Factors Affecting the Distribution of Pine Pitch Canker in Northern Spain

Article Factors Affecting the Distribution of Pine Pitch Canker in Northern Spain Lior Blank 1,* , Jorge Martín-García2,3 , Diana Bezos 3,4, Anna Maria Vettraino 5, Helena Krasnov 1, JoséM. Lomba 3,4, Mercedes Fernández 3,6 and Julio J. Diez 3,4 1Department of Plant Pathology and Weed Research, ARO, Volcani Center, Bet Dagan 50250, Israel; [email protected] 2Department of Biology, CESAM (Centre for Environmental and Marine Studies), University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal; jor[email protected] 3 Sustainable Forest Management Research Institute, University of Valladolid—INIA, Avenida de Madrid 44, 34071 Palencia, Spain; [email protected] (D.B.); [email protected] (J.M.L.); [email protected] (M.F.); [email protected] (J.J.D.) 4Department of Plant Production and Forest Resources, University of Valladolid, Avenida de Madrid 44, 34071 Palencia, Spain 5Department for Innovation in Biological, Agro-food and Forest systems (DIBAF) University of Tuscia, Via San Camillo de Lellis snc, 01100 Viterbo, Italy; [email protected] 6 Department of Agroforestry Sciences, University of Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain *Correspondence: [email protected].il; Tel.: +972-(3)9683581 Received: 5 February 2019; Accepted: 19 March 2019; Published: 2 April 2019   Abstract: Fusarium circinatum is the causal agent of pine pitch canker disease (PPC), affecting Pinus species and other conifers (i.e., Pseudotsuga menziesii (Mirb.) Franco.), forming resinous cankers on the main stem and branches and causing dieback in the terminal guide. This pathogen is spreading worldwide, causing economic losses by converting plantations into standing timber without any potential for future production. The disease was recently detected in Northern Spain in plantations of Pinus radiata and forest nurseries. The aim of the work reported here was to study the role of climatic and topographic variables, soil properties, and stand characteristics on PPC. For this purpose, we surveyed 50 pine stands in Cantabria and quantified the percentage of trees showing three symptoms in each stand: canker, defoliation, and dieback. We investigated the predictive power of 30 variables using generalized linear models and hierarchical partitioning. Both approaches yielded similar results. We found that the three symptoms correlated with different explanatory variables. In addition, more trees exhibited cankers in the proximity of the coast and the Basque Country. Additionally, our results showed that low canopy cover is related to a high level of the dieback symptom. Overall, this study highlights the important variables affecting the distribution of PPC in Cantabria. Keywords: forest epidemiology; Fusarium circinatum; generalized linear models; hierarchical partitioning; Pinus radiata Forests 2019,10, 305; doi:10.3390/f10040305 www.mdpi.com/journal/forests Forests 2019,10, 305 2 of 16 1. Introduction Pine pitch canker (PPC) disease is caused by the fungus Fusarium circinatum [ 1 ] (Teleomorph =Giberella circinata), a regulated pathogen under the EU legislations [ 2 ]. PPC is wide spread worldwide [ 3 ]. The disease was first reported in North Carolina (USA) [ 4 ], but since then it was also observed in California (USA) [ 5 ], Haiti [ 6 ], Chile [ 7 ], South Africa [ 8 ], Japan [ 9 ], Mexico [ 10 ], Korea [ 11 ], Uruguay [ 12 ], Colombia [ 13 ], and, more recently, Brazil [ 14 ]. In Europe, the first report was in Spain [ 15 ] in 2005, and the pathogen has also been reported in France [ 16 ], Italy [ 17 ], and Portugal [ 18 ]. In Italy and France it is now considered eradicated, whereas in Spain and Portugal the disease is established in the forests. Fusarium circinatum has been found to be pathogenic to over 60 pine species and also to Douglas fir (Pseudotsuga menziesii (Mirb.) Franco.), both in native and non-native forests [ 3 , 19 – 21 ]. Of these species, Monterey pine (Pinus radiata D. Don) is considered to be the most susceptible [ 22 ]. Pinus radiata, a native species to California (USA), Guadalupe, and Cedros Islands (Mexico), was introduced in the Basque Country (Spain) during the first half of the nineteenth century for commercial purposes because the climatic conditions are similar to its place of origin. Today, this is the most common exotic conifer in northern Spain covering an area of 200,000 ha [23]. Fusarium circinatum causes severe symptoms in mature trees, such as resin bleeding, deformations, and frequent formation of cankers on the trunk or thicker branches. In the crown, symptoms include defoliation, dieback, and presence of red shoots [ 3 ] (Figure 1). The increase in the resin production is due to the