Parasite beta diversity, host beta diversity and ... - Wiley Online Library

Journal of Biogeography (J. Biogeogr.) (2017) 44, 1880–1890

ORIGINAL ARTICLE

Parasite beta diversity, host beta diversity and environment: application of two approaches to reveal patterns of flea species turnover in Mongolia Renan Maestri1

Laboratorio de Citogenetica e Evolucß~ao, Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Av. Bento Goncßalves 9500, Porto Alegre, Rio Grande do Sul CEP: 91501-970, Brazil, 2Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000 Midreshet Ben-Gurion, Israel 1

, Georgy I. Shenbrot2 and Boris R. Krasnov2,*

ABSTRACT

Aim We investigated the beta-diversity patterns of fleas parasitic on rodents along environmental and host turnover gradients using linear and nonlinear approaches. We asked (1) which factors explain a larger proportion of the variation in flea beta diversity and (2) whether the results of the linear versus the nonlinear approach are similar in indicating the relative roles of environment versus host turnover. Location Mongolia. Methods The linear approach was represented by a partial linear regression of flea turnover against rodent turnover, environmental variables and a spatial term. The nonlinear approach was represented by generalized dissimilarity modelling (GDM) with the response variable being the dissimilarity of flea composition and the predictors being dissimilarity in rodent composition, dissimilarity in environmental variables and geographic distances between localities. To test for the response of rodent beta diversity to environmental gradients, this factor was used as a response variable/matrix. Results Partial regression analyses explained only 24% of observed variance. Environmental variables (mainly air temperature) and rodent turnover independently explained similarly small portions of flea turnover. Gradients of air temperature and rodent turnover were the most important factors in explaining flea turnover across space, whereas the altitudinal gradient and the gradient of annual variation in precipitation were the most important in explaining rodent turnover. The GDM resulted in 68.4% of explained deviance. The best predictor of flea species turnover was the air temperature gradient followed by rodent host beta diversity and, to a lesser degree, the precipitation gradient. The responses of rodent and flea turnover to environmental gradients differed.

*Correspondence: Boris R. Krasnov, Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000 Midreshet Ben-Gurion, Israel. E-mail: [email protected]

Main conclusions Air temperature and rodent host turnover were the main factors affecting flea beta diversity with the latter responding to climate directly and not being mediated by host responses. Accommodation of the nonlinearity of species turnover responses to gradients allows patterns obscured by linear approaches to be revealed. Keywords beta diversity, fleas, generalized dissimilarity modelling, Mongolia, parasites, partial linear regression, rodents, species turnover

INTRODUCTION One of the major aims of ecology is to understand the assembly rules of biological communities. Insights into

1880

http://wileyonlinelibrary.com/journal/jbi doi:10.1111/jbi.13025

environmental and geographic patterns of community composition greatly facilitate this understanding (e.g. Buckley & Jetz, 2008). It is thus not surprising that patterns of spatial variation in community composition (i.e. beta diversity) and

