Methodological overview and data-merging approaches in the study of plant–frugivore interactions

www.oikosjournal.org OIKOS Oikos 1 –––––––––––––––––––––––––––––––––––––––– © 2021 The Authors. Oikos published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Subject Editor: Eliana Cazetta Editor-in-Chief: Pedro Peres-Neto Accepted 10 June 2021 2022: e08379 doi: 10.1111/oik.08379 2022 e08379 Recording species interactions is one of the main challenges in ecological studies. Frugivory has received much attention for decades as a model for mutualisms among free-living species, and a variety of methods have been designed and developed for sampling and monitoring plant–frugivore interactions. The diversity of techniques poses an important challenge when comparing, combining or replicating results from different sources with different methodologies. With the emergence of modern techniques, such as molecular analysis or multimedia remote recorders, issues when combining data from different sources have become especially relevant. We provide an overview of all the techniques used for monitoring endozoochorous primary seed dispersal, focusing on a critical appraisal of the advantages and limitations, as well as the context-dependency nature, of the different methods. We propose five data merging approaches potentially useful to combine frugivory interactions data from different methodologies. Additionally, we provide two case studies where we combine empirical data from plant–animal interactions in Mediterranean shrublands using different methodologies. Data merging resulted in a net increase in the number of distinct pairwise interactions recorded and compensated biases inherent to different methods, resulting in a more robust estimation of network topological descriptors. These case studies clarify the context-dependent character of the merging approaches, highlighting the value of collecting detailed information on the sampling effort in terms of reliable results and reproducibility. Finally, we discuss the trends with different methodological approaches used in the last decades and future perspectives in this field. Keywords: ecological networks, endozoochory, frugivory, methods, plant–animal interactions, seed dispersal Introduction One of the greatest challenges that ecologists face is to properly determine the biodiversity present in their study systems, i.e. the presence and relative abundance of species (Magurran 1988). An important facet, yet a frequently dismissed one, in biodiversity analysis is to document how species interact with one another, and what are the outcomes of these interactions (Valiente-Banuet et al. 2014). Scholar accounts Methodological overview and data-merging approaches in the study of plant–frugivore interactions ElenaQuintero, JorgeIsla and PedroJordano E. Quintero (https://orcid.org/0000-0003-4979-6874) ✉ ([email protected]), J. Isla (https://orcid.org/0000-0002-2307-9730) and P. Jordano (https:// orcid.org/0000-0003-2142-9116), Estación Biológica de Doñana, CSIC, Sevilla, Spain. PJ also at: Dept Biología Vegetal y Ecología, Univ. de Sevilla, Sevilla, Spain. Research 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2 of the myriad connections among species date back at least to al-Jāhiz in the 9th century or even earlier to Aristotle in the 4th century BCE (Egerton, 2007). Yet, the more formal onset of the ecology of interactions took place later, fostered by late 18th-century naturalists. Pioneer studies of ecological interactions were focused on trophic cascades within food webs (Cohen 1978, Polis and Strong 1996), and later unfolded into the analysis of complex networks of ecological interactions in the late 1990s (Bascompte and Jordano 2014). Effectively incorporating the quantification and analysis of ecological interactions is essential to recent efforts to preserve the value of Biodiversity (IPBES 2019) yet we are still far from achieving this goal, not only by assessing the actual richness and diversity of interactions in nature, but also by assessing the ecological services associated to them. Frugivory has received much attention for decades and a variety of methods have been designed and developed to track how encounters between animal frugivores and plants result in seed dispersal events for the plants and food resource provisioning for the animals (Estrada and Fleming 1986, Fleming and Estrada 1993, Levey et al. 2002, Dennis et al. 2007). This reciprocal service is the basis of coevolved plant– frugivore mutualistic interactions and implies enormous consequences for forest regeneration and ecosystem functioning (Howe and Smallwood 1982, Schupp et al. 2002). A crucial aspect of interaction sampling, besides recording the mere presence of an interaction, is