A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities

A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities

Agriculture, Ecosystems and Environment 288 (2020) 106694 Contents lists available at ScienceDirect Agriculture, Ecosystems and Environment journal ...

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Agriculture, Ecosystems and Environment 288 (2020) 106694

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities

T

Rui Morgadoa,*, Joana Santanab,c, Miguel Portob,c, Juan S. Sánchez-Oliverb,c, Luís Reinob,c, José M. Herrerad, Francisco Regoa, Pedro Bejab,c, Francisco Moreirab,c a

CEABN/InBIO, Centro de Ecologia Aplicada “Professor Baeta Neves”, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal b CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-601 Vairão, Portugal c CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal d Mediterranean Institute for Agriculture, Environment and Development, University of Évora, Casa Cordovil, 2nd Floor, R. Dom Augusto Eduardo Nunes 7, 7000-651 Évora, Portugal

ARTICLE INFO

ABSTRACT

Keywords: Agricultural intensification Farmland birds Joint species distribution modelling Mediterranean Olive orchards Species traits

Combining biodiversity conservation with the agricultural production needed to meet the rising world food demand is a global challenge. This is a case in point for olive farming in the Mediterranean region, where highyielding intensive and super-intensive orchards are fast expanding, often replacing biodiversity-rich but lowyielding traditional orchards. Here we test the hypothesis that production intensification reduces bird diversity within olive orchards, and that bird responses to such intensification are largely mediated by species traits. Breeding birds were counted in southern Portugal (2014–2016) at 78 orchards covering the entire intensification gradient. Using hierarchical joint species distribution modelling, we found that the intensification-level explained a large proportion of bird community variation (35%), while accounting for the significant contributions of orchard features (16%) and landscape context (16%). Species richness declined from traditional (29 species), through intensive (25), to super-intensive (20) orchards. The occurrence probability of many species also declined significantly from traditional to intensive (7 species; 22% of all species considered) and to super-intensive (14 species; 44%) orchards, while prevalence in more intensive orchards increased only for linnet and goldfinch (6%). Cavity-nester insectivores were the most affected functional group, declining steeply along the intensification gradient. Our results suggest that production intensification, and particularly the shift towards super-intensive systems, is reshaping and greatly simplifying breeding bird communities associated with olive orchards, which may become dominated by generalist granivores. This is a warning signal of widespread negative impacts on bird diversity that may be occurring due to olive farming intensification in the Mediterranean. Efforts are needed to reduce such impacts, which in the case of birds would require actions targeted primarily at cavity-nester insectivores.

1. Introduction While major increases in food production through agricultural intensification are generally deemed necessary to meet the demands of a fast-increasing global population, they also represent one of the strongest drivers of worldwide biodiversity declines (e.g., Godfray et al., 2010; Crist et al., 2017). Therefore, there are increasing efforts to find strategies reconciling food production and biodiversity conservation, even in relatively intensive production systems (Phalan et al., 2011;

Tscharntke et al., 2012). To achieve this goal, however, more information is needed on the patterns and mechanisms of biodiversity change in different farming systems along intensification gradients (Green et al., 2005). Olive farming is a major agricultural system in the Mediterranean region, where it occupies ca. 10 million hectares (FAO, 2018), and has significant environmental, cultural and socio-economic importance (Rey, 1993; Loumou and Giourga, 2003; Tartaglini et al., 2012; International Olive Council, 2018). Across this region, traditional olive

⁎ Corresponding author at: Centro de Ecologia Aplicada “Professor Baeta Neves” (CEABN), InBIO, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal. E-mail address: [email protected] (R. Morgado).

https://doi.org/10.1016/j.agee.2019.106694 Received 19 November 2018; Received in revised form 23 September 2019; Accepted 25 September 2019 0167-8809/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Location of olive orchards (n = 78), and respective intensification level (symbols), sampled across the Alentejo region (districts of Portalegre, Évora and Beja; dark grey), Southern Portugal.

cultivation generally involves low-yielding (< 2.5 tonnes ha−1) rainfed orchards, often characterised by old trees arranged at low densities, with high levels of human labour (e.g., pruning, harvest), low agrochemical inputs and little if any mechanisation, which are often embedded in complex landscape mosaics (Herrera et al., 2015; SánchezMartínez and Cabrera, 2015; Infante-Amate et al., 2016). However, in the last three decades there has been an accelerating trend towards the intensification of production, with the standard intensive systems involving high-yielding irrigated orchards (4.0–10.0 tonnes ha−1) designed for harvest with trunk-shakers, and with 200–450 trees ha−1(Tous et al., 2014; Silveira et al., 2018). More recently, super-high density irrigated hedgerow systems have been developed (hereafter super intensive orchards), using highly productive dwarf varieties and densities of 1,000–2,500 trees ha−1, which facilitate the use of continuous straddle mechanical harvesters and the achievement of very high yields (8.5–10.5 tonnes ha−1) within only 3–4 years after planting (Tous et al., 2014; Connor et al., 2014; Silveira et al., 2018). Besides changes in orchard structure and mechanisation levels, the intensification of olive production is associated with higher inputs of inorganic fertilisers and pesticides (Sánchez-Martínez and Cabrera, 2015; Infante-Amate et al., 2016; Tous et al., 2014; Russo et al., 2016). At present, the intensive and, particularly, the super intensive olive production systems are fast expanding in the Mediterranean region, either replacing traditional orchards or occupying areas formerly used to produce other crops (Herrera et al., 2015; SánchezMartínez and Cabrera, 2015; Infante-Amate et al., 2016).These changes have been pushed by EU Common Agricultural Policy (CAP) economic incentives (Duarte et al., 2006; Scheidel and Krausmann, 2011) and, more recently, by increases in olive oil demand associated to the global recognition of the Mediterranean diet (Neves and Pires, 2018). Although this production intensification process is contributing to

