Author's personal copy

Landscape Ecol

https://doi.org/10.1007/s10980-020-01091-9 (0123456789().,-volV)(0123456789().,-volV)

RESEARCH ARTICLE

Scale-dependent correlates of reptile communities in natural patches within a fragmented agroecosystem

Guy Rotem . Itamar Giladi . Amos Bouskila . Yaron Ziv

Received: 4 May 2020 / Accepted: 5 August 2020 Ó Springer Nature B.V. 2020

Abstract measures are affected differentially by variables Context Studying biodiversity in light of increased related to different scales. fragmentation in agroecosystems requires the under- Methods We sampled reptiles in three 12.6 km2 - standing of scale-dependent and multi-scale determi- land units by using 100 9 50 m plots within 27 nants of various community measures. natural habitat patches. We collected spatial informa- Objectives In a heterogeneous agro-landscape, we tion of different scale-oriented physical and biotic aimed to understand whether: (1) Reptile communities variables and analyzed changes in community mea- are affected by a certain variable that belongs to a sures at four scales—landscape, land unit, patch and particular scale, or, by a combination of variables at plot—by using the model selection approach based on different scales, and (2) Reptile community the AICc. Results Multiple-scale, rather than single-scale models, best explained all three community measures, Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10980-020-01091-9) con- indicating that the reptile community structure is tains supplementary material, which is available to authorized highly affected by ecological processes operating at users. different scales, from the local up to the entire G. Rotem (&) Y. Ziv landscape scale. However, abundance, species rich- Spatial EcologyÁ Laboratory, Department of Life Sciences, ness and diversity were affected dissimilarly by Ben-Gurion University of the Negev, P.O.B. 653, different combined determinants and at different 84105 Beer-Sheva, Israel scales. e-mail: [email protected] Conclusions Reptile biodiversity at our heteroge- Y. Ziv nous agro-landscape is highly influenced by determi- e-mail: [email protected] nants of multiple scales, where each scale has its I. Giladi contribution to the overall obtained pattern. Number of Mitrani Department of Desert Ecology, Jacob Blaustein individuals and species richness respond differently to Institutes for Desert Research, Ben-Gurion University of various processes, depending on the scale at which the Negev, Beer-Sheva, Israel e-mail: [email protected] these processes operate. Agro-landscapes retain the complexity of ecological systems and can serve to A. Bouskila maintain natural communities through land sharing Behavioral Ecology Laboratory, Department of Life practices. Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel e-mail: [email protected]

123 Author's personal copy

Landscape Ecol

Keywords Abundance Agro-ecosystem AICc 2012). A multi-scale approach to explore biodiversity Á Á Á Community structure Model selection Reptile patterns in natural habitat patches embedded in a Á Á Á Species diversity Species richness relatively hostile environment has been advocated for Á agroecosystems (see Benton et al. 2003; Fahrig et al. 2011). Intensified agricultural practices typically leave natural patches in agroecosystems that vary in size, Introduction isolation, and at risk of biodiversity loss (Yaacobi et al. 2007a; Hogg and Daane 2010). Fundamental ecological theories, such as island bio- A comprehensive understanding of the structure geography (MacArthur and Wilson 1967) and and diversity patterns of a community requires the metapopulation dynamics (Levins 1969), postulate a simultaneous assessment of measures (e.g., total positive relationship between biodiversity in ecolog- abundance, species richness and diversity), reflecting ical patches (i.e., islands or remaining natural habitats) different aspects of the response of the community to and the connectivity between these patches. Ecolog- environmental conditions and to ecological con- ical theories also suggest, either explicitly (e.g., niche straints, such as habitat heterogeneity and climatic theory; Hutchinson 1959; Vandermeer 1972) or effects (Michael et al. 2008). For example, total implicitly (e.g., island biogeography and metapopula- abundance may reflect the biomass and food avail- tion dynamics theories), that greater environmental ability for all individuals, regardless of species identity heterogeneity supports greater biodiversity (Stein (Shine and Madsen 1997; Smart et al. 2000); species et al. 2014). Habitat fragmentation, which character- richness (i.e., the raw and/or corrected number of izes most modern agricultural landscapes, increases species per area unit) may reflect the means by which natural patch isolation and decreases environmental different organisms utilize resources within the com- heterogeneity (Robinson and Sutherland 2002; Green munity (i.e., their niche; Rocha et al. 2008; Soininen et al. 2005; FAO 2007). The latter represent major key et al. 2011); species diversity may reflect the propor- factors that contribute to the loss of biodiversity tional use and subdivision of resources among the worldwide (Sodhi and Ehrlich 2010). The conserva- existing species, thereby providing additional infor- tion of biodiversity in anthropogenic landscapes— mation about realized niches and interactions among such as modern agricultural landscapes—necessitates sets of species (Hiltunen et al. 2006). Importantly, a thorough understanding of such landscapes and because determinants of community structure and ecological processes therein. composition vary with scale (Dumbrell et al. 2008), Contemporary ecology considers species diversity each of these community-related measures may and community structure in natural patches to be a exhibit scale-dependency in a different manner. Con- product of various ecological processes operating at sequently, an advanced understanding of community different scales (Wiens 1989; Yaron 1998; Turner structure in a heterogeneous landscape, such as an et al. 2001; Farina 2006; Hastings et al. 2011). Hence, agroecosystem, may highly benefit from testing the a comprehensive understanding of the processes that effects of environmental determinants on several govern the diversity and composition of species community-related measures using the multi-scale requires a multi-scale approach. Although such an approach. approach has recently become more widespread in In the current study, we applied the multi-scale ecological literature (e.g., McGarigal et al. 2016; approach to study the patterns of reptile assemblage Franciele et al. 2018), it is less common in reptiles (hereafter community) in the semi-arid fragmented studies (but see: Bonnet et al. 2002; Fischer et al. 2004; agroecosystem of the southern Judea lowlands in Michael et al. 2008, 2017; Bruton et al. 2015, 2016). Israel. This study system is located along a very sharp This lacuna is troublesome because reptiles have an climatic gradient and comprises a highly fragmented important role in food-web structure and biodiversity, landscape in which habitat patches of different sizes and because reptiles are known to be highly affected are isolated, partly or entirely, by agricultural fields. by both internal habitat characteristics and landscape Based on our previous studies (Yaacobi et al. 2007a, b; metrics related to isolation, dispersal, and long-term Giladi et al. 2011, 2014; May et al. 2012, 2013; Rotem survival (Brown et al. 2011; Cabrera and Reynoso et al. 2013, 2015; Rotem and Ziv 2016), four explicit