also measuring its relative frequency and its impact, i.e. the outcome of interactions in terms of fitness effects for the interacting partners. The study and monitoring of seed dispersal events became increasingly apparent from late eighties with the publication of the first volume of FSD (Estrada and Fleming 1986), and direct observation and census at focal plants became a standard method to inventory plant–frugivore interactions with multiple objectives. Yet, new methods have emerged in the last decades allowing indirect, delayed recording of these interactions and opening new possibilities for research on frugivory and seed dispersal (Forget and Wenny 2005, Carlo et al. 2009, González-Varo et al. 2014). The diversity of techniques available to monitor species interactions pose the important challenge of comparing results obtained with different methodologies, replicating the results or incorporating interaction data from different sources. With the emergence of modern techniques, such as DNA-based molecular analysis (Carreon-Martinez and Heath 2009, Valentini et al. 2009, González-Varo et al. 2017, Mata et al. 2019), this has become especially relevant. Merging data from different sources allows us to maximize information and improve research potential for any kind of frugivory and seed dispersal study. Combining the distinct data types and information yielded by such a diversity of methods can become a difficulty and even a limitation if there are no well-established guidelines. Given the wide spectrum of seed dispersal interactions that exist, in the first part of this manuscript we provide a methodological overview where we primarily focus on endozoochorous seed dispersal. Our goal in this part is not a comprehensive review of methods, rather we aim to offer a critical appraisal of the advantages and limitations as well as the context-dependent nature of the major sampling methods, focusing on methods complementarity, reproducibility and sampling effort. In the second part of this manuscript we propose and illustrate five different merging approaches to combine datasets originated with different sampling methodologies. The merging data approaches we describe here may be also applicable to other interaction forms aside endozoochorous seed dispersal, such as synzoochory, epizoochory or secondary seed dispersal (Costa et al. 2014, Gómez et al. 2019) or even other types of interactions like pollination, host–parasite or plant–plant facilitation. To exemplify and validate the described merging methods we provide two case studies, using empirical data where we compare and combine different methodologies using an interaction network approach. Finally, we discuss the trends in the use of different approaches over the last decades and the future perspectives in this field. We hope that this overview and the combination strategies proposed here can serve as a useful reference for researchers when approaching future frugivory studies and may complement other papers in this Special Issue dealing with plant–frugivore interactions and thorough field-sam- pling approaches. Study focus, scale and resolution Depending on the study’s objective, the term ‘interaction’ and its measurement can vary greatly. The strength of an interaction (i.e. the effect magnitude of its outcome, in addition to its frequency of occurrence) can change depending on the focus of study and how its outcome for the partners is measured. Therefore, the study question will determine when and how interactions are monitored (Niquil et al. 2020). Focus may be directed towards the plant partner (i.e. phytocentric), the animal partner (i.e. zoocentric) or both (Jordano 2016). These approaches impose different sampling challenges and information, varying in their characteristics, accessibility, visibility, potential biases, logistic limitations, sampling effort demand, etc. In many cases the goals themselves can clearly establish the characteristics of the study focus (e.g. understand the role in seed dispersal of juvenile versus adult individual animal frugivores, González- Varo et al. (2019). However, sometimes the focus of study may be more complex to define (e.g. select a phytocentric or zoocentric approach when comparing seed dispersal networks between sites). Other important aspects include the resolution and scale of the sampling, being the intra-individual level the most refined, and scaling up to the aggregation of species in groups, eventually including higher taxonomic levels, morphological or functional groups (Moran et al. 2004). Clearly defining the spatial and temporal scale of the study is key. Plant–frugivore interaction patterns at different spatial scales are not necessarily consistent (Jordano 1993, García et al. 2004), furthermore