increment on the number of traumatic resin ducts (TRDs); this could benefit F.circinatum, since epithelial cells surrounding the TRDs have starch that the fungus uses for feeding [ 24 ]. The fungus can penetrate into the xylem, interrupting the sap flow and girdling the tree or big branches. This girdling can lead to tree or branch disruption due to wind or storms [ 4 ]. The fungus also causes damping off in seedlings, leading to mortality rates of up to 100% [ 20 ]. Consequently, this pathogen is considered a threat to pine plantations and wood industry productivity throughout the world. Fusarium circinatum can be naturally disseminated through spores that can be dispersed passively by wind, rain, or different vectors, such as insects [ 25 – 27 ]. However, infection by spores will usually be effective only in open wounds, where the spores can penetrate [ 28 , 29 ]. Generally, injuries are the result of extreme weather conditions (e.g., hail, wind damage, etc.) [ 30 ], insects (wood borers), or mechanical injuries [ 31 ]. On the other hand, F. circinatum can also be spread by human actions, i.e., trade of infected seeds, asymptomatic seedlings and plant products, infected substrates, and tools/machinery. According to the EU Plant Health Directive (Directive 2016/2031, in substitution of Directive 2000/29/EC;), Pinus spp. cannot be imported as plants for planting, and pine wood and bark should be properly treated. However, pests continue to be intercepted at the EU border on pine tissues (Europhyt database). Eschen et al. (2015) [ 32 ] showed that the standard of phytosanitary inspections at the EU border is not homogeneous. Thus, the risk of introductions of F. circinatum is still present. Once the pathogen is present in the forest, demarcated areas are delineated to eliminate infected host material and avoid their movement. However, despite environmentally-friendly methods for control [ 33 ], sanitation measurements, and a ban on planting susceptible species (Pinus spp. and Pseudotsuga menziesii) in infected areas (e.g., Spanish Royal Decree 637/2006 and 65/2010), PPC disease is very difficult to eradicate [2]. It is well known that environmental stress [ 34 , 35 ], physiological state of the host [ 36 , 37 ], and forest management [ 31 , 38 , 39 ] influence the rate of infection and incidence of F. circinatum and its pattern of spread. We hypothesized that abiotic factors and forest management play a key role in PPC disease in Spain. The aim of the work reported here was to study the role of climatic and topographic variables, soil properties, and stand characteristics on PPC. Forests 2019,10, 305 3 of 16 Forests 2019, 10 FOR PEER REVIEW 3 Figure 1. Pine pitch canker (PPC) symptoms of (a) cankers, (b) defoliation, and (c) dieback. 2. Materials and Methods 2.1. Site Description and Sampling Procedure The study was carried out in the Cantabria province of Northern Spain. This area is west of the Basque Country and east of the province of Asturias (Figure 2). The Cantabrian Sea borders the north of the province, while the Castilla and León regions border the south. Figure 1. Pine pitch canker (PPC) symptoms of (a) cankers, (b) defoliation, and (c) dieback. 2. Materials and Methods 2.1. Site Description and Sampling Procedure The study was carried out in the Cantabria province of Northern Spain. This area is west of the Basque Country and east of the province of Asturias (Figure 2). The Cantabrian Sea borders the north of the province, while the Castilla and León regions border the south. Forests 2019,10, 305 4 of 16 Forests 2019, 10 FOR PEER REVIEW 4 The ecological and environmental conditions of this area are very conducive to the development of Monterey pine and also PPC: elevation ranges between 0–1000 m asl, warm temperatures (10–14 °C annual average temperature, Atlantic climate) and frequent precipitation (700–2400 mm per year). Figure 2. Map of Cantabria (Spain) (lower map) and the location of the surveyed sites in Cantabria overlaid on an elevation map (upper map). A total of 50 plots were selected in areas where the disease was previously detected in order to represent a wide range of environmental and climatic conditions (Figure 2). A minimum distance of 500 m between plots was established. Dendrometric and forest health variables were assessed in 25 trees per plot (i.e., a total of 1250 trees), located near the plot’s center. In addition, climatic, soil, topography, and stand characteristic variables of each plot were measured. Forest health was evaluated by visual assessment of crown and stem conditions following the ICP Forest methodology [40]. We quantified the percentage of trees showing three symptoms in each stand: canker, defoliation, and dieback. At the same