123 Author's personal copy

Landscape Ecol scales—landscape (the entire extent with its climatic Methods variation that composes a mixture of reptile commu- nities at different land units), land unit (a cluster of Study area patches that provide opportunities for dispersal and species turnovers for diverse reptile species), patch (an The study area was located in the southern Judean isolated natural habitat that comprises several habitats lowlands (SJL), Israel (31° 240 0000–31° 400 5000 N, 34° and heterogenous local-scale conditions for reptile 480 30–34° 500 300 E; Fig. 1), which represents a species) and plot (a sampling area within a given patch transition zone between desert and Mediterranean which may include few home ranges of particular ecosystems, with precipitation sharply increasing reptiles)—can be identified in this system given its from south to north. The SJL is a meeting area of heterogeneity and complexity, making it an ideal three phytogeographic zones—Mediterranean, Irano- study area for exploring the community structure of Turanian, and Saharo-Arabic—and provides a mixture organisms in a patchy agroecosystem. Specifically, we of plant associations and animal communities of asked: (1) Are reptile communities within our agroe- different biogeographical regions (Zohary 1962). It cosystem affected by a certain dominant variable that is characterized by short mild winters and long, dry belongs to a particular scale, or, alternatively, by a and hot summers, with an average annual precipitation combination of variables that operate at different ranging from 291 mm in the south to 430 mm in the scales? and (2) Are particular measures of the reptile north, over a distance of nearly 20 km. This increase in community structure—abundance, species richness, precipitation from south to north results in a substan- and species diversity—affected differentially by vari- tial increase in plant density, plant species richness ables related to different scales? (Giladi et al. 2011), vegetation biomass (Schmidt and We hypothesized that reptile community structure Gitelson 2000) and considerable changes in floral depends on variables operating at different scales community composition (Kadmon and Danin 1997; (Michael et al. 2008). At the landscape scale, we 1999). For thousands of years, the landscape has been predicted that the three measures of the reptile used for sheep and goat grazing and for small-scale community structure will increase along the climatic subsistence farming (Naveh and Dan 1973; Acker- gradient, from its arid end to the more productive mann et al. 2008). Intensive agriculture in past decades Mediterranean end (Hawkins et al. 2003). At the land has reshaped the landscape into a patch-matrix mosaic, unit scale, based on species distribution theories with clear boundaries between semi-natural habitats (McArthur and Wilson 1967; Levins 1969; Hanski and the agricultural matrix (Yaacobi et al. 2007a; b; 1982; Gotelli 1991), we predicted that the three Giladi et al. 2011; Gavish et al. 2012). Historical aerial measures of the reptile community structure will be photographs show that the distribution of natural negatively correlated with between-patch isolation habitat patches in the landscape has remained rela- (Rotem et al. 2013). At the patch scale, based on tively constant during the last 60 years (Giladi et al. species-area relationship (Connor and McCoy 1979; 2014). The main vegetation types are characterized as Rosenzweig 1995; Ugland et al. 2003; Tjorve 2009), semi-steppe batha (Mediterranean scrubland) and we predicted that patch size will be correlated grassland (Giladi et al. 2011). positively with abundance and species richness. At the plot scale, based on niche theory (Hutchinson Study design 1959), we predicted that the community structure measures will be positively correlated with habitat We examined three 4 9 3.2 km land units in the SJL heterogeneity. Additionally, we predicted that the agroecological landscape, named, from north to south, reptile community measures will correlate negatively Galon, Lachish, and Dvir (Fig. 1b). These land units with grazing intensity (Attum et al. 2006; Read and are characterized by remnant patches of natural Cunningham 2010), and that this correlation will vegetation of different sizes and configurations, sur- depend on the climatic variation as was found for other rounded by a matrix of agricultural fields. In each of taxa (e.g., for plants; Diaz et al. 2007 for reptiles the three land units, we equally chose nine natural Rotem et al. 2015). patches of different sizes (0.1–361.13 ha), shapes and representative varieties of the local vegetation, to