it becomes extremely difficult to extend analyses of e.g. dispersal kernels, beyond the local scales 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3 (García and Borda-de-Água 2017). Likewise, temporal variations driven by the phenology of the species or the availability of resources will largely determine the interactions detected (Carnicer et al. 2009, Costa et al. 2020). When combining studies or methodologies, it is advisable to look at the study focus and at the scale at which each data source has been gathered. At the end of the combining methodologies section we propose a way of correcting the divergence that may exist between scales. Also, one of the case studies illustrates an example of data merging for two methodologies with different focus; observations of foraging animals, as plant-focused and mist-netting, as animal-focused. A general overview of sampling methods In order to illustrate current methodologies, we will divide sampling techniques into three intuitive categories based on the main stages of the seed dispersal process (Schupp et al. 2017). Depending on when we are collecting information for animal–plant interactions, the sampling will be directed towards one of these three stages (Fig. 1): ‘visitation’, ‘transport’ or ‘deposition’. While some methods may be directed to more than one stage, we have classified them in the most representative one. The first, or early stage (‘visitation’), refers to the actual interaction on the plant, when the animal is manipulating, removing or ingesting the fruits on the plant. The subsequent two stages refer to the dissemination process, where the seeds are first transported (i.e. moved some distance away from the source plant, ‘transport’) and then deposited (i.e. disseminated), which may involve actual burying of the seed or just dropping, e.g. by spitting, regurgitation or defecation (‘dissemination’). Methods targeting ‘visitation’ Methods used to monitor the initial ‘visitation’ stage (Fig. 1, Table 1) are typically directed towards seed sources (i.e. maternal) plant individuals with standing fruit crops, where it is possible to observe the interaction occurring. When considering the resource-harvest type of mutualism characteristic to most plant–frugivore interactions (Janzen 1983, Ollerton 2006), this typically refers to the feeding phase, when the source plant and individual animal actually interact. The methods used in this stage can be classified into direct methods that allow us to observe the interaction directly, and indirect methods based on the detectable signals of the interaction. Traditional methods are field focal observations at fruiting plants (Howe and Vande Kerckhove 1980, Herrera and Jordano 1981, Snow and Snow 1988, Jordano and Schupp 2000, Stevenson et al 2015), transects (Galetti and Pizo 1996), animal visual trackings (Gestich et al. 2019) and spot censuses (Howe and Vande Kerckhove 1981, Rother et al. 2015). The implementation of technological advances such as camera traps or other multimedia recording systems (e.g. action cameras) also allows us to observe the interaction taking place (Campos-Arceiz et al. 2012, Miguel et al. 2018). These non-invasive multimedia techniques avoid the interference of Figure 1. A non-exhaustive overview of the most frequently used methods for recording plant–frugivore interactions. Sampling methods are classified according to the seed dispersal stage in which they are most frequently applied: ‘visitation’, ‘transport’ and ‘deposition’. 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 Table 1. A summary of sampling methods used in plant–frugivore interaction studies. Including information about: seed dispersal stage, sampling effort, costs, detectability, replicability, additional information that can be obtained (apart from the interaction per se), units of measurement and reference studies illustrating the usage of each method. See the Supporting information for the full reference list of the example studies. Method Stage Sampling effort Cost Detectability Replicability Additional information Units Example studies Direct observations Visitation Requires long time in the field. Low. High cost in sampling time. Medium–low (human presence can affect significantly). Limited – affected by the expertise of the observer. Foraging behaviour, fruit handling behaviour. Frequency (e.g. visits/h and/or feeding rate) Total count 1, 2, 3 Spot censuses Visitation Requires long time in the field. Low. High cost in sampling time. Medium-low (human presence can affect significantly). Limited – affected by the expertise of the observer. Foraging behaviour. Frequency (e.g. interactions recorded/area/h) Total count 4, 5 ,6 Animal followings Visitation Requires long time in the field. Low. High cost in sampling time. Medium-low (human presence can affect significantly). Limited – affected by the expertise of the observer. Foraging behaviour, fruit handling behaviour. Frequency (e.g. interactions/ time, fruits/visit) Total count 27 Multimedia (e.g. phototrapping ) Visitation Low in the field but remarkable processing video efforts. Expensive at first (big inversion, then low - only batteries). High for largebodied animals. Low for small or fast animals. Medium-high – can be challenging for high canopy plants. Behaviour, n of fruits, feeding time, etc. Frequency (e.g. visits/h). Total count. 