improve farmer’s incomes and the global competitiveness of the olive sector, it is raising increasing concerns on the sustainability of olive farming (Moreira et al., 2019). For instance, studies have shown negative effects of olive farming on soil and water (e.g., Gómez et al., 2011; Vanwalleghem et al., 2011), and some impacts on biodiversity have also been documented, including effects on bird communities. Within olive orchards, the few available studies suggest that bird species richness tends to decrease with increasing levels of intensification (Solomou and Sfougaris, 2015; Bouam et al., 2017), probably as a consequence of increased pesticide inputs (e.g., Solomou and Sfougaris, 2015), but also due to habitat changes such as increased tree density, reduced herbaceous vegetation cover, and loss of woody hedges (e.g., Muñoz-Cobo and Montesino, 2003; Santos and Cabral, 2004; CastroCaro et al., 2015; Rey et al., 2019). At landscape level, more intensive olive orchards tend to be much larger than traditional orchards, resulting in more homogeneous and simplified landscapes, with potential negative effects on biodiversity not only within orchards (Rey et al., 2019), but also on species typical of open farmland areas into which these systems are expanding (Santos and Cabral, 2004; Santana et al., 2017a, b; Santos et al., 2018). However, most studies have been developed at a local scale and had relatively small sample sizes (e.g., Bouam et al., 2017), and effects of the super intensive production system remains unstudied. Also, few studies have investigated whether bird responses to intensification are mediated by species traits (but see, e.g., Santos and Cabral, 2004; Castro-Caro et al., 2015; Bouam et al., 2017), thus making it difficult to generalize results beyond particular study settings and hindering a full understanding of the mechanisms driving changes in bird communities. This study investigates how breeding bird communities within olive orchards change along the intensity gradient of olive production systems, from traditional, through intensive, to super-intensive, while 2

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accounting for potentially confounding effects of orchard features and landscape context. We tested the following hypotheses: (1) intensification contributes to significant declines in bird species richness and the probability of occurrence of most individual species within olive orchards; and (2) species-traits related to diet and nesting requirements contribute to explain the observed changes in species richness and occurrence patterns within orchards, providing a basis to predict effects on bird communities in other regions. To test these hypotheses, we used a recent, hierarchical Bayesian joint species distribution modelling approach, which seeks for community-level patterns in how species respond to intensification and can relate such patterns to species-specific traits and phylogenetic associations (Ovaskainen et al., 2017). Results were used to highlight the effects of intensification on breeding bird communities within olive orchards, and to devise potential management actions contributing to minimize eventual impacts.

Tous et al., 2014; Russo et al., 2016). This categorisation was then confirmed quantitatively using a classification analysis based on structural and management data collected in the field (see Section 2.5.1). In the selection process we constrained orchards to be on different farms, thereby reducing eventual dependencies due to similarities in management and spatial autocorrelation. In the end, we selected an equal number of orchards per intensification category, which ranged from 3.2 ha to 1665.5 ha (mean 195.4 ± 315.6 ha SD), and their nearest neighbour distance ranged between 0.5 km and 19.2 km (mean 3.6 ± 3.1 km SD). 2.3. Bird counts Breeding birds were surveyed at 1 to 3 point-counts per orchard (mean 2.4 ± 0.7 SD, n = 191), depending on orchard size. Points were placed along the longest axis that crossed the interior of the olive orchard and were 100 m away from each other and at least 100 m away from the orchard edges. Counts were carried out from early April to early June in 2014 (n = 16 orchards), 2015 (40) and 2016 (22) (see Table S1 for details). Each orchard was sampled only once during the study period, but orchards in the three intensification levels were sampled each year (see Table S1 for details). Counts were carried out by a team of experienced ornithologists (RM, AV, RP, LR), within the first 4 h after sunrise and the last 2 h before sunset, avoiding hot weather, heavy or persistent rain or strong wind conditions. At each point, birds were identified and counted for 10 min within a 50 m-radius circle, recording all individual birds or flocks of each species seen or heard (Bibby et al., 1992). To avoid double counting, the observer surveying a given orchard carefully tracked any bird movement within or between point counts and tallied only the first encounter with each individual bird. This procedure was particularly important to minimise double counts of birds that can move widely and rapidly, such as corvids, partridges, doves and pigeons. Birds flying over but not landing were not counted, except in the case of song flights, such as those of larks. Raptors, birds with nocturnal habits (owls, nightjars and stone curlew Burhinus oedicnemus), aquatic birds and aerial feeders (swifts, swallows, bee-eater Merops apiaster) were also discarded from analysis, as this census method does not accurately estimate their occurrence. Categorisation to the genus level was made for crested and Thekla larks (Galerida cristata and G. theklae) due to difficulties in reliably identifying every individual in the field.

2. Material and methods 2.1. Study area The study was carried out in the Alentejo region of Southern Portugal, within an area of about 15,000 km2 (37°38′ - 39°01′ N, 7°02′ 8°16′ W, Fig. 1). The area is characterized by a Mediterranean climate with hot dry summers, mild winters, and > 75% of annual rainfall concentrated in October–March. The topography is flat or gently undulating (100 to 450 m a.s.l.) and the main land cover types include open agricultural areas (mostly dry or irrigated annual crops and cattle or sheep pastures), holm oak (Quercus rotundifolia) and cork oak (Quercus suber) woodlands, olive orchards and vineyards. Alentejo is by far the main olive oil producing region in Portugal, comprising ca. 50% of the country’s olive area (ca. 175,000 ha) and accounting for ca. 70% of total olive oil production (INE, 2018). It is also the Portuguese region with the steepest increase in area occupied with intensive and superintensive olive orchards, with at least ca. 45,000 ha planted in the last 2 decades (INE, 2011, 2017), either replacing less intensive orchards or expanding into areas formerly occupied by annual crops (Stoate et al., 2009; Ribeiro et al., 2014). Apart from the influence of more general policy and market drivers, the local process of intensification is also strongly linked to the construction of the Alqueva dam (Silveira et al., 2018), the Europe’s largest artificial lake, whose massive irrigation project made water available for irrigation of ca. 120,000 ha of land across the Alentejo since 2004.