123 Author's personal copy

Landscape Ecol

Fig. 1 Hierarchical scales of the study system. a A map of and the distribution of remnant natural patches within the Israel, showing the sharp precipitation gradient and the location agricultural matrix. d A patch, with its specific attributes of area, of the Southern Judea Lowlands (SJL) along this gradient. b The shape, and internal heterogeneity. e A perennial shrub (bottom Galon, Lachish, and Dvir land units within the SJL. Note the right corner) surrounded by annual plants (\ 15 cm tall), and the sharp north-to-south decline in the mean annual precipitation contrast with the agricultural matrix (left side of the picture) values along a short distance of 20 km. c The Galon land unit during the sampling period accurately represent their diversity, a total of 27 (i) line-transect surveys—walking in a straight line natural patches. Reptiles in these patches were sam- along the long axis of the sampling plot, during the late pled in plots, 100 9 50 m each. Sampling was morning hours (no earlier than 10:00 am) after reptile conducted according to four levels of patch size as activity has started; (ii) active searches—searching follows: (i) very small patches (\ 0.5 ha) were along the long axis of the plot during the late morning sampled entirely, (ii) small-sized patches (0.5–2 ha), hours, immediately after completing the line-transect where one sampling plot was chosen/sampled, (iii) survey. The active search included turning-over any medium-sized patches (2–5 ha), where three sampling rock with a diameter exceeding 10 cm, and at least 100 plots were sampled and (iv) large patches ([ 50 ha), rocks per plot. We returned all the overturned rocks to where six sampling plots were sampled. We randomly their original positions to avoid damaging the habitat; placed the plots within each patch and verified their (iii) dry pitfall traps—censusing 1260 1-L traps (20 independence by spatial autocorrelation analysis (see traps per plot). We arranged the pitfall traps in each below). Overall, 21 plots were sampled in each land plot at 10 m intervals, in two lines spaced 25 m apart unit for a total of 63 plots. parallel to the long axis of the plot (Corn and Bury 1990; Read and Moseby 2001; Fisher et al. 2008). We Survey protocol opened the traps before sunset and left them open for two whole days (each trap was open for 6 days a year, We searched for reptiles in six sessions throughout the for a total of 15,120 trapping days). After two days the spring (March–June) of 2009 and 2010 (three sessions trapped individuals were identified and released. To in each year). We used three complementary methods prevent injury to animals and to reduce their discom- to sample the reptile community within each plot: fort, we built a tiled roof over each trap, which

123 Author's personal copy

Landscape Ecol protected the trap from direct solar radiation and and stone sizes and used them as explanatory reduced the exposure of trapped animals to predation variables. risk. In the 1st year of the study (2009), we marked We quantified the level of grazing by domestic captured reptiles individually (Ferner 2007; Perry animals. In the southern (Dvir) and middle (Lachish) et al. 2011). Since recapture rate was very low (less land units, where grazing is by sheep and goats, we than 5%) during the 1st year, we ceased marking estimated grazing pressure at the plot level by taking individuals in the subsequent year (Rotem et al. 2013). the average feces density measured in ten 1 9 1m Given that we sampled the reptiles in equal-sized quadrats in each plot (Rotem et al. 2015). In the plots, we measured species density rather than patch- northern (Galon) land unit, which is grazed by cattle, level species richness (Holt 1992; Gotelli and Graves plot-level grazing pressure was estimated by averag- 1996; Connor et al. 2000; we consider it later in the ing grazer feces density from five 2 9 2 m quadrats ‘‘Discussion’’ section). per plot (Rotem et al. 2015). We used rectified aerial photographs (pixel size = 1 Measurements of variables at different scales m2) to identify all the patches of natural vegetation within each land unit. We then demarcated the We focused on three measures related to community boundaries of each patch on a digitized map and structure: abundance (i.e., number of individuals per stored the information as a vector-based coverage in plot), local species richness (i.e., the raw number of the Geographical Information Systems (GIS) platform species per area unit) and species diversity (i.e., the (ArcMap, ESRI, Redland, USA). Next, we converted combined index of abundance and richness—Fisher’s the data to a raster-based layer (grid cells size = 5 9 alpha; Fisher et al. 1943; Rosenzweig 1995). We 5 m) and exported it to FRAGSTATSÓ (McGarigal recorded a range of categorical and continuous and Cushman 2002) to calculate patch shape and patch variables at multiple spatial scales—plot, patch, land proximity index (PI). unit and landscape—and used them as explanatory At the patch-scale level, we used patch area and variables for the community structure measures patch shape as explanatory variables. Initially, we (Table 1). considered a wider set of patch-level metrices, but Habitat types and plot-scale heterogeneity were since many of these were correlated among them- evaluated by using line transects along the long axis of selves, we were left with the above two uncorrelated each sampling plot. Based on previous studies (Giladi metrices. We calculated patch shape by using the et al. 2011; Gavish et al. 2012), we measured the shoreline fractal-dimension index (Farina 2006). relative cover of six habitat variables, including stone At the land-unit scale, we applied proximity index cover, exposed soil, annual plants, shrubs, Sar- (PI) as a measure of patch isolation, which quantifies copterium spinosum and Hyparrhenia hirta. The two the spatial context of a patch in relation to neighboring latter habitat types are defined by the most dominant patches (Gustafson and Parker 1992; see also Farina perennials in the study area, both of which form a 2006). We used a radius of 1000 m because prelim- unique microhabitat under and around their canopies. inary data indicated that PI values were very similar Built on the relative covers of the six habitat variables, for different radii (the correlation coefficients between we calculated plot heterogeneity by using Shannon PI-100 and PI-500, between PI-100 and PI-1000, and diversity. Slope aspect was included as an explanatory between PI-500 and PI-1,000 were 0.75, 0.76, and variable because south-facing (90°–269°) slopes 0.99, respectively). We examined additional metrices receive higher levels of solar radiation and, conse- of connectivity (e.g. Nearest neighbor), but since they quently, have lower water availability and higher were correlated with PI, we excluded them from any ground temperatures than north-facing (270°–89°) further analysis. At the landscape-scale level, we used slopes. As a result of ancient agricultural activity, the average annual rainfall as an explanatory variable some plots contained ancient cisterns and/or stone (Fig. 1; see Giladi et al. 2011). walls that may serve as shelter for reptiles; we regarded the presence of each of these structures as a categorical explanatory variable. When plots included stone heaps, we measured the total area of the heaps