7, 8 Footprint traps. Bill and teeth marks Visitation Low. Low in material. High cost in sampling time. Medium, useful for interactions with well-known signals. Medium. Highly dependent on substrate type or in species signals. Animal body size, traits of discarded fruits. Frequency: visits/ time. Total counts: Number of contacted fruits. 9, 10 Fruit removal in the plant Visitation Low-mid: problematic if huge crop sizes. Low in material. High cost in sampling time. High with mediumlow crop sizes. Subsampling could be useful. Very systemdependent (e.g. unique seed disperser or species with different temporal activity patterns). Temporal activity patterns. Total counts: Fruit removal metrics. 11, 12 Offerings Visitation Low-mid: problematic if low crop size. Low in material. High cost in sampling time. Low specificity. Medium-High. Feeding preferences. Frequency: visits/ time. Total counts: Fruit removal metrics. 13, 7 Stomach content (after dead) Transport High, needs large samples. Bio-ethical considerations. Low in material. Medium-high cost in sampling time and processing. High. Low – depends on sample availability. Animal phenotypic traits. Fruit preferences. Frequency: seeds/ sample. Total counts: Number of fruits/seeds consumed. 14, 15 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5 Method Stage Sampling effort Cost Detectability Replicability Additional information Units Example studies Mist netting, traps Transport High-may need a long time to capture sufficientlyrepresentative sample. Mid-high. Low – very selective method, elusive animals are hard to capture. Mid-low. Animal phenotypic traits. Supplementary diet resources. Frequency: seeds/ bird Number of seeds per stomach content or fruits consumed. 16, 17 Fecal samples or regurgitated/spat seeds (in the field) Deposition High, requires field work in addition to sample treatment and identification. Low in material. High cost in sampling time and processing. Medium. It is difficult to detect and identify samples of rare species. High. Micro-habitat deposition. Other food resources. Precise spatial reference. Fruit preferences and handling. Seed shadow metrics. Frequency of seeds per faecal sample. Faeces or seeds per transect. No. fruits consumed. 18, 19 DNA-based molecular techniques (e.g. barcoding) Deposition Mid-Relatively short time in the field. High both in materials and processing time. High for rare species Low for species not adequately sampled in seed traps. High-based on protocol. Micro-habitat deposition. Other food resources. Precise spatial reference. Fruit preferences. Interaction frequency. Seed shadow metrics. Frequency of seeds per faecal sample. No. dispersed seeds. 20, 21 Stable isotopes Transport– deposition High – may need a long time to capture sufficientlyrepresentative sample. High. Limited, usually not species-specific detection. High. Possibilities for large-scale analysis. Dispersal distances. Interaction frequency. Presence of plant material in diet. 22, 23 Interviews Visitation– transport– deposition Low. Low cost in terms of material. High cost in time for interviews. Medium–low (human presence can affect significantlysometimes difficult to identify at species level). Limited-Low. (High areadependent effect). Behaviour, no. of fruits, feeding time, long-term trends. Number of positive responses for total interviews (total count). 24 Bibliographic review Visitation– transport– deposition Low, no field. Hard review and researchers contact work. Limited material. High cost in time. High, M&M dependent. High. Behaviour, no. of fruits, feeding time, frugivore community and plant resources availability. Specific units of each study. Number of studies. Total interactions for all sources. 