time, the diameter at breast height, total height, and canopy cover were measured for all trees. The following stand characteristic variables were quantified: canopy cover (Canopy), mean trunk diameter at breast height (DBH), stand age (Age), trunk perimeter (Perimeter), and the tree’s mean height (Height) (Table 1). Five soil samples were collected from the upper 30 cm soil layer in each stand. The first soil sample was taken from the middle of the plot and the rest of the samples were taken two meters Figure 2. Map of Cantabria (Spain) ( lower map) and the location of the surveyed sites in Cantabria overlaid on an elevation map (upper map). The ecological and environmental conditions of this area are very conducive to the development of Monterey pine and also PPC: elevation ranges between 0–1000 m asl, warm temperatures (10–14 ◦ C annual average temperature, Atlantic climate) and frequent precipitation (700–2400 mm per year). A total of 50 plots were selected in areas where the disease was previously detected in order to represent a wide range of environmental and climatic conditions (Figure 2). A minimum distance of 500 m between plots was established. Dendrometric and forest health variables were assessed in 25 trees per plot (i.e., a total of 1250 trees), located near the plot’s center. In addition, climatic, soil, topography, and stand characteristic variables of each plot were measured. Forest health was evaluated by visual assessment of crown and stem conditions following the ICP Forest methodology [ 40 ]. We quantified the percentage of trees showing three symptoms in each stand: canker, defoliation, and dieback. At the same time, the diameter at breast height, total height, and canopy cover were measured for all trees. The following stand characteristic variables were quantified: canopy cover (Canopy), mean trunk diameter at breast height (DBH), stand age (Age), trunk perimeter (Perimeter), and the tree’s mean height (Height) (Table 1). Forests 2019,10, 305 5 of 16 Table 1. Descriptive statistics, across all 50 plots, of the dependent (pathological symptoms) and independent variables used in this study. The proportion of each of the pathological symptoms represents the number of sampled trees out of the 25 in each plot that showed each symptom. Variables Abbreviation Units Average Min Max Pathology Cankers Canker proportion 0.20 0.00 0.64 Defoliation Defoliation proportion 0.14 0.00 0.66 Dieback Dieback proportion 0.16 0.00 0.48 Climate Average annual precipitation Precipitation mm 1232 702 1735 Average annual maximal temperature Tmax ◦C 18.3 17.0 21.0 Average annual minimal temperature Tmin ◦C 7.7 6.0 9.0 Average summer temperature Tm_sum ◦C 18.6 17.4 19.8 Average winter temperature Tm_win ◦C 8.66 7.2 10.2 Average summer precipitation Psum mm 202.1 82.4 251.6 Average winter precipitation Pwin mm 355 165.8 453.3 Frost period Frost Number of months 5.3 3.0 7.0 Topography Slope Slope Degree 15.8 5.0 35.0 Elevation Elevation Meters asl 360.5 92.0 898.0 Distance from the eastern boarder of Cantabria distEast Km 19.3 2.9 39.5 Distance from the coast distCoast Km 19 2.0 39.0 Soil pH pH No units 4.6 3.8 6.6 Cationic exchange capacity Conductivity MS/cm 0.08 0.03 0.6 Coarse fragments CF G/100 gr 5.3 0.0 58.0 Percentage of sand Sand % 56.7 12.6 80.0 Percentage of silt Silt % 17.9 3.3 44.6 Percentage of clay Clay % 24.2 1.4 49.7 Organic matter OM G/100 gr 3.5 0.7 9.4 Potassium K G/100 gr 61.94 13.00 365.00 Phosphorus P Mg/kg 1.2 0.0 5.5 Calcium Ca Meq/100 gr 1.5 0.03 29.1 Magnesium Mg Meq/100 gr 0.25 0.03 0.84 C/N ratio CN No units 11.9 6.2 16.7 Nitrogen N G/100 gr 0.2 0.05 0.44 Stand characteristics Canopy cover Canopy % 44.3 17.7 81.4 Mean diameter DBH cm 25.1 13.8 51.5 Stand age Age years 22.6 5.0 56.0 Average height Height m 16.6 10.5 27.3 Average perimeter Perimeter cm 80.6 47.7 169.3 Five soil samples were collected from the upper 30 cm soil layer in each stand. The first soil sample was taken from the middle of the plot and the rest of the samples were taken two meters away from the first one. The samples were pooled and homogenized to produce one composite sample per plot (Table 1). Particle size distribution was determined by the Bouyoucos method (hydrometer method) (CF), and the ISSS (International Society of Soil Science) classification was applied (Sand,Silt, and Clay). The pH was determined potentiometrically with a pH meter in a soil solution (1:2.5, soil:water). Organic matter (OM) was determined by the K 2 Cr 2 O 7 method. Total N was determined by Kjeldahl digestion (N). Soil available P was extracted by the Olsen procedure and determined photometrically by the molybdenum blue method (P). Soil exchangeable cations (K, Ca 2+ and Mg 2+ ) were extracted with ammonium