2.4. Explanatory variables

2.2. Orchard selection

Olive orchards (n = 78) were characterized using a set of variables reflecting orchard features and landscape context (Table 1). Orchard features were characterised considering five variables describing orchard structure and management (olive tree density, height and DBH; orchard age; and use of irrigation), and one variable coding the presence of natural vegetation elements (Table 1). Tree density (DENS) was first estimated from satellite imagery (Google Earth, version 7.1.2.2041) and then confirmed in the field. Mean tree height (HEIGHT) and trunk diameter at breast height (DBH) were estimated from measurements taken on the 5 trees closer to each bird sampling point, using a hypsometer and a calliper, respectively. In smaller trees (e.g., super-intensive orchards), trunk diameter was measured at the height of first tree bifurcation. Orchard age (AGE) was assessed from enquiries to landowners or estimated by comparing aerial imagery from different periods (e.g., Google Earth and Bing images). The presence of drip irrigation (IRRIG) was noted in the field. The main natural vegetation elements represented in olive orchards were adult oak trees, and thus their presence (OAK) was recorded within 100 m of each sampling point from Google Earth and Bing images from 2014 to 2016, and then confirmed in the field. Landscape context was described from the proportional cover of the main land uses within 500m-radius buffers around the centre of each

A total of 78 sampling orchards were selected following a stratified random procedure designed to encompass the intensification gradient of olive farming in the region. As for most individual orchards it was virtually impossible to obtain detailed data on management intensity (e.g., application of agrochemicals and number of mechanical operations) and on productivity (average olive production per hectare and year), we used the structural classification of orchards in three categories reflecting traditional, intensive and super intensive production systems as a proxy of production intensity, as defined in the agronomic literature (e.g., Tous et al., 2014; Russo et al., 2016), and previously used in ecological studies (e.g., Matono et al., 2013; Herrera et al., 2015) and policy documents (e.g., European Commission, 2010). Structural categorisation was first made using aerial imagery (Google Earth, version 7.1.2.2041) and field surveys, considering the following orchard types: (i) traditional - orchards with trees planted at low density (< 150 trees ha−1), usually with old trees and no irrigation; (ii) intensive – irrigated orchards with olive trees planted at high density (200–450 trees ha−1) with regular spacing among trees; and (iii) super intensive – irrigated orchards with hedgerow-like plantation of dwarf olive tree varieties at very high densities (1,000–2,500 trees ha−1) (e.g., 3

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Table 1 Description and summary statistics (mean, range) of variables used to characterise the selected olive orchards (n = 78) at the farm and landscape levels. Variable (code) Orchard features Tree density (DENS) Tree height (HEIGHT) Tree DBH (DBH) Age (AGE) Irrigation (IRRIG) Oak Trees (OAK) Landscape context Open farmland (AGRIC) Woodland (WOOD) Olive (OLIV)

Units

Description

Mean (Min-Max)

trees ha−1 m cm years 1/0 1/0

Estimated olive tree density Mean olive tree height Mean olive tree diameter at breast height Time since planting Presence/absence of drip-irrigation system Presence/absence of adult oaks inside orchards

691.0 (40-1975) 3.3 (1.3-5.8) 15.6 (3.0-53.3) 28.0 (2-75) 0.7 (0-1) 0.4 (0-1)

% % %

Percentage cover by open agricultural area Percentage cover by woodland habitats Percentage cover by olive orchards

35.9 (1.0-87.3) 3.5 (0.0-36.6) 57.5 (3.9-85.2)

orchard sampling area. Land-use information was extracted in a Geographic Information System (GIS) from official land cover maps of Continental Portugal for 2015 (http://mapas.dgterritorio.pt/geoportal/ catalogo.html), and updated where necessary from field surveys. Land uses were classified in three dominant categories, which were found previously to affect farmland bird communities in the study area (e.g., Santana et al., 2017a, b; Herrera et al., 2018): (1) open agricultural land (AGRIC), including annual dry or irrigated annual crops (cereals, fodders and other), pastures and fallows; (2) woodland habitats (WOOD), including mainly cork and holm oak woodlands of varied tree density, but other woodlands (umbrella pine, eucalyptus) and riparian forests patches, and; (3) olive orchard areas (OLIV) (Table 1). We did not consider other categories (e.g., urban areas, vineyards), due to their very low representativeness in the 500m-radius buffers (< 0.6% average cover), thus making it difficult to quantify their effect on bird communities inhabiting olive orchards.

communities using a recently developed hierarchical Bayesian joint species distribution model (HMSC – Hierarchical Modelling of Species Communities; Ovaskainen et al., 2017). This modelling framework is more suited than simpler approaches to obtain a comprehensive understanding about the complex network of processes determining community assembly, by incorporating information not only on species occurrence patterns and environmental covariates, but also on species traits (or functional groups), phylogeny and on the hierarchical spatial structure of the study design (Ovaskainen et al., 2017). 2.5.2.1. Species occurrence patterns. Species occurrence patterns were summarized in a species x site matrix (Y matrix; Ovaskainen et al., 2017). Analysis were carried out using as sampling units the pointcounts (n = 191) rather than the olive orchards (n = 78), because there was some variation in the number of points across olive orchards and so they were not directly comparable. Due to the high prevalence of zeros in the bird data and to minimize potential inter-observer differences, we truncated the bird counts to presence-absence data, and thus the response variable was the vector of presence-absences of all recorded species across sampling points.

2.5. Data analysis 2.5.1. Orchard classification in intensification levels To confirm the a priori qualitative classification of intensification levels used to select sampling orchards, a quantitative categorisation was produced using the five variables describing orchard structure and management (DENS, HEIGHT, DBH, AGE, IRRIG; Table 1). This categorisation was based on the “farming system” approach, which assumes that each farming system is associated with specific agricultural practices and land-use patterns to which biodiversity components respond (e.g., Ribeiro et al., 2014, 2016). Using farming systems rather than the raw variables also avoids problems related to strong intercorrelations among variables due to orchard structure and management tending to be far more similar within than between systems, and it provides a closer link to market and policy drivers (Ribeiro et al., 2014, 2018). Categorisation was based on a Hierarchical Clustering on Principal Components analysis (HCPC, Husson et al., 2010), which summarized the information on orchard features and identified groups of orchards with similar management. HCPC included a first pre-processing step using Principal Components Analysis (PCA) for data summarizing and denoising. The set of principal components preserving at least 90% of the original variance was then subjected to a hierarchical clustering process using the Ward's agglomeration method (Husson et al., 2010). The partition obtained was then consolidated using the K-means algorithm for partitional clustering, setting as three the minimum number of clusters to create (Husson et al., 2010). The optimal cut level of the hierarchical tree and the final number of clusters were then calculated automatically by the HCPC function based on inertia gains between successive partitions (Husson et al., 2010). The PCA and HCPC functions were implemented using the package FactoMineR (Lê et al., 2008) for R (R Development Core Team, 2018).