ª 2017 John Wiley & Sons Ltd

Parasite beta diversity along gradients processes that underlie these patterns have attracted much attention during the last two decades (Lennon et al., 2001; Koleff et al., 2003; Baselga, 2010). Spatial and environmental determinants of beta diversity have been studied in a variety of plant (Condit et al., 2002; Svenning & Skov, 2007) and animal (Baselga, 2008; Melo et al., 2009) communities in terrestrial (Maestri & Patterson, 2016), marine (Thrush et al., 2010) and freshwater environments (Pool et al., 2014). The majority of these studies dealt with free-living organisms and considered variation in their species composition along both abiotic (Bishop et al., 2015) and biotic (Sobek et al., 2009) gradients, as well as geographic distance (Qian, 2009). Parasitic organisms received less attention, although they represent a substantial proportion of global biodiversity (Poulin & Morand, 2000; Poulin, 2014). Nevertheless, parasites are convenient and important models to study the spatial variation in beta diversity for the following reasons. Their convenience is related to the facts that parasites of the same taxon share a trophic level and that the resource niche of the majority of parasite taxa can easily be defined as an array of host species they exploit. This may allow the avoidance of cases in which species composing a community differ sharply in their traits and requirements and respond differently to environmental gradients, thus masking the real patterns of community composition (Buckley & Jetz, 2008). The importance of investigations into parasite beta diversity is related to their role as agents and vectors of diseases of humans, livestock and wildlife, so that understanding the spatial turnover of parasitic species will facilitate the mapping, prediction and prevention of these diseases and assessment of the risks associated with them (Caminade et al., 2014). Furthermore, contrary to free-living species, the environment of parasites is ‘dual’. It is represented by both their hosts that provide parasites not only with food but also with a place to live, and by abiotic factors. This ‘duality’ is especially true for ectoparasites that are strongly affected by offhost environmental factors (e.g. Krasnov et al., 2001). The relative effects of these two groups of factors differ both between parasite taxa (Krasnov et al., 2005 vs. Vinarski et al., 2007) and spatial scales (Krasnov et al., 2015). Most (if not all) studies of the spatial patterns of parasite beta diversity used the so-called distance approach (Nekola & White, 1999; Soininen et al., 2007) by plotting pairwise (dis)similarities in community composition against pairwise geographical distance or environmental/host composition (dis)similarities (e.g. Poulin, 2003; Vinarski et al., 2007; Seifertova et al., 2008; Thieltges et al., 2009). These studies produced contradictory results with patterns of, for example, the relationship between community similarity (compositional or phylogenetic) and geographic distance differing between parasite taxa (Krasnov et al., 2005 vs. Vinarski et al., 2007), parasites with different life cycles or different intermediate hosts (Fellis & Esch, 2005a; Thieltges et al., 2009), spatial scales (Fellis & Esch, 2005a vs. Fellis & Esch, 2005b) or regions (Krasnov et al., 2012). The reasons for these contradictions could be both biological (Fellis & Esch, 2005a) and Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

methodological. In particular, the major methodological issue in studies of beta-diversity patterns is that the rate of species turnover along gradients is assumed to be linear, so that a linear approach to the analysis could be applied (Qian et al., 2005; Maestri & Patterson, 2016). However, a linear approach represents not only a statistical (see details in Ferrier et al., 2002, 2007; Fitzpatrick et al., 2013) but also, importantly, a biological problem because species composition along different parts of a given environmental gradient may vary at different rates. As a result, the effect of, for example, environment on beta diversity can be underestimated (Fitzpatrick et al., 2013). To overcome this problem, a novel method, generalized dissimilarity modelling (GDM), has recently been developed (Ferrier et al., 2007; see details below). However, the results of the application of either a linear or a nonlinear approach to the same set of parasite data have never been compared. Here, we used both linear (partial linear regressions) and nonlinear (GDM) approaches to study the beta-diversity patterns of fleas parasitic on rodents across Mongolia. The Mongolian territory was comprehensively surveyed for both rodents and fleas (see Appendix 1 in the Supporting Information). We asked (1) which factor (environment or host beta diversity) explains a larger proportion of the variation in flea beta diversity and (2) whether the results of the linear versus the nonlinear approach are similar in indicating the relative roles of environment versus hosts. In addition, we asked (3) whether rodent host turnover along environmental gradients might be a mediator of flea turnover along these gradients by comparing patterns of rodent and flea beta diversity. Fleas are characteristic parasites of small mammals, being most diverse and abundant on rodents. They are obligate haematophages that usually alternate between periods when they occur on the host body and periods when they occur in their burrow or nest. In most cases, pre-imaginal development is entirely off-host. Larvae are usually not parasitic and feed on organic debris found in the burrow and/or nest of a host (Krasnov, 2008). Earlier studies on the spatial patterns of ectoparasite beta diversity were carried out using data from surveys of a number of isolated regions across a large (e.g. continental) scale (Vinarski et al., 2007; Krasnov et al., 2010, 2012). Here, we considered flea turnover at a smaller scale and across a continuous area because small-scale habitat transitions might reveal patterns obscured at larger scales. MATERIALS AND METHODS Data on flea and rodent distribution Occurrence records for flea (67 species) and rodent (67 species) distribution in Mongolia and adjacent areas were taken from the GBIF (Global Diversity Information Facility, http:// www.gbif.org), from the database of the Zoological Museum of Moscow State University and from the literature (see 1881

