17 Viveros Ibañez http://www.viverosibanez.es 09/20/16 Zaragoza 171 18 Viveros Corma http://www.corma.es 09/30/16 Barcelona 383 19 Viveros Canós http://viveroscanos.com 09/30/16 Badajoz 132 20 Viveros Perica http://viverosperica.com 10/03/16 La Rioja 195 21 Viveros Ametza http://www.viverosametza.com 10/03/16 Navarra 147 133 Appendix 1.2 Watch list The Watch List includes invasive and potentially invasive species with potential impacts below median; classified as: a) not in the wild, b) casual, c) naturalized and d) invasive species. Native distribution: Afr: Africa; As: Asia (Temperate); AT: Asia (Tropical); Aus: Australia; NAm: North America; SAm: South and Center America. Family Native distribution Impacts Envir. Impacts Socioec. a) Species not in the wild Berberis darwinii Berberidaceae SAm 2 0 Euonymus alata Celastraceae As 2 0 Ficus elastica Moraceae As 2 0 Fraxinus americana Oleaceae NAm 0 0 Gunnera manicata Gunneraceae SAm 2 0 Pyrus calleryana Rosaceae As, AT 0 0 Thevetia peruviana Apocynaceae NAm, SAm 0 0 Zelkova serrata Ulmaceae As, AT 0 0 b) Casual species Ageratum houstonianum Asteraceae SAm 0 0 Aloe vera Liliaceae Afr 0 0 Corymbia citriodora Myrtaceae Aus 0 0 Erigeron karvinskianus Asteraceae SAm 0 0 Lagerstroemia indica Lythraceae As, AT 2 0 Salix babylonica Salicaceae As 0 0 Salvia microphylla Lamiaceae NAm 1 0 Trachycarpus fortunei Arecaceae As 1 0 c) Naturalized species Alpinia zerumbet Zigimberaceae AT 2 0 Berberis aquifolium Berberidaceae NAm 1 0 Parthenocissus quinquefolia Vitaceae NAm 0 0 Passiflora edulis Passifloraceae SAm 2 0 d) Invasive species Acer negundo Aceraceae NAm 1 0 Cyperus alternifolius Cyperaceae Afr 0 0 Eriobotrya japonica Rosaceae As 0 0 Eucalyptus camaldulensis Myrtaceae Aus 1 0 Oenothera biennis Onagraceae NAm 1 0 Pelargonium capitatum Geraniaceae Afr 2 0 Tropaeolum majus Tropaeolaceae SAm 0 0 135 Appendix 1.3 Uncertainty list The uncertainty list includes non-invasive and potentially non-invasive species which lack sufficient information, or those that do not meet the requirements to be included in the Green List; classified as: a) not in the wild, b) casual, and c) naturalized species. Native distribution: Afr: Africa; As: Asia (Temperate); AT: Asia (Tropical); Aus: Australia; Eur: Europe; NAm: North America; Pac: Pacific; SAm: South and Center America. Invasive elsewhere and climate suitability: Y: yes; N: no; Confidence in the likelihood of climatic suitability: H: high; M: med. a) Species not in the wild Family Native distribution Invasive Elsewhere Climate suitability Confidence Agave vivipara Agavaceae SAm Y Y L Anubias barteri Araceae Afr N N L Anubias hastifolia Araceae Afr Y N L Anubias heterophylla Araceae Afr Y N L Bacopa caroliniana Plantaginaceae NAm N Y L Bismarckia nobilis Arecaceae Afr N N L Blyxa japonica Hydrocharitaceae NAm N Y H Brahea armata Arecaceae NAm N Y L Brahea edulis Arecaceae NAm N Y L Butia capitata Arecaceae SAm N Y L Cabomba furcata Cabombaceae SAm N Y L Campanula portenschlagiana Campanulaceae Eur N Y H Carex buchananii Cyperaceae Aus N Y L Chamaedorea seifrizii Arecaceae NAm N Y L Clematis montana Ranunculaceae As N Y M Cordyline australis Asparagaceae Aus N Y L Corymbia ficifolia Myrtaceae Aus N Y L Cotinus coggygria Anacardiaceae As N Y H Cryptostegia madagascariensis Apocynaceae Afr Y N H Cycas revoluta Cycadaceae As N Y L Delonix regia Fabaceae Afr Y N H Euphorbia lactea Euphorbiaceae SAm Y