2.5.2.2. Environmental covariates. The response variable (Y matrix) was related to an environmental matrix (X matrix; Ovaskainen et al., 2017) containing a variable with the intensification levels extracted from HCPC (INTENS), and variables describing orchard features and landscape context. As there was major redundancy between intensification categories and the set of variables describing orchard structure and management (see section 3.1), they could not be both included directly in the HMSC analysis. Therefore, to account for intracluster variability, we used a variable summarising the main gradient of residual variation in orchard features within each intensification level, rather than the raw variables describing overall variation across all orchards. For this, we considered the scores of the orchards in the first component extracted from the PCA carried out in the HCPC, and for each orchard we subtracted its score from the average score of all orchards pertaining to the same intensification level. This variable (DEVIAT) thus indicates how distant (positive or negative) each orchard is from the mean characteristics of its respective cluster, and thus interpretation of bird responses to DEVIAT needs to be made considering the loadings of the original variables on the first PC axis. The environmental X matrix also included the variable coding the presence of natural elements (OAK), and the AGRIC and WOOD landscape context variables. Land cover by olive orchards (OLIV) had to be discarded from analysis to avoid multicollinearity problems (Dormann et al., 2013), because it was almost perfectly specified by AGRIC and WOOD (r=-0.94). 2.5.2.3. Species traits. All bird species recorded in the study were categorised considering diet and nesting traits, which are expected to affect responses to olive farming intensification (e.g., Morris et al., 2005; Bas et al., 2008): (1) cavity-nesting insectivores, representing

2.5.2. Joint species distribution modelling We evaluated the effects of intensification on breeding bird 4

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insectivore species fully dependent on cavities to breed; (2) non-cavitynesting insectivore, representing the rest of the insectivore community; (3) tree-nesting granivores and; (4) ground-nesting granivores (Table S2). Nesting site was one of the traits considered because along the intensification gradient there is a reduction in the size and age of olive trees, making it likely a negative effect of intensification on cavity nesters (e.g., Grüebler et al., 2013). Diet was categorised assuming that intensification is associated with increasing pesticide applications to control pests, which may reduce arthropod abundance and thus negatively affect insectivore species (e.g., Hallmann et al., 2014). Information on traits was obtained from del Hoyo et al. (2018), Storchová and Hořák (2018) and personal observations in the study area. Species other than insectivores and granivores (e.g., omnivores such as Corvidae) were left unclassified in terms of traits. The final classification of bird species in trait classes was summarised in a species x trait matrix using a binary code (T matrix; Ovaskainen et al., 2017).

was assessed by looking at the posterior mean of the model phylogenetic correlation parameter q, which varies between 0, when these responses are randomly distributed with respect to phylogeny, and 1, when these are fully explained by their phylogenetic correlations. The model explanatory power was assessed using the Tjur's coefficient of discrimination (Tjur, 2009) following Ovaskainen et al. (2017). This coefficient was proposed as a goodness‐of‐fit statistic for logistic models and is defined as the difference between the averages of predicted values for presences and absences (Tjur, 2009). Tjur varies from one, when the model predicted probabilities of occurrence are always one in sites where the species is recorded and zero otherwise, through zero, when the average probabilities of occurrence are equal irrespective of species presence or absence, and minus one, when the probabilities of occurrence are always zero in sites where the species is recorded and one otherwise, though negative Tjur values rarely occur in practice. Care should be taken when interpreting Tjur, because its value can remain low even when there is complete separation between predicted presences and absences, as it is a measure of the effect size, and not a measure of the model discrimination ability. To account for this and provide a more complete model quality assessment, we also used the area under the ROC (Receiver Operating Characteristic) curve (AUC) to assess the model discrimination ability, i.e. the accuracy of model predictions regardless of the effect size (Elith et al., 2006). AUC values of 0.5 indicate that model predictions are no better than random, whereas values closer to 1 indicate the best performance of the model (Swets, 1988). Confidence intervals at 95% were calculated for AUC using the bootstrap approach implemented in the package pROC (Robin et al., 2011) for R (R Development Core Team, 2018), with 2000 replicates. When the confidence interval does not intersect 0.5 the model can be taken to perform significantly better than a random model. When AUC = 1 the confidence interval is null and should not be considered.

2.5.2.4. Phylogeny. Phylogeny was included in the analysis following Ovaskainen et al. (2017), because phylogenetic-related species are expected to show more similar trait and occurrence patterns than non-related ones (e.g., Harvey and Pagel, 1991; Wiens and Donoghue, 2004). Therefore, a species x species phylogenetic correlation matrix (C matrix; Ovaskainen et al., 2017) was included in the model to assess the phylogenetic structure of the residual variation in species environmental responses (after accounting for the effects of traits). The presence of a phylogenetic signal in the model thus indicates that residual variation is phylogenetically structured, which can be due to unmeasured traits influencing species responses to environmental covariates. The phylogenetic matrix was created from a phylogenetic tree (Fig. S1) produced using the approach and online data provided by Kumar et al. (2017). 2.5.2.5. Model building and assessment. The model was fitted using the HMSC package for R (HMSC-R, Ovaskainen et al., 2017; R Development Core Team, 2018), with a probit link function, assuming a Bernoulli distribution and the default priors described in Ovaskainen et al. (2017). The five environmental covariates were used as the model fixed effects. The variable describing intensification levels (INTENS) was coded as a factor variable, using the traditional level as reference. To normalize the distributions of variables, angular and logarithmic transformation were used on proportional (AGRIC and WOOD variables) and continuous data (DEVIAT), respectively. To account for the hierarchical spatial structure of our study design, we specified as random effects the point count (sampling unit) and orchard (sampling plot), using a latent factor approach (π matrix, Ovaskainen et al., 2017; Abrego et al., 2017). We also considered as random effects in the π matrix the sampling year (2014, 2015 or 2016) and season (early vs. late season) (see Table S1 for details). The phylogenetic correlation matrix was included in the model’s covariance structure following Abrego et al. (2017). We ran the model for 100,000 iterations with a burn-in of 50,000 and thinned the chains by a factor of 50 to produce 1,000 simulations for each parameter. Convergence was assessed by visually inspecting MCMC trace plots (Ovaskainen et al., 2017). The parametrized model was then used to: (a) partition the explained variation of species occurrence patterns into fixed (attributable to the environmental covariates) and random effects and; (b) predict the responses of different species and functional groups to olive farming intensification. Predicted mean species richness in each point was computed as the sum (over the species) of the species-specific occurrence probabilities. Differences in species richness between intensification levels were assessed after accounting for the potential effects of the remaining environmental covariates, by setting their values to the mean within each intensification level, and they were considered significant if the 95% credible intervals did not overlap each other. The strength of the phylogenetic signal on the residuals of the environmental responses of species (after accounting for the effects of traits)