acetate and determined by atomic absorption/emission spectroscopy (K,Ca, and Mg, respectively). The cationic exchange capacity (Conductivity) was determined by Bascomb’s method [ 41 ]. The topographic and spatial variables studied were: Elevation (Elevation), slope (Slope), distance from the eastern border of Cantabria (distEast) and distance from the sea coast (distCoast) (Table 1). The climatic variables include: average annual precipitation (Precipitation), average annual minimum (Tmin) and maximum (Tmax) temperature, mean summer temperature (Tm_sum), mean winter temperature (Tm_win), mean summer precipitation (Psum), mean winter precipitation (Pwin), and the number of frost (Frost) months. The climatic data were obtained from the Digital Climatic Atlas of the Iberian Peninsula [ 42 ]. The maps are based on data from meteorological stations in the Iberian Peninsula. Precipitation values are calculated for at least twenty years and temperatures for at least fifteen years for the 1950–1999 period. Forests 2019,10, 305 6 of 16 2.2. Statistical Analysis 2.2.1. Spatial Autocorrelation Analyses Spatial analysis is used to demonstrate that there is no significant spatial autocorrelation at the given sampling scale, in which case classical statistical tests of hypothesis can be used. To assess whether spatial autocorrelation in model residuals could bias statistical testing [ 43 ], we calculated global Moran’s Iautocorrelation coefficient to estimate whether the occurrence of the disease exhibits a random spatial pattern. The autocorrelation calculates the Moran’s IIndex value and both the pvalue and Z score, evaluating the significance of the index [ 44 ]. The null hypothesis states that there is no spatial clustering of the values associated with the geographic features in the study area. This analysis was implemented using the tool Spatial Autocorrelation Global Moran’s Iin ArcMap10.5. 2.2.2. Univariate Analyses We used non-parametric Spearman correlations to analyze the degree of correlation between variables. We constructed a correlation matrix between all considered variables belonging to the same group, selecting the one with the higher explained variability and subsequently removing all variables that were highly correlated with it, keeping the maximal variance inflation factor (VIF) at 5.84 for the climatic variables and lower than 3 for the other three variable groups. Neter et al. (1989) [ 45 ] suggested that multicollinearity is considered severe when VIF > 10. Variance inflation factor was calculated in R using the function ‘vif’ from the package ‘usdm’ [ 46 ]. In total, 30 variables were evaluated (Table 1). The following variables were excluded from the multivariate analysis duo to collinearity and high VIF: Precipitation,DistEast,Sand,Ca,DBH, and OM. 2.2.3. Multivariate The 30 variables collected and quantified were classified into four groups of variables: climate, topography, soil, and stand characteristics. Prior to analysis, data normality was checked using the Shapiro–Wilk tests for normality (‘’Stats” package implemented in R). The datasets for Defoliation and Dieback were normally distributed without transformation, while Canker had to be log transformed. We used logistic regression in the framework of General Linear Models (GLMs). Multi-model inference based on Akaike’s information criterion (AIC) was used to rank the importance of variables [ 47 – 49 ]. We used the package ‘’glmulti” to facilitate multi-model inference based on all first-order combinations of the variables on each scale (128 models for the climate variables, 8 models for the topographic variables, 1024 models for the soil variables, and 16 models for the stand characteristic variables) [ 50 ]. Although comparing all possible models is not usually recommended for model selection [ 47 ], we decided not to formulate models based on previous knowledge of the PPC disease. As this is the first intensive work aiming to understand the factors affecting this disease in this part of the world, we did not want to constrain the models to previous findings stemming from works in different regions. The estimated coefficients related to each variable and their relative importance were evaluated using multi-model averages. The importance weight for a variable is the sum of Akaike weights of the models in which the variable was present. Model assumptions were verified, following Zuur and Ieno [ 51 ], by plotting Pearson residual versus fitted values (using the function ‘residualPlots’ in the package ‘car’ [ 52 ]) and space coordinates (using the function ‘spline.correlog’ in the ‘ncf’ package [ 53 ]). In order to better understand the independent contributions of each variable, we used hierarchical partitioning [ 54 , 55 ] in the package ‘Hier.part’ [ 56 ]. Statistical significances of the independent contributions of the variables were tested using a randomization with 500 repetitions by using the function ‘rand.HP’. We calculated R N2 values [ 57 ] using the package ‘fmsb’ [ 58 ]. All statistical analyses were carried out using R 3.1.0 [59]. Forests 2019,10, 305 7 of 16 3. Results The presence of the symptoms on trees was high across the study area and each of the symptoms was present in about 90% of the surveyed stands. Only one of the forest stands did not show any of the three studied symptoms (canker, defoliation, or dieback). According to the results of the spatial autocorrelation analysis, there was no significant spatial autocorrelation for the three dependent variables (Canker: Moran’s I= − 0.18, pvalue = 0.25; Defoliation: Moran’s I= 0.17, pvalue = 0.15; and Dieback: Moran’s I= 0.07, pvalue = 0.52). Dieback and Defoliation were correlated (r = 0.5; pvalue = 0.0002) (Table 2). The percentage of trees with Dieback was significantly correlated with Precipitation, Pwin, and Psum (r = − 0.36, − 0.35, and − 0.32; pvalue = 0.01, 0.013, and 0.025, respectively). More trees exhibited the Cankers near the sea (r = − 0.29; pvalue = 0.04) and in Eastern Cantabria (close to the Basque Country) compared to the areas in the west (near Asturias) (r = − 0.35; pvalue = 0.014) (Table 2). In addition, Defoliation was negatively correlated with Pwin and Psum (r = −0.3, −0.28; pvalue = 0.031, 0.047, respectively) (Table 2). Table 2. Spearman correlation coefficient between the independent variables and the three symptomatic variables. Significant correlations are in bold letters (pvalue < 0.05). Variables marked with * were removed from further analysis due to collinearity. Independante Variable Canker Defoliation Dieback Tm_sum −0.19 0.15 0.03 Tm_win 0.13 0.09 0.03 Psum 0.08 −0.28 −0.32 Pwin −0.14 −0.3 −0.35 Precipitation * −0.05 −0.27 −0.36 Tmax −0.08 0.26 0.25 Tmin 0.19 −0.03 0.13 Frost −0.11 0.09 0.02 Slope 0.11 −0.1 0.08 Elevation −0.1 −0.12 −0.01 DistEast * −0.35 0.09 −0.12 DistCoast −0.29 0.07 −0.12 pH 0.09 −0.02 0.08 Conductivity −0.07 0.02 0.17 CF 0 −0.14 0.18 Sand * −0.24 −0.06 −0.1 Silt 0.11 −0.06 0.05 Clay 0.12 0.14 0.09 OM * −0.16 −0.01 0.13 P−0.2 0.1 0.1 K 0.06 −0.09 0.02 Ca * −0.17 −0.17 0.04 Mg −0.07 −0.24 0.05 N−0.19 −0.07 0.13 CN 0.03 0.16 0.09 Canopy 0.03 −0.06 −0.28 DBH * 0.08 −0.07 0.06 Age 0 0.09 0.25 Perimeter 0.05 −0.15 −0.05 Height −0.01 −0.16 −0.13 Canker Defoliation 0.04 Dieback 0.13 0.5 The best models selected for each symptom in each of the four variable groups are shown in Supplementary Materials (Table S1). According to the validation process, these models did not show Forests 2019,10, 305 8 of 16 spatial correlation. In addition, we found that the relationship between Pearson residuals versus fitted explanatory variables showed no clear violations of the model assumptions (Table S2). Topographic variables explained the highest percentage of the variation for Canker compared to Defoliation, and Dieback (12%, 1.5%, and 4%) (Figure 3), while the stand characteristics explained 20% of the variation for Dieback and only 9% and ~1% for Defoliation and Canker, respectively (Figures 3–5). The variations of the three symptoms were equally explained by the climatic variables (~20%) (Figures 3–5). Soil variables explained more of the variation in cankers compared to the variation in Dieback and Defoliation (Figures 3–5). Forests 2019, 10 FOR PEER REVIEW 9 (a) (b) Figure 3. The relative importance (a) and the estimated coefficients (b) of variables estimated across all fitted GLM models using a multi-model average approach for the Canker symptom. We also indicate the percentage of deviance explained by each complete model. Figure 3. The relative importance ( a ) and the estimated coefficients ( b) of variables estimated across all fitted GLM models using a multi-model average approach for the Canker symptom. We also indicate the percentage of deviance explained by each complete model. Forests 2019,10, 305 9 of 16 Forests 2019, 10 FOR PEER REVIEW 10 (a) (b) Figure 4. The relative importance (a) and the estimated coefficients (b) of variables estimated across all fitted GLM models using a multi-model average approach for the Defoliation symptom. We also indicate the percentage of deviance explained by each complete model. Figure 4. The relative importance ( a ) and the estimated coefficients ( b ) of variables estimated across all fitted GLM models using a multi-model average approach for the Defoliation symptom. We also indicate the percentage of deviance explained by each complete model. Forests 2019,10, 305 16 of 16 55. 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