25, 26 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 the observer with the animal in the field, allow continuous sampling over day and night and extended periods of time, and enable simultaneous monitoring over large study areas, thus increasing the probability of detection of rare interactions and improving the description of interactions distribution. Other indirect methods such as footprint traps allow to identify the species of animal that visits the plant (Jácome- Flores et al. 2020), or offerings (Garrote et al. 2018) that allow to quantify frugivory rates, can also be very useful, since they do not require a continued presence nor entail a high economic cost. Bill and teeth marks are signals that can be used to infer interactions too (Alves-Costa and Lopes 2001). An alternative indirect method in this phase is the estimation of the fruit removal caused by frugivores by counting the plant crop size over consecutive periods of time. This method becomes useful for plants with one exclusive frugivore (i.e. exclusive frugivory on islands, Hansen and Traveset 2012; or cases of double mutualism, Hansen and Mueller 2009, Gomes et al. 2013) as well as to discern between guilds of daytime and night-time frugivores (Palmeirim et al. 1989, Korine et al. 2000). Methods targeting ‘transport’ Methodologies used during the ‘transport’ stage are typically those where the animal is intercepted by means of capture, before any kind of fruit or seed release or deposition has naturally taken place (Fig. 1, Table 1). The capture methods depend on the target species, for example the mist nets are the most commonly used for medium-small size birds and bats (Herrera 1984a, Costa et al. 2020). Live traps are used for mammals (Genrich et al. 2017), and for fish there are also capture methods that allow obtaining the stomach content of the captured animals (Weiss et al 2016). Other sources of information include the stomach contents of animals after death from directed hunting (Remsen et al 1993), fishing (Galetti et al. 2008, Correa et al. 2015) or roadkills (Vaz et al. 2012). Interactions can be quantified on the basis of the number of seeds of different species found in the feces or in their stomach contents, properly accounting for pulp and/or seed remains due to potential biases generated by differential gut treatment (Oliveira et al. 2002; Supporting information). Methods targeting ‘deposition’ Methods targeting the ‘deposition’ stage (Fig. 1, Table 1) are used when the seed has reached its final destination by means of defecation, regurgitation, spitting, scatter-hoard- ing, discarding or unfortunate drop. In this case, sampling is directed towards the final seed destination (except when secondary or subsequent dispersal events are involved, i.e. recaching), typically in scats or droppings. Collection of fecal samples or regurgitated seeds can be carried out in different ways. For example, using transects or established areas to collect samples in the field has been widely used to study seed dispersal by mammals (González-Varo et al. 2013, Perea et al. 2013). In the case of primates, continuous monitoring of individuals can prove to be useful (Gestich et al. 2019). For bird dispersal, the use of seed traps is more common, since it greatly facilitates sample detection and can limit bias effects such as post-dispersal predation or secondary seed dispersal (Jordano et al. 2007). Direct identification of frugivores by the shape and size of the feces is possible for some carnivore species (Guitián and Munilla 2010). Individual tracking or identification of other frugivore droppings can be challenging, such as for reptiles, birds or bats; fortunately, new molecular techniques such as DNA-barcoding or metabarcoding offer a great potential to solve this problem. DNA-barcoding methods, allow the identification of the frugivorous species from the genetic material (animal origin) present on the seeds after their dispersal, matching the sequences obtained with reference sequences deposited in the BarCode of Life databank (Hebert et al. 2004, Kress et al. 2005, González-Varo et al. 2017). More and more studies are using this method, which promotes the expansion of species with available reference sequences and the optimization and adjustment of the protocol. Conversely, if we are interested in identifying the plant species consumed by a frugivore over a period of time, DNA metabarcoding techniques would be the ideal option, however see Tercel et al. (2021) for a discussion on potential limitations of this method. This molecular approach allows the simultaneous identification of multiple taxa from a single frugivore scat containing a mixture of DNAs by means of high-through- put sequencing of a carefully selected parts of the genome, a technique widely used in plant–herbivore interaction studies (Evans et al. 2016, Kartzinel et al. 2019). Lastly, stable-isotopes analyses can also be useful in frugivory studies, although with less resolution than with other techniques (Galetti et al. 2016). This approach is based on the premise that there is a relationship between stable isotopic compositions of consumer tissues and the stable isotopic compositions