R. Maestri et al. Appendix S1 in Supporting Information). Rodents and fleas were sampled at 2370 localities (Fig. 1) evenly distributed across the Mongolia (data from museum databases and literature sources; see Appendix S1). In total, more than 20,000 rodents were captured and more than 15,000 fleas were collected (M. Kiefer, personal communication; see also references in Appendix S1). Coordinates of occurrence points with the original GPS data were used as is; records that had no original GPS coordinates were georeferenced using Geographic Names Gazetteers available at http://earth-info.nga. mil/gns/html/cntry_files.html and old Soviet (http://maps.vla senko.net) and military topographic maps and checked for suitable habitats using Google Earth. Detailed explanation on compilation of datasets can be found in Appendix S2. Data on altitude and climatic variables We extracted variables describing altitude (one variable) and climate (temperature and precipitation; 19 variables, see Table 1) from the WORLDCLIM Version 1.4 (BIOCLIM) database (Hijmans et al., 2005). Altitude reflects landscape gradient. Climatic variables strongly correlated with each other (|r| = 0.55–0.98), but their correlation with altitude was weaker (|r| = 0.06–0.46). Consequently, we applied a principal component analysis to climatic variables and extracted four principal components that were then used as climatic gradients. The principal component analysis of the 19 climatic variables resulted in four principal components that explained 91.4% of the total variance (31.0%, 20.3%, 22.8% and 17.2%, respectively) (Table 1). The first and the second principal components (PC1 and PC2, respectively) represented variation in air temperature and its range,

whereas the third and the fourth principal components (PC3 and PC4) reflected variation in precipitation (Table 1). Approach 1. Partial linear regressions We calculated flea and rodent turnover across space using a gridding approach (Melo et al., 2009; Maestri & Patterson, 2016). We mapped the binary raster grids created for each species onto a map of Mongolia divided into a grid of 0.25° by 0.25° cells. This resolution was chosen to maximize the information on rodent and flea distribution (based on point occurrences) and provide reliable estimates of geographical distribution for rodents and fleas. It also assumes within-cell homogeneity in environment as well as in rodent and flea species composition (Dorzhgotov, 2009 and personal field experience of G. Shenbrot). Occurrence records for each species were then transformed into a site (=cell)-by-species matrix with the presence/absence of each species in each cell. Matrices for flea and rodent presence/absence were constructed separately. Then, each matrix was used to calculate turnover across cells. Species turnover was calculated using the Simpson-based dissimilarity index (bSIM) = min(b,c)/ a+min(b,c), where a is the number of species shared by two cells, and b and c are the number of species unique to each cell (Baselga, 2010). This index is characterized by low sensitivity to differences in species richness between cells (Lennon et al., 2001). For each given cell, a turnover value was calculated as the mean of turnover values between this cell and each of its eight adjacent cells (Melo et al., 2009; Maestri & Patterson, 2016). Species turnover was calculated using R 3.3.0 (R Core Team, 2016) with the packages ‘betapart’ (Baselga et al., 2013) and ‘CommEcol’ (Melo, 2016).

Figure 1 Map of sampling localities of rodents and fleas across Mongolia.

1882

Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

Parasite beta diversity along gradients Table 1 Summary of the principal component analysis of the 19 climatic variables in Mongolia. Linear correlation with each principal component Variable Mean annual temperature Mean diurnal range of temperature Isothermality Temperature seasonality Maximal temperature of the warmest month Minimal temperature of the coldest month Annual temperature range Mean temperature of the wettest quarter Mean temperature of the driest quarter Mean temperature of the warmest quarter Mean temperature of the coldest quarter Annual precipitation Precipitation of the wettest month Precipitation of the driest month Precipitation seasonality Precipitation of the wettest quarter Precipitation of the driest quarter Precipitation of the warmest quarter Precipitation of the coldest quarter