123 Author's personal copy

Landscape Ecol

Table 1 A list of independent explanatory variables, their description, and the scale at which they were measured Explanatory variable Variable type Description

Landscape scale North–south gradient Continuous Average annual rainfall Land unit scale Proximity index Continuous derived from FRAGSTAT Patch scale Patch size Continuous GIS-based Shoreline fractal dimension Continuous derived from FRAGSTAT Plot scale Grazing Continuous Based on average feces density per plot as grazing pressure measure Aspect Categorical Measured with a compass Stone cover Continuous Based on line transects along the long axis of the plot Exposed soil Continuous Based on line transects along the long axis of the plot Annual plants Continuous Based on line transects along the long axis of the plot Shrubs Continuous Based on line transects along the long axis of the plot Sarcopterium spinosum Continuous Based on line transects along the long axis of the plot Hyparrhenia hirta Continuous Based on line transects along the long axis of the plot Cistern Categorical Present/absent Historical wall Categorical Present/absent Heap area Continuous Total area and stone size Plot heterogeneity Continuous Shannon diversity, based on line transects along the long axis of the plot

Statistical analyses with the three community measures. Finally, we examined whether our best models (i.e., lowest AICc Based on the prospective explanatory variables, we for either abundance, species richness, or species constructed a series of 68 biologically meaningful diversity) were biased by residual spatial autocorrela- Generalized Linear Models (Online Appendix 1) with tion. For each model, we calculated the correlogram of Poisson distribution and log-link for each measure of the model residuals based on Moran’s I (Dormann the community structure (i.e., abundance, species et al. 2007). We found no spatial autocorrelation in the richness, and species diversity). Some models residuals of any of the models described in the Results included only the variables belonging to a particular section. scale (i.e., plot scale, patch scale, land-unit scale or All analyses were implemented in R programming landscape scale), while other models included vari- language (R Core Team 2013) by using the following ables belonging to several scales (i.e., multi-scale functions from the following R packages: MuMIn models) with all the existing combinations. (Barton 2013) for model selection, AICcmodavg Both the intercept (null) model and the global (Mazerolle 2013) for model averaging, and Pgirmess model were included in the analysis. However, we (Giraudoux 2014) for the spatial autocorrelation present these two extreme cases in the relevant analysis. tables only when they belong to the set of models with the best fit to the data. Following the construction of the full set of models, we applied a model selection Results procedure by using the Akaike Information Criterion, corrected for small sample size (AICc; Anderson Overall, in the 63 sampled plots, we recorded a total of 2008). We also used regression and ANOVAs to 781 individual reptiles belonging to 29 species and 12 analyze the association of each explanatory variable different families. Most of the species are

123 Author's personal copy

Landscape Ecol

insectivorous and shared a similar range of body mass from all four scales (Table 4) had lower AICc values (85% of individuals caught weighed less than 10 g and than models that included explanatory variables from had a 200 mm snout-vent length). Of these, 349 only one scale (single-scale models), hence, showed a individuals (representing 20 species) were observed in better fit to the data. the northern land unit (Galon), 226 individuals At the landscape scale, species richness was not (representing 19 species) were observed in the central significantly correlated with precipitation. At the land- land unit (Lachish), and 206 individuals (representing unit scale, species richness was significantly and 25 species) were observed in the southern land unit negatively correlation with proximity (PI) (AR2- (Dvir). Three species (Trachylepis vittata, Chalcides = 0.12, P = 0.042). At the patch scale, we found no guentheri and Ablepharus rueppellii) accounted for significant correlation between species richness and over 53% of all observations (Table 2). The mean patch size (P [ 0.05). At the plot scale, as for total number of reptile species per plot was 5.01 (range abundance, species richness was correlated with 1–11) and the mean abundance was 10.2 individuals several variables, and with most of them in a land per plot (range 2–24). unit-dependent manner. Species richness correlated positively with grazing intensity in Galon (AR2- Correlates of total abundance models = 0.46, P = 0.006) but negatively in Dvir (AR2- = 0.34, P = 0.04). No significant correlation with For total abundance (Table 3), models that included grazing intensity was found in the central land unit explanatory variables from multiple scales (models (Lachish). We also found a positive correlation

1–3; Table 3) had lower AICc values than models that between species richness and plot heterogeneity included explanatory variables from only one scale (AR2 = 0.1102, P = 0.005, Fig. 4b). Additionally, (single-scale models), hence they showed a better fit to reptile species richness was positively correlated with the data. percent rock cover in Dvir (AR2 = 0.14, P = 0.04), Reptile abundance was positively correlated with and negatively correlated in Galon (AR2 = 0.17, precipitation (a landscape-scale variable, Adjusted P = 0.039). At this spatial scale we also found positive R-squared (AR2 = 0.1079, P = 0.004, Fig. 2a), sig- and negative correlations between reptile species nificantly and negatively correlated with fractal index richness and total plant cover percent in Dvir (AR2- (patch-scale variable, AR2 = 0.1188, P = 0.006, = 0.15, P = 0.047, Fig. 5f) and Galon (AR2 = 0.106, Fig. 3a), but was not significantly correlated with P = 0.05, Fig. 5d), respectively. patch size nor with patch isolation (both are patch- scale variables). Reptile abundance was also corre- Correlates of species diversity models lated with plot scale variables, but these correlations varied among land units. Reptile abundance was Similar to abundance and species richness, the mul- correlated with grazing intensity as well as percent of tiple-scale species diversity-related models had lower 2 bare ground only in Dvir (AR = 0.24, P = 0.023 and AICc values, reflecting a better fit to the data than AR2 = 0.19, P = 0.047, respectively). We also found a single-scale models (Table 5). correlation between reptile abundance and percent of At the landscape scale, species diversity negatively rock-cover in two of the land units—Dvir and Galon. correlated with precipitation (AR2 = 0.103, However, while the correlation was positive for Dvir P = 0.015, Fig. 2c). At the land-unit scale, species (AR2 = 0.46, P = 0.038), it was negative for Galon diversity was negatively correlated with proximity (AR2 = 0.14, P = 0.041). We also found a significant (AR2 = 0.101, P = 0.042). At the patch scale, species correlation between reptile abundance and total plant diversity correlated positively with fractal index cover percent in Dvir (AR2 = 0.23, P = 0.05, Fig. 5c) (AR2 = 0.1831, P = 0.001, Fig. 3c). Similar to species but not in the other two land units. richness, we did not find a significant correlation between species diversity and patch size (P \ 0.05). Correlates of species richness models At the plot scale, as for total abundance and species richness, reptile species diversity correlated with Similar to the measure of reptile abundance, models of several variables, and with most of them in a land species richness that included explanatory variables unit-dependent manner. Species diversity correlated