of the diet (DeNiro and Epstein 1978, 1981). The stable isotope technique is only useful in situations where two isotopically distinct dietary sources are available for frugivorous species (i.e. relative contributions of C3, and C4, plant-based proteins to avian diets, see Hobson and Clark 1992), although it may not be useful for describing interaction patterns across multiple partners. Alternative methods There are other approaches to compile interaction data, such as bibliographic searches, image repositories, interviews or word-of-mouth (Koike and Masaki 2008). These interaction records do not normally come with specific information on the moment of the seed dispersal process, therefore it is not possible to assess a specific interaction detection moment. For example, for some specific studies like seed dispersal systems in remote areas, conducting interviews with native inhabitants can be a precious source of information (Cámara-Leret et al. 2019), due to their close relationship with the natural environment or even their use of fleshy fruits. Another example of these compilation methods could be citizen science studies 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 7 (Bath-Rosenfeld 2019) or image repositories (Gonçalves dos Santos et al. 2019). Likewise, thorough bibliographic review in search of interaction data can provide a useful method for more general reviews or greater scope studies (Bufalo et al. 2016, Bello et al. 2017). These data gathering strategies can be very powerful, yet they come with some limitations, such as the information obtained will come from different methodologies and the sampling effort or precise georeferencing may be difficult or impossible to establish. Complementary information obtained with different methods Different stages of the plant–frugivore interaction process (Fig. 1) will provide varied and valuable information. The first part of the dispersal process (‘visitation’) is the only stage where we are able to observe both partners together. The source plant will be exclusively present during this phase, leaving a progeny in the form of a seed, to be present in later stages. This enables us to get data on feeding rates (e.g. fruits per visit, visit length), handling damage to fruits and seeds and fruit foraging behavior (Herrera and Jordano 1981, Moermond and Denslow 1985, Levey 1987, Snow and Snow 1988, Jordano and Schupp 2000). In addition, we may collect valuable information about intrinsic and extrinsic attributes of the mother plant (e.g. crop size, fruit traits, conspecific neighbourhood densities), that would not be possible otherwise (Sallabanks 1993, Miguel et al. 2018). Methods targeting the second stage (‘transport’) can be very useful for zoocentric studies, since they provide valuable information on dispersing animals. During this phase individual identification and marking is possible, as well as, we can gather additional data on animal body condition, morphological traits or even measurements of gut passage time (Herrera 1984b, Remsen et al. 1993). Radio trackers can also be settled in captured animals to study dispersal distances (Uriarte et al. 2011). This type of complementary data related to animal vectors and their behavior, allows us to better understand how and why the interactions we detect are taking place, as well as to be able to project and model the consequences of their dispersal (Nathan et al. 2012). Sampling carried out during the last seed dispersal stage (‘deposition’) can be suitable for plant demographic studies (Howe 1990), or animal habitat use, occupation or home range studies (Gestich et al. 2019). Maternal genetic correlates, such as relatedness between seeds, can be obtained through molecular techniques (García et al. 2009) and can help disentangling spatial genetic patterns of plant growth. Methods targeting seed deposition also provide important evidence on dispersal distance and can help identify longdistance dispersal (LDD) events (Nathan et al. 2012), with recent extensions based on extreme events theory allowing the exploration of very long-distance events (García and Borda-de-Água 2017). Combining sampling techniques can often increase the complementary information available (Schlautmann et al. 2021). The combination of methods with different focus of study can be useful to acquire more in-depth knowledge about interactions outcome (i.e. combining phytocentric and zoocentric methods). Sampling methods: constraints, potential limitations and sampling effort By definition, no sample is complete; a key aspect is to evaluate how far from completeness we are when analyzing a specific system with a specific sampling method. Different methods (Fig. 1, Table 1) are subjected to different constraints (e.g. logistic, temporal, accessibility, economic cost or technical difficulty) and these may differentially affect sampling completeness. Having a robust sampling design is important. When monitoring comprises several individuals, species or areas, the sampling effort should be adjusted and its adequacy explicitly evaluated (e.g. with accumulation methods). Otherwise, a posteriori corrections need to be incorporated to account for unequal sampling effort (Jordano 2016, Vizentin-Bugoni et al. 2016). Another aspect to consider is the potential bias derived from each sampling method, mostly arising from detectability biases (Schlautmann et al. 2021). Costs can be evaluated in terms of time, necessary expert workforce, economic expenses, material or logistics. Once the samples are collected, variation exists in terms of processing costs. A tradeoff between collection and processing costs emerges for different monitoring interaction techniques (Supporting information). Genetic or high-tech methods such as DNA-barcoding or camera traps are economically costly but they can reduce the laborious time spent in the field. While these methods can save time in the field, they frequently impose longer processing times for robust identification of animal visitors or during laboratory work; however, recent advances in automatic detection may contribute to alleviate this issue (Norouzzadeh et al. 2018). Given the above constraints and limitations, sampling effort eventually becomes limiting for obtaining an adequate completeness of the data. Interaction accumulation curves (IAC) (Fig. 2) provide an excellent tool to estimate the sampling completeness of a study and its robustness (Jordano 2016, Macgregor et al. 2017, Mata et al 2019). This method is a simple reformulation of the species accumulation curves (SAC) method (Gotelli and Colwell 2001, Chao et al. 2014) that plots the cumulative number of unique pairwise interactions recorded as a function of sampling effort (Jordano 2016). Completeness can be estimated as the percentage of interaction richness detected with our sampling, where the observed interactions are divided by the total number of estimated interactions with Chao2 and multiplied by 100 (Chacoff et al. 2012). Sampling effort can be measured from different perspectives: it may represent the time spent recording or identifying interactions (Fig. 2A–B), as well as number of samples collected (Fig. 2C–D) or the number of sites sampled. This approach provides an estimation of how many distinct 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 pairwise interactions, among the possible ones that can be recorded in the study area, are actually recorded. Different sampling methods will saturate their accumulation curve faster than others, approaching asymptotic sample completeness to variable degrees. Most recent studies of plant–frugivore interactions report sampling completeness in some way (Olesen et al. 2011, Bello et al. 2017, Acosta-Rojas et al. 2019, Costa et al. 2020). Combining data obtained with different methodological approaches Combining data allows overcoming the limitations of each method and obtaining a more accurate and complete representation of the interaction network (Bosch et al. 2009). The problem of data combination is central in frugivory studies, for example in analyses of complex networks aiming to get the maximum information from diverse sources to obtain a robust estimation of the interactions present. In this section we describe five different approaches to merge interaction data coming from different methodologies. To illustrate the data merging options we will consider, as an example, two matrices of pairwise interactions between a set of frugivore species and their food plants, assumed to result from different sampling approaches (Fig. 3). Interactions are tallied and summarized as adjacency matrices, with rows representing animal species and columns indicating plant species, so that matrix elements aij can represent estimates of the presence/absence (i.e. qualitative) or interaction strength (i.e. Visitation stage Dissemination stage Direct Indirect 0200 400600 80010001200 51015 Camera traps Cameras working time in days 0200 400600 8001000 5 10 15 DNA−barcoding Number of samples analyzed 0 500 1000 1500 020406080100 120 Avian mist−netting Number of birds captured Interaction richness Interaction richness 020406080 100 120 140 0 10 20 30 Focal observations Observation time (h/plant) Interaction richness Interaction richness (A) (B) (C) (D) Figure 2. Examples of interaction accumulation curves (IAC), where the number of unique pairwise interactions (y-axis) accumulates as the sampling effort increases (x-axis). Each plot represents a different field sampling methodology with different sampling effort associated (x-axis). (A) Focal Observations: frugivore visits to Cecropia glaziovii individual plants; where the sampling effort is represented by the number of individual trees observed. (B) Camera traps: animal interactions with Juniperus phoenicea individual plants, where the sampling effort is represented by the number of camera-days. (C) Mist-netting: plant–frugivore interactions at community level in a Mediterranean shrubland, where the sampling effort is represented by the number of samples analyzed from captured birds. (D) DNA-barcoding: frugivore interactions with Pistacia lentiscus individual plants, where the sampling effort is represented by the number of fecal samples analyzed. 