PC1

PC2

PC3

PC4

0.92 0.13

0.15 0.26

0.29 0.07

0.18 0.09

0.04 0.12 0.90

0.92 0.98 0.25

0.02 0.05 0.26

0.08 0.07 0.17

0.79

0.54

0.17

0.16

0.09 0.92

0.94 0.24

0.04 0.26

0.04 0.17

0.78

0.46

0.30

0.16

0.92

0.24

0.26

0.17

0.77

0.56

0.24

0.18

0.37 0.35 0.13 0.16 0.35 0.25 0.35

0.09 0.08 0.07 0.10 0.07 0.16 0.07

0.85 0.90 0.39 0.59 0.90 0.35 0.90

0.35 0.22 0.88 0.69 0.22 0.87 0.22

0.26

0.17

0.32

0.88

We analysed the effects of rodent turnover and climatic variables on flea turnover using a partial linear regression (Diniz-Filho & Bini, 2005). The response variable was flea species turnover, whereas the explanatory variables were rodent species turnover, environmental variables (altitude and climatic variables PC1 to PC4) and a spatial term to account for spatial autocorrelation in the model. The spatial term consists of eigenvectors extracted from a principal coordinate analysis (PCoA) conducted on a truncated distance matrix connecting all sites (Borcard & Legendre, 2002). The truncation distance was defined under a minimum spanning tree criterion (Rangel et al., 2006), and the eigenvectors to be used in the model were selected based on the minimization of Moran’s I residuals (Diniz-Filho & Bini, 2005). In addition, we evaluated the response of rodent beta diversity to environmental gradients by running a partial linear regression with rodent turnover as the response variable and the environmental variables and spatial filters as predictors. If rodents respond in the same way as fleas to environmental gradients, it would suggest flea turnover is mediated by the turnover of their hosts. The partial regression analyses and the extraction of spatial eigenvectors were done using the package ‘vegan’ (Oksanen et al., 2016) implemented in R 3.3.0. Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

Approach 2. Generalized dissimilarity modelling Our second approach was the implementation of GDM for analysing the relationship between flea beta diversity and (1) rodent beta diversity, (2) environmental gradients and (3) geographic distance. GDM (Ferrier et al., 2002, 2007) is a nonlinear extension of the matrix regression technique that was originally aimed at analysing turnover in the species composition of communities along environmental gradients and geographic distances. This technique was further adapted for testing the relationships between the beta diversities of two taxa (Jones et al., 2013) and for the analysis of the spatial structure of phylogenetic, rather than compositional, beta diversity (Rosauer et al., 2014). The main advantages of GDM, in comparison with earlier linear approaches to studying the spatial patterns of beta diversity (e.g. MRM; Lichstein, 2007), is that it takes into account two types of nonlinearity often occurring in ecological data (Ferrier et al., 2007). First, the rate of compositional turnover along environmental gradients is rarely constant but rather varies among different parts of the gradient (e.g. Oksanen & Tonteri, 1995). Second, the relationship between pairwise between-site compositional dissimilarity and environmental/ spatial separation is curvilinear rather than linear especially in datasets with high levels of between-site dissimilarity (i.e. when many site pairs do not share any species) (Ferrier et al., 2007). The GDM deals with the variation in the turnover rate along each gradient by transformation of each of the predictor variables using an iterative maximum-likelihood estimation and I-splines (Ferrier et al., 2007; Fitzpatrick et al., 2013). The maximum height of each plotted Ispline characterizes the total amount of turnover associated with a given gradient, while all other predictors are held constant, so that the I-splines are partial regression fits, showing the importance of each predictor’s effect on compositional turnover. The slope of the I-spline demonstrates the turnover rate and, importantly, its variation along a given gradient. To account for the curvilinear relationship between compositional dissimilarity and environmental/spatial separation, the linear predictor variable is transformed via a link function that defines the relationship between compositional pairwise between-site dissimilarities (constrained to the range 0–1) and a scaled combination of between-site distances based on any number of environmental or geographical variables (see Ferrier et al., 2007 for details). To run the GDMs, we used the package ‘gdm’ (Manion et al., 2016) implemented in R. We calculated the Sørenson dissimilarity of flea species and rodent species composition (separately) between each pair of cells using the package ‘vegan’ in R. Then, we fitted the GDM model using the default of three I-splines. The response matrix in the main GDM was pairwise dissimilarity in flea composition, whereas the predictors were (1) pairwise dissimilarity in rodent species composition, (2) dissimilarity in environmental variables (mean altitude and four principal components extracted from 19 climatic variables; see below) and (3) geographic 1883