3. Results 3.1. Orchard general characterization Overall, tree density in the studied orchards varied widely from 40 trees ha−1 to 1975 trees ha−1 (Table 1). Tree height and DBH were on average 3.3 m and 15.6 cm, respectively, with maximum values reaching 5.8 m and 53.3 cm (Table 1). The average orchard’s age was ca. 28 years, ranging from very young plantations (2 years old) to very old orchards (75 years). Irrigation was used in ca. 70% of the orchards, whereas oak trees were present near sampling points in ca. 40% (Table 1). The landscape around each orchard was dominated by olive orchards (ca. 58%) and open agricultural land (ca. 36%), followed by woodland (ca. 3.5%, Table 1). 3.2. Orchard classification The PCA extracted three principal components accounting for 96.6% of the original variance, which were used in the Hierarchical Clustering procedure (Table S3). The first PC accounted for a very large share of variation in orchard structure and management (79.4%), representing a gradient from younger orchards with high tree density and low tree size, largely corresponding to more intensive management regimes, to older orchards with lower tree densities and larger trees, largely corresponding to the more traditional orchards. The hierarchical clustering identified three well defined clusters, composed of 26 olive orchards each, matching exactly the a priori qualitative categorization used for orchard selection (details in Supplementary Table S4 and Figs. S2 and 3). Cluster 1 (hereafter referred to as traditional) included nonirrigated old orchards (mean 69.5 ± 5.1 years SD), composed of large trees (mean height 4.2 ± 0.6 m SD) planted at low densities (mean 82 ± 30 trees ha−1 SD). Cluster 2 (intensive) included irrigated orchards with intermediate ages, tree size and densities. Cluster 3 (super5

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intensive), included irrigated young orchards (mean 6.8 ± 2.1 years SD), composed of small trees (mean height 2.7 ± 0.5 m SD) planted at very high densities (mean 1704 ± 241 trees ha−1 SD) (Fig. S3; Table S4).

levels (Fig. S7 A). The prevalence of blue tit and short-toed treecreeper increased in orchards with comparatively older, taller and thicker trees, whereas greater short-toed lark Calandrella brachydactyla, black-eared wheatear and stonechat Saxicola rubicola showed an opposite pattern, with higher prevalence in more recently planted higher tree-density orchards (Fig. S7 B). No significant differences were found in species prevalence in relation to the presence of mature oaks around sampling points (Fig. S7 B). The proportion of agricultural land around olive orchards had a significantly positive effect on the prevalence of corn bunting (Fig. S7 C). The proportion of woodland had significantly positive effects on bluet tit and woodchat shrike, and negative effects on magpie Pica pica and red-legged partridge Alectoris rufa (Fig. S7 D).

3.3. Overall patterns of bird occurrence A total of 32 bird species was recorded in the sampled olive orchards (Table S2). Twenty-nine of these species were observed in the traditional orchards, 25 in the intensive and 20 in the super-intensive (Fig. S4, Table S2). Traditional orchards had six exclusive species (longtailed tit Aegithalus caudatus, short-toed treecreeper Certhia brachydactyla, nuthatch Sitta europaea, woodchat shrike Lanius senator, turtle dove Streptopelia turtur and house sparrow Passer domesticus), whereas the intensive and super-intensive orchards hosted one exclusive species each, respectively, black-eared wheatear Oenanthe hispanica and chaffinch Fringilla coelebs (Fig. S4, Table S2). The most prevalent species in traditional orchards were corn bunting Emberiza calandra (36.1% of sampling points), blue tit Cyanistes caeruleus (31.1%), Iberian magpie Cyanopica cooki (26.2%) and the Galerida larks (26.2%). In intensive orchards, the most prevalent species were Galerida larks (39.1%), serin Serinus serinus (25.0%), goldfinch Carduelis carduelis (20.3%) and linnet Linaria cannabina (20.3%). Sardinian warbler Sylvia melanocephala (37.9%), goldfinch (36.4%) and linnet (27.3%) had the highest prevalence in super-intensive orchards (Table S2).

3.5. Community-level responses The model obtained predicted a decrease in global mean species richness between the traditional and the more intensively managed olive farms, after accounting for the potential influence of orchard features and landscape context (Fig. 3 A). Analysis of traits only yielded significant results for insectivore cavity-nesters, that decreased their richness along the intensification gradient, with significant differences not only between traditional and the more intensive orchards, but also between the two intensive management levels (Fig. 3 B–E). The phylogenetic signal (q = 0.93; 95% credible interval 0.90-0.98) yielded strong support for the hypothesis that residual variation in species occurrence follows a phylogenetic structure, suggesting the potential existence of additional functionally important traits that were not considered in our analysis (Fig. S8).