123 Author's personal copy

Landscape Ecol

Table 2 Summary of reptile sampling: species composition, site occupancy (number of occupied plots), and species abundance of all reptiles recorded in the SJL Family Species Occupancy (# (%) Total no. of plots occupied) individuals (%)

Agamidae Roughtailed rock agama (Laudakia stellio) 7 (11.1) 20 (2.5) European glass lizard (Ophisaurus apodus) 4 (6.3) 4 (0.5) Atractaspididae Muller’s two-headed snake (Micrelaps muelleri) 2 (3.1) 2 (0.2) Boidae Javelin sand boa (Eryx Jaculus) 4 (6.3) 5(0.6) Chamaeleonidae Mediterranean chameleon (Chamaeleo chamaeleon recticrista) 1 (0.1) 5(0.6) Colubridae Dice snake (Natrix tessellata) 1 (0.1) 1(0.1) Mediterranean cat snake (Telescopus fallax yriacus) 2 (3.1) 2 (0.2) Large whip snake (Coluber jugularis asianus) 14 (22.2) 26 (3.3) Collared dwarf racer (Coluber rubriceps) 17 (26.9) 30 (3.8) Montpellier snake 3 (4.7) 5 (0.6) (Malpolon monspessulanus insignitus) Palestine kukri snake 4 (6.3) 6 (0.7) (Rhynchocalamus melanocephalus) Roth’s dwarf racer (Eirenis rothi) 21 (33.3) 25 (3.2) Gekkonidae Lichtenstein’s short-fingered gecko 9 (14.2) 29 (3.7) (Stenodactylus sthenodactylus) Mediterranean house gecko (Hemidactylus urcicus) 9 (14.8) 26 (3.3) Sinai fan-fingered gecko (Ptyodactylus guttatus) 4 (6.4) 27 (3.4) Lacertidae Olivier’s sand lizard (Mesalina olivieri) 1 (1.8) 1 (0.1) Small spotted lizard (Mesalina guttulata) 2 (3.7) 2 (0.2) Snake-eyed lizard (Ophisops elegans) 15 (23.8) 30 (3.8) Leptotyphlopidae Hook-snouted worm snake (Leptotyphlops macrorhynchus) 5 (7.3) 7 (0.8) Scincidae Bridled mabuya (Trachylepis vittata) 53 (84.2) 253 (32.3) Ocellated bronze skink (Chalcides ocellatus) 11 (17.6) 25 (3.2) Gunther’s cylindrical skink (Chalcides guentheri) 36 (57.4) 79 (10.1) Ru¨ppell’s snake-eyed skink (Ablepharus rueppellii) 28 (44.4) 87 (11.1) Algerian skink (Eumeces schneideri pavimentatus) 21 (33.3) 22 (2.8) (Eumeces schneideri schneideri) 2 (3.7) 2 (0.2) Testudinidae Mediterranean spur-thighed tortoise (Testudo graeca) 15 (23.8) 31 (3.9) Typhlopidae Eurasian blind snake (Typhlops vermicularis) 12 (19.0) 18 (2.3) Simon worm snake (Typhlops simoni) 4 (6.3) 6 (0.7) Viperidae Palestine viper (Vipera palaestinae) 3 (4.7) 5 (0.6) Total abundance 63 781

with grazing intensity positively in Galon and Lachish heterogeneity (AR2 = 0.12, P = 0.03, Fig. 4c). While (AR2 = 0.33, P = 0.005 and AR2 = 0.30, P = 0.0018, species diversity was correlated with bare ground respectively) and negatively in Dvir (AR2 = 0.22, percentage significantly and positively in Dvir (AR2- P = 0.052). Additionally, a positive correlation was = 0.13, P = 0.05), no correlation was found in either found between reptile species diversity and plot Lachish or Galon. Species diversity correlated to rock

123 adcp Ecol Landscape

Table 3 AICc-based model selection to explain reptile abundance (see bottom of the table for abbreviations of parameters) Parameter Plot scale Patch scale Land unit scale Land-scape scale Model 1 Model 2 Model 3

A Landscape PRC - 0.271 - 0.235 - 0.247 - 0.202 Land unit PI 3.218E-06 2.8E-05 3.6E-05 2.5E-04 Patch PS 0.087 - 0.058 - 0.037 - 0.186 F 0.197 0.347 0.275 0.319