R. Maestri et al. distances between the geometric centres of each pair of cells. The figures represent fitted functions only, whereas data points are not shown due to the extremely large number of these points (4,108,411). Then, we performed separate GDM runs with either each of the predictors (geographic distance, environmental variables or rodent beta diversity) or their combinations (geographic distance and environmental variables, geographic distance and rodent beta diversity, environmental variables and rodent beta diversity). We also performed additional GDM runs to estimate the ‘pure’ effect of host, environmental and geographic predictors. We extracted a ‘pure’ effect for each predictor as follows. We calculated the explained deviance for the entire GDM run with all predictors and for the GDM runs without one of the predictors (predictor of interest). Then the ‘pure’ effect of this predictor of interest was obtained similarly to the decomposition of Legendre & Legendre (1998), dedicated to multiple fractions of explanations (see also Borcard et al., 1992; Lichstein, 2007; Krasnov et al., 2010). We expressed the fraction of deviance explained by each ‘pure’ effect as a percentage of the total explained deviance. Finally, to understand whether the response of flea beta diversity to environmental gradients is mediated by the beta diversity of their hosts (see above), we performed a GDM run with pairwise between-site rodent dissimilarity as the response matrix and environmental variables and geographic distance as predictors. As mentioned above, similar responses of flea and rodent dissimilarities to environmental dissimilarity and/or geographic distance would suggest the mediating role of the host communities. RESULTS Partial regression analyses Partial regression analyses revealed that a great portion of the variation (c. 76%) in flea turnover remained unexplained (Table 2), suggesting that the linear models accounted only for a minor part of flea turnover. The explained portion was divided between rodent turnover alone (10.34%) and environmental variables alone (8.02%; mainly PC1 and PC3, see Table 3). Environmental variables still shared part of the explained variation with geographical space (6.29%). In other words, environmental variables and rodent turnover independently explained similar small portions of flea turnover (14.31% and 10.34%, respectively), with no sharing between them. The negligible shared explanation between rodent turnover and environmental variables suggested that the fleas’ response to the environment differed from that of their rodent hosts. The partial regression with rodent turnover as a response variable resulted in 16.03% of the variance explained by environmental variables, and only 0.77% explained by the shared contribution of environment and space. In this model, 83.20% of the variance remained unexplained. When 1884

Table 2 Summary of the partial linear regression results with flea turnover in Mongolia as the response variable and rodent turnover, environment variables (altitude and climatic variables PC1 to PC4) and spatial filters as predictors. Shared fractions are found by subtracting different models, and thus can be negative (Oksanen et al., 2016). These negative values can be interpreted as zeroes, and no F-statistics can be applied. Predictor

r²adj

Rodent turnover Environment Spatial filters Environment 9 spatial filters shared Rodent turnover 9 spatial filters shared Rodent turnover 9 environment shared All shared Residuals Total

0.1034 0.0802 0.0036 0.0629 0.0007

F

P

325.39 107.93 14.72

< 0.001 < 0.001 < 0.001

0.0032 0.0127 0.7665 1.0000

Table 3 Standard coefficients from the partial regressions for the relationship between flea or rodent turnover in Mongolia with environmental variables (altitude and climatic variables PC1 to PC4). Rodent turnover entered as a predictor variable in the partial regression of flea turnover as a response (see Table 2). Predictor Rodent turnover Altitude PC1 PC2 PC3 PC4