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 9 quantitative) between animal species i and plant species j (Bascompte and Jordano 2014). Qualitative combination (QC) For all those cases where the characteristics of the datasets are hardly comparable, or if they just refer to presence/absence of the interaction (0–1), a qualitative combination of matrices (QC, Fig. 3) may be the most conservative option. This straightforward approach maximizes the number of pairwise interactions recorded, taking advantage of the full detectability potential of both sampling methods. Although qualitative matrices can be useful when describing frugivory assemblages (Bascompte and Jordano 2014, Almeida et al. 2018), quantitative information better describes the complexity of the structure of natural systems (Banasek-Richter et al. 2004, Dormann et al. 2009). Quantitative sum (QS) The simplest way to merge two weighted matrices without losing information, is to make a direct sum (QS, Fig. 3) of both datasets (Timóteo et al. 2018). This merging approach can be useful to combine data with equivalent sampling efforts whenever an absolute sum of records can be achieved without sacrificing biological interpretation. Despite incorporating more detailed information than the previous approach, it also has important limitations. Merging datasets that differ greatly in their measurement units, associated sampling efforts or spatio-temporal scales may yield unreliable results (Miranda et al. 2019) with this merging method. Sampling effort standardization (SES) Having detailed knowledge of the sampling effort associated with a given interaction survey (e.g. time, area, number of individuals sampled) allows using a more realistic and reliable standardization method, a sampling effort standardization (SES, Fig. 3). In order to conduct the data combination, both datasets need to be referred to the same ‘currency’ of interaction or unit, controlling for the sampling effort (e.g. visitation frequency in phytocentric studies, see Simmons et al. 2018, or ingestion rates in zoocentric studies). Once both matrices are standardized to a common ground, one could merge values by using the mean (option shown in Fig. 3) or the highest value recorded for each pairwise interaction. Averaging values can be problematic if the detectability of specific interactions differs significantly among techniques, as it may downplay or overestimate the weight of some interactions. On the other hand, selecting the maximum value for each interaction tries to harness the highest number of interactions observed but may also produce upward-biased values with some methods. This approach is appropriate for methodologies that share the same focus of study (i.e. only zoocentric or only phytocentric), as it can be challenging to find a common reference unit between a plant-focused study and an animal-focused one. Grand total standardization (GTS) When sampling techniques are very different and sampling effort correction cannot be applied, either because it is unreliable or not available, an option to collapse information is standardizing by the total number of interactions recorded. This approach, which we refer to as grand total standardization Figure 3. Illustrative example showing five merging methods for interaction data matrices considered in this study: qualitative combination (QC), quantitative sum (QS), sampling effort standardization (SES), grand total standardization (GTS) and min–max scaling (MMS). Matrices show the result of merging the simulated datasets A and B. For the SES approach we include sampling effort information for a simulated phytocentric study: area of the plant sampled and observation time on each plant. 16000706, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/oik.08379 by Universidad De Sevilla, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 Alves-Costa, C. P. and Lopes, A. V. 2001. Using artificial fruits to evaluate fruit selection by birds in the field. – Biotropica 33: 713–717. 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