Plot GR 0.009 - 0.115 - 0.175 - 0.101 Author's C 0.023 - 0.074 - 0.025 W 0.229 0.104 0.115 HA 0.001 0.036 0.015 0.093 PH 0.856 0.507 0.496 0.521 personal EX - 0.0001 2.20E-03 1.35E-03 RO 0.0005 - 0.086 0.047 0. 076 SA 0.008 0.215 0.274 Interactions PRC 9 GR - 0.163 - 0.129 - 0.206

B copy Model selection information Adjusted R2 0.213 0.101 0.032 0.132 0.641 0.638 0.581

AICc - 276.679 - 89.811 - 87.874 - 95.927 - 313.967 - 313.169 - 311.789 Delta AIC 37.288 224.950 228.092 218.093 0 0.798 2.177 Model probability (Wi) 0 0 0 0 0.46 0.25 0.19 Cumulative Wi 1 1 1 1 0.46 0.70 0.89 Relative probability 0 0 0 0 1 0.38 0.29

(A) Parameter coefficient of the four best models with a cumulative probability of 0.89 (* 90) and the parameter coefficient of the plot, patch, land unit and landscape scales. (B) Model statistics: adjusted R2, AICc, delta AIC, model probability (Wi), accumulative Wi, and relative probability, for each of the models PRC precipitation, PI proximity, PS patch size, F fractal, GR grazing, C cistern, W wall, HA heap area, PH plot heterogeneity, EX exposed soil, RO rock cover percentage, SA slope aspect 123 Author's personal copy

Landscape Ecol

Fig. 2 Effect of precipitation on reptile abundance, species richness and species diversity Fig. 3 Effects of fractal on reptile abundance, species richness and species diversity cover percentage significantly and positively in Dvir and Lachish (AR2 = 0.16, P = 0.03 and AR2 = 0.11, habitats located within agricultural fields. In the P = 0.04 respectively), while a significantly negative current study, we examined scale-dependent variables correlation was found in Galon (AR2 = 0.11, that affect reptile communities in natural patches P = 0.04). within an agricultural ecosystem. We addressed two main questions: (1) Are reptile communities within an agro-ecosystem mainly affected by variables that Discussion belong to a particular scale, or by integrative variables that operate at different scales? and (2) Are particular For more than five decades, ecologists have been measures of the reptile community differentially concerned with questions regarding species diversity affected by variables related to different scales? within natural patches or islands. The classical For all community measures combined, the multi- approach to answering such questions was to study ple-scale models showed a better fit to the data than processes occurring within a local area or patch (e.g., single-scale models. Our findings support the hypoth- Cornell and Lawton 1992). However, a new research esis that the structure of a community of reptiles in approach that has become widely accepted over the natural patches is affected by different ecological past three decades examines a variety of biological processes relating to different spatial scales (Michael processes related to different spatio-temporal scales et al. 2008). In addition, the results of the GLM (Palmer and Dixon 1990; Wu and Loucks 1995; statistical analyses strengthen our hypothesis that each Harrison and Cornell 2008; Hastings et al. 2011). This of the three community measures—total abundance, new approach can be applied to systems of natural species richness and diversity—is affected by different

123 adcp Ecol Landscape

Table 4 AICc-based model selection to explain reptile species richness (see bottom of the table for abbreviations of the parameters) Parameters Plot scale Patch scale Land unit scale Land-scape scale Model 1 Model 2 Model 3

(A) Landscape PRC 0.412 0.421 0.365 0.396 Land unit PI 1.59E-06 - 1.9E-06 - 3.21E-06 - 2.58E-06 Patch PS 0.065 0.26 0.018 0.036 F 0.095 0.137 0.098

Plot GR 0.009 - 0.134 - 0.175 0.215 Author's C - 0.046 - 0.217 - 0.135 - 0.164 W 0.146 0.64 HA 0.003 - 2.54E-04 - 1.65E-03 PH 0.079 0.418 0.406 0.472 personal EX - 0.001 - 0.014 - 0.028 - 0.007 RO 0.002 0.026 0.032 0.045 SA 0.005 - 0.432 - 0.411 Interactions PRC 9 GR 0.254 0.106 0.185

(B) copy Model selection information Adjusted R2 0.093 0.076 0.001 0.024 0.324 0.309 0.302

AICc - 230.541 - 228.692 - 199.676 - 203.118 440.967 - 431.169 - 427.542 Delta AIC 210.425 212.274 241.199 237.849 0 9.798 13.424 Model probability (Wi) 0 0 0 0 0.35 0.32 0.24 Ccumulative Wi 1 1 1 1 0.35 0.67 0.91 Relative probability 0 0 0 0 1 0.81 0.62 (A) Parameter coefficient of the three best models with a cumulative probability of 0.91 and the parameter coefficient of the plot, patch, land unit and landscape scales. (B) Model statistics: adjusted R2, AICc, delta AIC, model probability (Wi), accumulative Wi, and relative probability, for each of the models PRC precipitation, PI proximity, PS patch size, F fractal, GR grazing, C cistern, W wall, HA heap area, PH plot heterogeneity, EX exposed soil, RO rock cover percentage, SA slope aspect 123 Author's personal copy