Flea turnover 3.1192 1.6059 6.4301 1.3177 2.3420 1.7609

Rodent turnover – 7.6140 4.5136 1.3780 1.6519 6.5692

rodent turnover entered as a response variable in the model, the analysis revealed that the environmental variables that influenced species turnover to the greatest extent differed between fleas and rodents. The air temperature gradient (PC1) was the most important in explaining flea turnover across space, whereas altitudinal gradient and the gradient of annual variation in precipitation (PC4) were the most important in explaining rodent turnover (Table 3). Generalized dissimilarity modelling The GDM of flea beta diversity when geographic distance, environmental gradients and rodent beta diversity were taken into account resulted in 68.4% of the explained deviance. The best predictor of flea species turnover across Mongolia was the air temperature gradient followed by rodent host beta diversity and, to a lesser degree, the gradient in precipitation (Table 4). On the contrary, flea beta diversity did not respond (or weakly responded) to geographic distance, altitude as well as annual variation in air temperature and precipitation. Geographic distance alone explained a negligible Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

Parasite beta diversity along gradients Table 4 Coefficients of the I-splines from the GDM of the dependence of flea beta-diversity in Mongolia on geographic distance, environmental variables (altitude and climatic variables PC1 to PC4) and rodent host beta diversity. I-Spline Gradient

1

2

3

Sum of coefficients

Geographic distance Altitude PC1 PC2 PC3 PC4 Rodent beta diversity

0.06 0 1.72 0.18 0.81 0 0.003

0 0 0.18 0 0.06 0 0

0 0 0 0 0 0 1.19

0.06 0 1.90 0.18 0.87 0 1.12

portion of the total deviance, whereas environment alone and compositional rodent dissimilarity alone explained substantial portions of the deviance, although the former explained less deviance than the latter (Table 5). Environmental variables and rodent beta diversity, taken together, explained almost the same portion of the total deviance as the whole model (Table 5). Similarly, the ‘pure’ effect was strongest for rodent turnover either alone or combined with other predictors (Table 6). The dissimilarity of flea species composition increased steeply with an increase in ecological dissimilarity until some threshold and then slowed down (Fig. 2). The rate of flea species turnover was greater at locations with higher air temperatures and precipitation (note signs of correlations between the original climatic variables and the principal components; Table 1) and then slowed down (Fig. 3). Flea turnover was steady at low and medium values of rodent turnover, and then, its rate increased substantially at high values of rodent turnover (Fig. 4). When rodent dissimilarity was introduced into the analysis as a response matrix and geographic distance and environmental variables as predictors, it appeared that the responses

Space only Environment only Hosts only Space and environment Space and hosts Hosts and environment

I-Spline Gradient

1

2

3

Sum of coefficients

Geographic distance Altitude PC1 PC2 PC3 PC4

0.26 0.16 0.97 0 0.81 0

0.07 0 0.31 0 0.82 0.47

0 0.15 0.39 0 0 0.10

0.34 0.31 1.67 0 1.63 0.57

Figure 2 Relationship between observed pairwise between-cell dissimilarity in flea species composition and the linear predictor of the regression equation from GDM (predicted pairwise between-cell ecological distance).

% of total deviance explained

% of explained deviance due to ‘pure’ effect

of rodent and flea turnover to environmental gradients differed. First, climatic variables and geographic distances explained a lower percentage of the total deviance for rodent turnover than for flea turnover (37.86% and 49.76%, respectively). Second, geographic distances, altitude and variation in precipitation (PC4) were substantially better predictors of rodent turnover than flea turnover (Table 6). Third, the shape of the I-splines of the effect of temperature and precipitation gradients on species turnover differed between rodents and fleas (compare Figs 3 & 5).

< 0.01 48.08

0.16 18.30

DISCUSSION

54.73 49.76

27.28 20.02

55.91

29.74

68.32

99.9

Table 5 Generalized dissimilarity model deviance in flea beta diversity in Mongolia explained separately by distance matrices of environmental variables (environment), geographic distance (space) and rodent beta diversity (hosts) and their combinations. Predictor

Table 6 Coefficients of the I-splines from the GDM of the dependence of rodent beta diversity in Mongolia on geographic distance, altitude and climatic variables (PC1 to PC4).