3.4. Species-level responses The HMSC model showed moderate to high discrimination ability of species presence/absence as measured by AUC (mean 0.88 ± 0.10 SD; range = 0.61–1.00, Fig. S5). The estimated 95% confidence intervals of AUC never intersected 0.5, indicating that each model performed significantly better than the corresponding random model (Table S5). However, four species with a single occurrence (turtle dove, long-tailed tit, nuthatch and house sparrow) had AUC = 1 and thus confidence intervals could not be computed (Table S5). Separation between the mean predicted values for presences and absences tended to be small (Mean Tjur 0.09 ± 0.07 SD; range = 0.01–0.25). There was no significant correlation between Tjur and species prevalence (r = 0.32, P = 0.071), while a strong negative relationship with prevalence was found for AUC (r=-0.71, P < 0.001), though it is strongly reduced when four species with low AUC and high prevalence are excluded (r=0.38, P < 0.048) (Fig. S5). Intensification level accounted on average for 35% (range = 12–59%) of the explained variation in the probability of species occurrences, whereas orchard features and landscape context accounted for 16% each (range = 5–39% and 7–39% respectively, Fig. S6). The remaining variation was attributed to random effects at the orchard (9%; range 2–27%) and point-count (9%; range 2–31%) levels, while the contributions of temporal effects were broadly similar for sampling year (8%, range = 2–26%) and season (7%, range = 3–17%) (Fig. S6). Fourteen species (44% of total species) showed a significant decline in their predicted occurrence probabilities from traditional to superintensive management, and seven species (22% of total species) showed the same declining pattern from traditional to intensive (Fig. 2). In contrast, only linnet and goldfinch showed significant opposite patterns, with linnet increasing its occurrence probability from traditional to both intensive and super-intensive management (Fig. 2), and goldfinch only increasing from traditional to super-intensive management (Fig. 2). There was also a clear trend (although not significant) for a positive response to intensive orchards by serin, and to super-intensive orchards by Sardinian warbler (Fig. 2). Regarding orchard features, we found that the probability of occurrence of five species varied significantly in relation to residual variability in orchard structure and management within intensification

4. Discussion The results largely confirmed the initial hypotheses of the study, namely that (1) olive faming intensification is associated with significant declines in breeding bird species richness and in the probability of occurrence of many individual species within olive orchards, and (2) that declines are at least partly driven by species traits, as indicated by the steeper declines observed for cavity-nesting insectivores and the significant phylogenetic signal suggesting that unmeasured traits further contributed to the patterns detected. These strong responses were evident after statistically controlling for orchard- and landscape-level factors that are known to affect farmland bird communities in the region (e.g., Santana et al., 2017a, b), and that also contributed to variation in bird communities within orchards. Overall, our findings are consistent with previous studies, which found similar patterns of decreasing bird richness or abundance along olive farming intensification gradients (e.g., Muñoz-Cobo et al., 2001; Santos and Cabral, 2004; Siebert, 2004; Solomou and Sfougaris, 2015; Bouam et al., 2017; Rey et al., 2019). To our knowledge, however, this study provides the first published evidence of the particularly damaging consequences for birds of the conversion to super-intensive orchards. 4.1. Limitations and potential shortcomings It is unlikely that our key results were shaped to any significant extent by methodological biases or shortcomings, though our study was based on birds surveyed in a single visit per orchard, and there was variation between orchards on the sampling year and the period of sampling (i.e., early- versus late-breeding season). These factors contributed about 15% to variation in bird communities among points, but they were controlled statistically by specifying temporal random effects, and thus did not influence the observed responses to intensification. Another potential problem might be the use of several adjacent point-counts within the same orchard, which implies that they were not independent, both because of similarities in ecological and management conditions within orchards, or due eventually to duplicate counts of the same individuals in nearby points, though great care was taken to 6

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Fig. 2. Species responses (standardised regression coefficients) to olive farming intensification, considering the transitions from traditional (reference level) to intensive (A), and super-intensive (B) management. Symbols and bars show the average response of the species to these transitions (posterior mean) and corresponding 95% credible intervals. Species whose responses had statistical support (intervals not including zero) are highlighted with red triangles (representing negative responses) and green squares (positive responses). See Table S2 for species codes correspondence. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

avoid the later problem. Olive orchard identity was incorporated as a random effect in the model to tackle this, and our results showed that the clustering of points within orchards did indeed contribute about 9% to community variation. It may also be argued that our study design was insufficient to characterize bird communities due to the low number of sampling units per orchard, or due to the limited duration and short detection radius of the point counts. This sampling scheme may have indeed contributed to underestimate species richness and increase random variation in species composition per sampling point, and thus to introduce sampling noise that might have made it more difficult to detect ecological signals. However, this scheme resulted from the usual trade-off between selecting many points and having a large sampling effort per point, under the constraint of limited human, financial and logistic resources. Given the objective of our study, we believe that the compromise solution achieved was reasonable, which is supported by the responses observed to the intensification gradient. Finally, it is possible that the observed effects of intensification within orchards were driven to some extent by landscape-scale effects, because traditional orchards tended to be embedded in heterogeneous Mediterranean landscapes, while more intensive orchards generally occurred in more homogeneous landscapes dominated by intensive

olive farming. Our results suggest that there were indeed important effects of landscape context on bird communities within orchards, but such effects were largely controlled statistically in the model, showing that there were declines in species richness when maintaining the landscapes variables at their mean value across sampled orchards. However, we recognise that a more thorough appreciation of the local versus landscape effects underpinning the responses of birds to olive farming intensification is warranted, though this was beyond the scope of the current study. 4.2. Bird community responses to the intensification of olive farming Our study showed that the probability of occurrence of almost half the species considered (14 out of 32 species) declined significantly in response to intensification, while ca. 30% of the species observed in traditional olive orchards were absent altogether in super-intensive orchards. The range of species negatively affected by intensification was very wide in terms of taxonomy and ecological preferences, including for instance columbids (e.g., woodpigeon Columba palumbus), corvids (e.g., Iberian magpie) and passerines associated to both wooded (e.g., blue tit) and open arable (e.g., corn bunting) habitats. This 7

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Fig. 3. Variation in predicted species richness (posterior mean and 95% credible intervals (CI) of number of species detected by point count) considering all species (A) and the trait groups (B–E), across the 3 olive farming intensification levels (Trad – Traditional, Int – Intensive, Super – Super-intensive). Distinct letters (a,b,c) in each panel correspond to intensification levels with non-overlapping confidence intervals, and that were thus considered significantly different from each other.