Landscape Ecol

and Amitai 2001). An equivalent pattern was found for vertebrate species richness in Europe, with an inverse relationship between reptile species richness and water availability (Whittaker et al. 2007). Another well-known equivalent pattern was found for reptile species richness in Australia and southern Africa (Milewski 1981). At the land unit-scale, the variable describing patch isolation (namely, the PI) had no significant effect on reptile abundance, but it nega- tively affected reptile species richness and diversity. Patch isolation may reduce the ability of reptile species to move between different patches through the agricultural matrix (e.g., Rotem and Ziv 2016). Since abundance may reflect the biomass and food avail- ability for all individuals, regardless of their species identity (Shine and Madsen 1997; Smart et al. 2000), it might not be affected by patch isolation. However, species richness and diversity, both of which relate to species identity, were negatively affected by patch isolation. At the patch-scale level we found, in contrast to our prediction, that patch size had no significant effects on any of the community measures, given that there is no significant correlation between patch size and reptile abundance, species richness or diversity. However, it is important to note that we sampled the reptiles in Fig. 4 Effects of plot heterogeneity on reptile abundance, equal-sized plots; therefore, we measured species species richness and species diversity density rather than patch-level species richness (Holt 1992; Gotelli and Graves 1996; Connor et al. 2000). variables at different scales. Below, we address these Thus, the relationship between patch size and species different scales individually. richness in our study does not represent a classic At the landscape-scale level we found that the species/area relationship (see Giladi et al. 2014). precipitation gradient affects all three measures of Nevertheless, our results indicate that in our region community structure. However, while reptile abun- large and rich reptile communities can also be found dance increases from the arid end to the Mediterranean within small patches (see Cabrera-Guzman and end of the gradient, the opposite trend exists for Reynoso 2012). species richness and diversity. Process-wise, the At the plot-scale level, we received complex change from a Mediterranean to a semi-arid climate results. We found that different variables or determi- is characterized, inter alia, by a decrease in plant nants from this scale affected each community mea- biomass (Shoshany and Karnibad 2011), which in turn sure differently and the effect of some variables from may lead to a decrease in the biomass of higher trophic this scale on community measures varied with loca- levels, such as arthropods, that serve as prey for many tion, along the climatic gradient. The plot-scale reptiles. Consequently, a decrease in overall prey variables represented plot heterogeneity, niche avail- abundance can lead to a decrease in reptile abundance. ability and possibly, food availability. Furthermore, a With respect to species richness and diversity, the negative correlation has been found between arthro- pattern we found reflects the well-described gradient pod richness and bare soil (e.g., Yaacobi et al. 2007a; at a greater geographical context, given that there are Silva et al. 2010; Eckert et al. 2020). Because more terrestrial reptile species in the arid and semi- arthropods are a key dietary component for many of arid than in the Mediterranean zone in Israel (Bouskila the reptile species in our research, bare ground may

123 Author's personal copy

Landscape Ecol

Fig. 5 Different effects of plant cover percent on reptile abundance, species richness, and diversity with association to the location along the north–south climatic gradient have had a negative effect on reptile food availability, there were different effects on the same community and consequently on reptiles’ abundance (see above). measure according to the specific locations along the These results support our second hypothesis, which climatic gradient. Our findings are well in line with the states that the three measures of community structure recent ecological literature that shows that community would be associated differently with different vari- measures may change with the spatial and temporal ables. Different effects of variables on the different scales of observation. This is because many processes community measures were found at all scales. At the are scale-dependent (McGill 2010) and, hence, pat- landscape scale, precipitation had a positive effect on terns of community composition differ between scales abundance, probably due to its positive effect of according to the relevant processes within each scale precipitation on productivity, but a negative effect on (Chase et al. 2018, 2019; McGlinn et al. 2018). species diversity, most likely in relation to the Specifically, several other studies tested the effect increasing representation of arid species as precipita- of scale-oriented factors on reptile communities. Our tion decreases. At the land-unit scale, proximity varied results differ from those of Bruton et al. (2016) who from having no significant effect on abundance to found strong effect of the local scale variables but no having a negative effect on species richness and effect of the large-scale variables on reptile commu- species diversity. The effects of plot scale variables nity measures. However, our findings are well in line exhibit a more complex patterns than at the other with results of other studies—Fischer et al. (2004), scales. This may not be surprising given that (a) the Michael et al. (2008), Bruton et al. (2015), and number variable at this scale was higher than at the Michael et al. (2017)—which demonstrated that both other scales, and (b) the plot scale variables represent local and landscape/regional factors contribute to the various underlying mechanisms (e.g. small-scale spatial distribution and various reptile community heterogeneity, disturbance (grazing), and unique habi- characteristics in their study systems. With respect to tat features, etc.) Apart from different effects of the agroecosystems, given their complex environment, same variables on different community measures, where different crop types of different sizes are

123 123

Table 5 AICc-based model selection to explain reptile species diversity (see bottom of the table for abbreviations of the parameters) Parameters Plot scale Patch scale Land unit scale Land scape scale Model 1 Model 2 Model 3

(A) Landscape PRC 0.251 - 1.097 - 1.135 - 1.122 Land unit PI 1.02E-06 1.49E-06 2.51E-06 2.10E-06 Patch PS 0.013 - 0.021 - 0.018 F - 0.148 - 0.293 - 0.315 - 0.318

Plot GR 0.0003 - 0.350 - 0.341 - 0.342 Author's C 0.113 - 0.030 W - 0.048 0.123 0.120 HA 0.001 - 0.044 - 0.001 PH 0.923 - 0.646 - 0.564 - 0.576 personal EX 0.004 - 0.005 - 0.005 - 0.005 RO - 0.002 - 0.044 - 1.226 SA - 0.0006 - 0.001 0.115 Interactions PRC 9 GR - 0.439 - 0.458 - 0.451

(B) copy Model selection information Adjusted R2 0.004 0.003 0.001 0.012 0.461 0.442 0.418

AICc 103.736 233.516 230.171 203.134 76.850 78.231 81.675 Delta AIC 26.885 156.665 153.320 126.284 0 1.380 4.824 Model probability (Wi) 0 0 0 0 0.34 0.28 0.21 Cumulative Wi 1 1 1 1 0.34 0.62 0.83 Relative probability 0 0 0 0 1 0.62 0.53