N M Ficus lyrata Moraceae Afr N Y M Ficus microcarpa Moraceae Aus Y Y L Freesia alba Iridaceae Afr N Y M Fuchsia magellanica Onagraceae SAm Y N M Gardenia jasminoides Rubiaceae As - AT N Y M Gaultheria mucronata Ericaceae SAm N Y L Gaura lindheimeri Onagraceae NAm N Y M Geum coccineum Rosaceae Eur N Y M Glossostigma elatinoides Phrymaceae Aus N Y H Lilaeopsis brasiliensis Apiaceae SAm N Y L Liriope muscari Liliaceae As - NAm N Y L Livistona chinensis Arecaceae As Y N L Lonicera pileata Caprifoliaceae As N Y L Loropetalum chinense Hamamelidaceae As N Y M Mayaca fluviatilis Mayacaceae SAm N Y L Myoporum tetrandrum Scrophulariaceae Aus N Y L Ophiopogon japonicus Asparagaceae As N Y H Opuntia microdasys Cactaceae NAm N Y H Perovskia atriplicifolia Lamiaceae As N Y M Pittosporum tenuifolium Pittosporaceae Pac N Y L Pogostemon stellatus Lamiaceae AT - Aus N N L Rotala wallichii Lythraceae As N Y L Serenoa repens Arecaceae NAm N Y L Vaccinium corymbosum Ericaceae NAm N Y M 136 b) Casual species Family Native distribution Invasive Elsewhere Climate suitability Confidence Acacia baileyana Fabaceae Aus N NA NA Acer campestre Sapindaceae Afr - Eur N NA NA Aloe arborescens Liliaceae Afr N NA NA Aloe maculata Liliaceae Afr N Y H Aloysia citrodora Verbenaceae SAm N Y H Anthriscus cerefolium Apiaceae Eur N NA NA Artemisia dracunculus Asteraceae As - Eur - NAm N NA NA Brachychiton populneus Malvaceae Aus N NA NA Caesalpinia gilliesii Fabaceae SAm N NA NA Callistemon citrinus Myrtaceae Aus N NA NA Calocedrus decurrens Cupressaceae NAm N NA NA Campsis radicans Bignoniaceae NAm N NA NA Catalpa bignonioides Bignoniaceae NAm N NA NA Catharanthus roseus Apocynaceae Afr N NA NA Cedrus deodara Pinaceae As N NA NA Cedrus libani Pinaceae Eur N NA NA Cercis siliquastrum Fabaceae Eur N NA NA Coffea arabica Rubiaceae Afr N NA NA Crassula ovata Crassulaceae Afr N Y M Cupressus arizonica Cupressaceae NAm N NA NA Cupressus macrocarpa Cupressaceae NAm N NA NA Cydonia oblonga Rosaceae Eur N NA NA Diospyros kaki Ebenaceae As N NA NA Eucalyptus gunnii Myrtaceae Aus N NA NA Euonymus japonicus Celastraceae As N NA NA Euphorbia candelabrum Euphorbiaceae Afr N NA NA Helianthus annuus Asteraceae NAm N NA NA Hibiscus rosa-sinensis Malvaceae As N NA NA Hibiscus syriacus Malvaceae As N NA NA Hyacinthus orientalis Asparagaceae Afr - AT N NA NA Jacaranda mimosifolia Bignoniaceae SAm N NA NA Jasminum nudiflorum Oleaceae As N NA NA Jasminum officinale Oleaceae As N Y H Juglans nigra Juglandaceae NAm N NA NA Koelreuteria paniculata Sapindaceae As N NA NA Lagunaria patersonii Malvaceae Aus N NA NA Larix decidua Pinaceae Eur N NA NA Lepidium sativum Brassicaceae Afr N NA NA Ligustrum ovalifolium Oleaceae As N NA NA Lobelia erinus Campanulaceae Afr N NA NA Lycium barbarum Solanaceae As N NA NA Malus domestica Rosaceae As N NA NA Mimosa pudica Fabaceae SAm Y Y L Monstera deliciosa Araceae SAm N NA NA Origanum majorana Lamiaceae As - Eur N NA NA Phytolacca dioica Phytolaccaceae SAm N NA NA Pinus canariensis Pinaceae Afr N NA NA Pinus strobus Pinaceae NAm N NA NA Pistacia vera Anacardiaceae As - Eur N NA NA Pittosporum tobira Pittosporaceae As N NA NA Plumbago auriculata Plumbaginaceae Afr N NA NA Populus simonii Salicaceae As N NA NA Prunus armeniaca Rosaceae NAm N NA NA Prunus domestica Rosaceae Eur N NA NA Pseudotsuga menziesii Pinaceae NAm N NA NA Ruta graveolens Rutaceae Eur N NA NA Salvia splendens Lamiaceae SAm Y N M