shrublands, their preferred habitats (del Hoyo et al., 2018). MunõzCobo and Montesino (2003) also highlighted the association of this species to young and dense olive plantations in Southern Spain. The richness and composition of bird communities were also explained by orchard features (16%) and landscape context (16%). Regarding the former, analysis was based on residual variation in orchard age, and tree density and size, within each intensification level, possibly reflecting a gradient from more recently planted to older orchards. Responses to this gradient suggest that species such as shorttoed lark, black-eared wheatear and stonechat favoured younger plantations with smaller trees, possibly because they resemble open shrublands with large expanses of bare ground that provide key habitats for these species. In contrast, woodland species such as blue tit and short-toed treecreeper were more prevalent in relatively older orchards with larger trees planted at lower densities, probably because these are more like the wooded habitats favoured by the species in this region. Against our expectations (e.g., Manning et al., 2006), the presence of adult oaks within olive orchards had no significant effects, likely

suggests that intensification was associated with a general degradation of ecological conditions for a great part of the bird community, which was probably related to the radical change in orchard structure and management along the intensification gradient, including the disappearance of large and old trees characteristic of traditional orchards, and their replacement by young and small trees, together with likely increases in agrochemical inputs and disturbance through mechanization (Tous et al., 2014; Infante-Amate et al., 2016). In contrast to this general pattern, linnet, goldfinch and, to a lesser extent, serin appeared to benefit from these changing conditions, probably because they are eclectic generalists that often thrive in highly modified landscapes (del Hoyo et al., 2018). This is in line with previous studies showing higher prevalence and densities of Fringillidae in intensive fruit orchards (e.g., Genghini et al., 2006; Bouvier et al., 2011), including intensive olive orchards (Muñoz-Cobo et al., 2001; Munõz-Cobo and Montesino, 2003). There was also a tendency, albeit non-significant, for Sardinian warblers to respond positively to super intensive orchards, probably because they are structurally similar to 8

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because isolated trees were more prevalent in traditional orchards, and so this effect was probably already accounted for by the intensification level covariate. Landscape context was also influential, with farmland species (e.g., corn bunting and red-legged partridge) being favoured by the amount of open arable land around olive orchards, while woodland species (e.g., blue tit and woodchat shrike) were favoured by the amount of oak woodlands and other forest habitats around olive orchards.

orchards, particularly for cavity-nesters, though the effects of factors such as pesticide application and mechanical operations still needs to be evaluated. Additional negative effects may result from homogenization associated with the progressive occupation of entire landscapes by intensive olive farming, as spill-over from surrounding land uses (e.g., arable land, oak woodlands) contributes to the species pool recorded within olive orchards. Overall, therefore, our results suggest that the fast expansion of intensive olive farming production systems have the potential to result in major farmland bird declines, both in the Mediterranean and elsewhere (Connor et al., 2014). To mitigate the impacts of intensive and super-intensive olive farming, measures should be taken to prevent the conversion of farmland habitats that are rich in biodiversity and have high scenic and cultural value, such as traditional olive orchards (Perrino et al., 2014; Fleskens et al., 2009) and open agricultural systems favoured by steppe birds (Santos and Cabral, 2004; Reino et al., 2009; Morgado et al., 2010; Santos et al., 2018). This can be achieved through legal restrictions to the conversion of particularly valuable habitats, or through direct economic incentives to farmers to maintaining less intensive land uses, including for instance targeted agri-environment schemes (Beaufoy and Marsden, 2010; Ribeiro et al., 2018) or increased support to organic farming options (Mili et al., 2017). Similar restrictions or incentives should be used to prevent the overly dominance of intensive and particularly super-intensive olive groves at the landscape scale, thereby avoiding the negative effects of land use homogenization on bird diversity (Santana et al., 2017a, b; Rey et al., 2019). Finally, efforts should be made to retain as much bird diversity as possible within intensive and, particularly, super intensive olive orchards, though the type of measures likely to work best is still uncertain. Nevertheless, it should be worth testing general measures that have already proved positive for biodiversity in olive orchards, such as retaining herbaceous cover and woody hedges (Castro-Caro et al., 2015; Rey et al., 2019), but also in other agricultural systems, such as retaining woodland and shrubland patches, including isolated large trees (e.g., Manning et al., 2006; Tryjanowski et al., 2011; Morelli, 2013), or the provisioning of nest boxes (Jedlicka et al., 2014). The implementation of such measures might be supported by agri-environment schemes, at it is the case already for the maintenance of herbaceous cover (see for e.g., Rey et al., 2019), though it may also be driven by consumers demanding a more environmentally-friendly production of olive oil. Overall, the results of our study suggest that a greater care than has been taken so far should be devoted to the impacts of intensive and, particularly, of super-intensive olive farming on biodiversity, aiming to guarantee that environmental regulations and agricultural policies are aligned with the goal of achieving a more balanced equilibrium between the needs of production and environmental sustainability (Moreira et al., 2019; Pe’er et al., 2019). This requires wide recognition that the current expansion and intensification processes are unsustainable, and they may lead to vast areas of the Mediterranean region losing their valuable biodiversity elements. As these processes are at least partly driven by increased worldwide olive oil demand associated to the health benefits of the Mediterranean Diet (e.g. Scheidel and Krausmann, 2011), which is also recognized as the paradigm of an “environmentally sustainable” diet (Burlingame and Dernini, 2012), efforts should be directed to creating public awareness about the paradoxical negative ecological costs of the globalization of this diet (Moreira et al., 2019).