(A) Parameter coefficient of the three best models with a cumulative probability of 0.83 (* 0.9) and the parameter coefficient of the plot-scale, patch-scale, land unit scale and landscape-scale. (B) Model statistics: adjusted R2, AICc, delta AIC, model probability (Wi), accumulative Wi, and relative probability, for each of the models PRC precipitation, PI proximity, PS patch size, F fractal, GR grazing, C cistern, W wall, HA heap area, PH plot heterogeneity, EX exposed soil, RO rock cover percentage, SA slope aspect adcp Ecol Landscape Author's personal copy

Landscape Ecol located within a larger unit of mixed natural and References agricultural land uses, stretching along a geographical/ environmental gradient, it is not surprising that most Ackermann O, Svoray T, Haiman M (2008) Nari (calcrete) studies indeed showed that community measures and outcrop contribution to ancient agricultural terraces in the Southern Shephelah, Israel: insights from digital terrain characteristics depend on variables of different scales. analysis and a geoarchaeological field survey. J Archaeol Different scales, each with its distinctive complexity, Sci 35:930–941 forces particular ecological processes and, hence, their Anderson DR (2008) Model based inference in the life sciences: emergent patterns. a primer on evidence. Springer, New York Attum O, Eason P, Cobbs G, El Din SMB (2006) Response of a Ecological theory and empirical information indi- desert lizard community to habitat degradation: do ideas cate that the overall persistence of populations about habitat specialists/generalists hold? Biol Conserv depends on the movement of individuals between 133:52–62 remote patches (e.g., rescue effect; Brown and Kodric- Barton K (2020) MuMIn: multi-model inference. R package Benton TG, Vickery JA, Wilson JD (2003) Farmland biodi- Brown 1977). Consequently, only a heterogeneous versity: is habitat heterogeneity the key? Trends Ecol Evol landscape can successfully provide the conditions 18:182–188 critical for maintaining reptile populations. Similarly, Bonnet X, Shine R, Lourdais O (2002) Taxonomic chauvinism. habitat heterogeneity at different scales is mandatory Trends Ecol Evol 17:1–3 Bouskila A, Amitai P (2001) Handbook of amphibians and for the long-term persistence of a diverse community, reptiles of Israel. Keter Publishing House, Jerusalem comprised of populations with different ecological Brown GW, Dorrough JW, Ramsey DSL (2011) Landscape and requirements. This notion is supported by the positive local influences on patterns of reptile occurrence in grazed correlation we found between plot heterogeneity, temperate woodlands of southern Australia. Landsc Urban Plan 103:277–288 species richness and species diversity. Brown JH, Kodric-Brown A (1977) Turnover rates in insular Modern agriculture reduces habitat heterogeneity biogeography: effect of immigration on extinction. Ecol- (Robinson and Sutherland 2002) and, therefore, neg- ogy 58:445–449 atively affects the ability of reptiles to find suit- Bruton MJ, Maron M, Franklin CE, McAlpine CA (2016) The relative importance of habitat quality and landscape con- able habitats. We have already showed that reptiles text for reptiles in regenerating landscapes. Biol Conserv can move between patches, however the effective 193:37–47 permeability of the matrix to reptile movement Bruton MJ, Maron M, Levin N, McAlpine CA (2015) Testing depends on the crop type and the mosaic of natural the relevance of binary, mosaic and continuous landscape conceptualizations to reptiles in regenerating dryland habitat patches (Rotem et al. 2014; Rotem and Ziv landscapes. Landsc Ecol 30:715–728 2016). Hence, one possibility for reducing this nega- Cabrera-Guzman E, Reynoso VH (2012) Amphibian and reptile tive effect is to protect natural patches within the communities of rainforest fragments: minimum patch size agricultural fields that retain, as much as possible, the to support high richness and abundance. Biodivers Conserv 21:3243–3265 local and spatial heterogeneity (Maisonneuve and Chase JM, McGill BJ, McGlinn DJ, May M, Blowes SA, Xiao Rioux 2001). This approach is known as ‘Land X, Knight TM, Purschke O, Gotelli NJ (2018) Embracing Sharing’ and is a relatively cheap and simple method scale-dependence to achieve a deeper understanding of to protect biodiversity within an agricultural system biodiversity and its change across communities. Ecol Lett 21:1737–1751 (Green et al. 2005; Phalan et al. 2011). Our findings Chase JM, McGill BJ, Thompson PL, Anta˜o LH, Bates AE, support the employment of land sharing as an effective Blowes SA, Dornelas M, Gonzalez A, Magurran AE, Supp tool to protect biodiversity. SR, Winter M, Bjorkman AD, Bruelheide H, Byrnes JEK, Cabral JS, Elahi R, Gomez C, Guzman HM, Isbell F, Myers-Smith IH, Jones HP, Hines J, Vellend M, Waldock Acknowledgements This study was supported by a grant C, O’Connor M (2019) Species richness change across from the Israel Science Foundation (751/09) to Y.Z. We thank spatial scales. Oikos 128:1079–1091 our assistants for their help in the field and the farmers of Connor EF, Courtney AC, Yoder JM (2000) Individuals-area Kibbutz Beit-Nir, Moshav Lachish and Kibbutz Dvir for their relationships: the relationship between animal population cooperation. This study was conducted under Israel National density and area. Ecology 81:734–748 Parks Authority permits number 38096/2011. This is Connor EF, McCoy ED (1979) The statistics and biology of the publication no. 1082 of the Mitrani Department of Desert species-area relationship. Am Nat 133:791–833 Ecology. Corn PS, Bury RB (1990) Sampling methods for terrestrial amphibians and reptiles. U.S. For Serv. https://doi.org/10. 2737/PNW-GTR-256

123