4.3. Traits mediate community responses to intensification Bird responses to intensification were accounted to a significant extent by species traits, with consistent declines observed for cavitynester insectivores. The effects on cavity-nesters might be expected because more intensive plantations are characterized by small and young trees, which lack natural cavities and are also generally unsuitable for cavity building (e.g., Grüebler et al., 2013). In contrast, traditional orchards have old and large trees which have plenty of cavities, allowing the establishment of a rich community of cavity-nesters (see e.g., Kajtoch, 2017 for other fruit orchards). The negative effects on insectivore birds might also be expected, because they tend to decline with management intensification in several agricultural contexts, including fruit orchards, possibly due to the application of pesticides reducing food availability (e.g., Morris et al., 2005; Bouvier et al., 2011; Hallmann et al., 2014). It should be noted, however, that effects of intensification were not apparent for non-cavity nesting insectivores, which suggests that in our case the main driver of declines was probably the lack of nesting conditions, rather than changes in food availability. This idea is supported by the nearly significant positive response of Sardinian warbler to super-intensive orchards, where this insectivore bird was likely to find adequate breeding conditions (del Hoyo et al., 2018). These results suggest that breeding habitat availability or quality might have a potentially larger role than anticipated in limiting the occurrence of insectivore birds in intensively managed olive orchards (but see Genghini et al., 2006; Wiącek and Polak, 2008). The other traits considered in this study did not have consistent responses to intensification, probably because within each functional group there were species with disparate responses. For instance, among tree-nesting granivores there were marked negative response to intensification by wood pigeon, and positive responses by linnet and goldfinch. Likewise, among the non-cavity nesting insectivores there was a negative response by woodchat shrike, a nearly significant positive response by Sardinian warbler, and non-significant effects of intensification by all the other species. Despite these results, it is likely that other traits affected bird responses to intensification and other environmental factors, as indicated by the strong phylogenetic signal provided by the model. This signal shows that part of the residual variation was taxonomically structured (Ovaskainen et al., 2017), thereby suggesting that groups of taxonomically related species shared some unmeasured traits that elicited similar responses to environmental gradients. Such common responses were found for instance for Columbidae, all of which tended to be negatively affected by intensification, and the Fringillidae, most of which had positive responses to more intensive olive farming. Future studies should strive to understand what traits elicit such common responses, which would greatly help identifying the species most susceptible to the intensification of olive farming. 5. Conclusions and management implications Our study shows that production intensification is leading to the simplification of breeding bird communities inhabiting olive orchards, which is in line with the strong detrimental impacts of agricultural intensification on birds reported across Europe (e.g., Donald et al., 2006; Herzon et al., 2008; Reif and Vermouzek, 2018). Negative effects appeared to be primarily related to the poor breeding habitat conditions offered by intensive and specially by super-intensive hedgerow-like

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Acknowledgments

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This work is funded by FEDER through the Operational Programme for Competitiveness Factors (COMPETE) and by national funds through Foundation for Science and Technology (FCT) under the UID/BIA/ 50027/2019, POCI-01-0145-FEDER-006821, PTDC/BIA-BIC/2203/ 2012 and PTDC/AAG-REC/6480/2014. RM, MP, LR, JMH and FM were supported by FCT grants/contracts SFRH/BD/99746/2014, SFRH/ BPD/97025/2013, SFRH/BPD/93079/2013, IF/00001/2015 and IF/ 01053/2015, respectively. JS and FM were supported by FARSYD project (POCI-01-0145-FEDER-016664 - PTDC/AAG-EC/5007/2014) and REN Biodiversity Chair, respectively. PB was supported by EDP Biodiversity Chair. We thank Alexandre Vaz and Rui Pedroso for their collaboration in bird censuses; Otso Ovaskainen, Guillaume Blanchet and Ricardo Rocha for their help in the use of the HMSC modelling framework; and the journal Editor and three anonymous referees for their thoughtful comments/suggestions that helped improve earlier versions of this paper. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agee.2019.106694. References Abrego, N., Norberg, A., Ovaskainen, O., 2017. Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. J. Ecol. 105, 1070–1081. https://doi.org/10.1111/1365-2745.12722. Bas, Y., Renard, M., Jiguet, F., 2008. Nesting strategy predicts farmland bird response to agricultural intensity. Agric. Ecosyst. Environ. 134 (3-4), 143–147. https://doi.org/ 10.1016/j.agee.2009.06.006. Beaufoy, G., Marsden, K., 2010. CAP Reform 2013: Last Chance to Stop the Decline of Europe’s High Nature Value Farming. Joint Position Paper by European Forum on Nature Conservation and Pastoralism. Birdlife International, Butterfly Conservation Europe and WWF European Policy Office. EFNCP, Derwentside, UK (accessed 15 March 2018). http://www.efncp.org/download/policy-cap-reform-2013.pdf. Bibby, C.J., Burgess, N.D., Hill, D.A., 1992. Bird Census Techniques. Academic Press, London, UK. Bouam, I., Bachir, A., Katayama, N., 2017. Variation in bird assemblages along an agricultural intensification gradient: A case study of olive orchards in North-Eastern Algeria. Ornithol. Sci. 16 (2), 147–157. https://doi.org/10.2326/osj.16.147. Bouvier, J.C., Ricci, B., Agerberg, J., Lavigne, C., 2011. Apple orchard pest control strategies affect bird communities in southeastern France. Environ. Toxicol. Chem. 30, 212–219. https://doi.org/10.1002/etc.377. Burlingame, B., Dernini, S. (Eds.), 2012. Sustainable diets and biodiversity: directions and solutions for policy, research and action. Proceedings of the International Scientific Symposium Biodiversity and Sustainable Diets United Against Hunger, 3–5 November 2010, FAO, Rome, Italy. http://www.fao.org/docrep/016/i3004e/i3004e. pdf (accessed 18 June 2018). Castro-Caro, J.C., Barrio, I.C., Tortosa, F.S., 2015. Effects of hedges and herbaceous cover on passerine communities in Mediterranean olive groves. Acta Ornithol. 50, 180–192. https://doi.org/10.3161/00016454AO2015.50.2.006. Connor, D.J., Gómez-del-Campo, M., Rousseaux, M.C., Searles, P.S., 2014. Structure, management and productivity of hedgerow olive orchards. A review. Sci. Hortic. 169, 71–93. https://doi.org/10.1016/j.scienta.2014.02.010. Crist, E., Mora, C., Engelman, R., 2017. The interaction of human population, food production, and biodiversity protection. Science 356 (6335), 260–264. https://science. sciencemag.org/content/356/6335/260. del Hoyo, J., Elliott, A., Sargatal, J., Christie, D.A., de Juana, E. (Eds.), 2018. Handbook of the Birds of the World Alive. Lynx Edicions, Barcelona (accessed 28 March 2018). http://www.hbw.com/. Donald, P.F., Sanderson, F.J., Burfield, I.J., van Bommel, F.P.J., 2006. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990-2000. Agric. Ecosyst. Environ. 116 (3-4), 189–196. https://doi.org/10.1016/j. agee.2006.02.007. Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J.R.G., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D., Lautenbach, S., 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36 (1), 27–46. https://doi.org/10.1111/j.1600-0587. 2012.07348.x. Duarte, F., Jones, N., Lúcio, C., Nunes, A., 2006. The reform of the olive oil regime and its impacts on the olive and olive oil sector: a case study in Northern Portugal - Trás-osMontes. New Medit 5 (2), 4–15. Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T.,

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