The application of both linear and nonlinear methods to Mongolian data demonstrated that (1) air temperature and rodent host turnover were the main factors affecting flea beta diversity and (2) the shape of rodent turnover along environmental gradients differed from that of flea turnover. Furthermore, comparing the results of linear versus nonlinear modelling indicated that the latter provided a substantially 1885

R. Maestri et al.

Figure 3 Generalized dissimilarity model-fitted I-splines (partial regression fits) of (a) temperature-related (PC1) and (b) precipitation-related (PC3) principal components as predictors of flea species turnover across Mongolia. The steeper slope of the transformed relationship on a given section of the gradient indicates greater rate of a turnover.

Figure 4 Generalized dissimilarity model-fitted I-spline (partial regression fit) of rodent species turnover as a predictor of flea species turnover across Mongolia.

better fit to the data than the former and allowed important patterns to be revealed. Flea beta diversity and climatic variables Both linear and nonlinear analyses demonstrated that air temperature contributed strongly to flea turnover. Moreover, 1886

Figure 5 Generalized dissimilarity model-fitted I-splines (partial regression fits) of (a) temperature-related (PC1) and (b) precipitation-related (PC3) principal components as predictors of rodent species turnover across Mongolia.

the rate of flea turnover was the highest at higher temperatures suggesting that flea species composition differed between locations characterized by higher temperatures more strongly than that between locations with lower temperatures. Air temperature affects both pre-imaginal and adult fleas. For example, the duration of pre-imaginal development is strongly temperature dependent (Heeb et al., 2000; Krasnov et al., 2001; Kreppel et al., 2016), whereas adult fleas respond to changes in ambient temperature by changes in their metabolic rate (Fielden et al., 2004), feeding activity (Gong et al., 2004) and sperm transfer (Dean & Meola, 2002). Flea temperature preferences are often species specific and may differ not only among distantly-related fleas (Kreppel et al., 2016) but also among congenerics (Krasnov et al., 2001). This variation in species-specific temperature preferences can explain why species composition in flea assemblages varies between locations characterized by different temperatures. For example, in Madagascar, Synopsyllus fonquerniei is found mainly in the cooler highland regions, whereas Xenopsylla cheopis inhabits warmer areas (Kreppel et al., 2016). The developmental threshold of S. fonquerniei was found to be 3.1 °C lower than that of X. cheopis, allowing the former, but not the latter, to occupy colder habitats. Moreover, the temperature preferences of many flea species, albeit variable, lie at relatively higher temperature ranges, whereas fewer fleas prefer lower temperature (see Krasnov, 2008, and references therein). As a result, flea assemblages in warmer locations are more diverse than those in colder locations (Krasnov et al., 2005). The higher within-location Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

Parasite beta diversity along gradients diversity of flea assemblages in warmer locations may thus allow a higher between-location rate of species turnover as compared with that between colder locations. We also found some moderate effect of the precipitation gradient on flea beta diversity. The pattern of flea turnover along this gradient was similar to that along the temperature gradient (a higher rate at higher precipitation), but the magnitude of total change along the precipitation gradient was two times lower than that along the temperature gradient, indicating a lower relative importance of the former. Precipitation is directly associated with relative humidity in host burrows, which, in turn, has a direct effect on fleas. Humidity is especially important for larval fleas because they cannot close their spiracles, and thus are extremely sensitive to low humidity (Mellanby, 1933). Nevertheless, relative humidity is important for adult fleas as well and may affect their oviposition rate (Vashchenok, 1993). In general, the majority of flea species avoid low humidity, so the mechanism of the response of flea turnover to the precipitation gradient is similar to that along the temperature gradient. Given the strong direct effects of climatic variables on terrestrial ectoparasites belonging to other taxa (see review in Marshall, 1981), one may expect similar responses of their beta diversity to environmental gradients. Moreover, increased air temperature and decreased precipitation under global climate change scenarios could affect the pattern of flea species turnover across space and cause changes in spatial distribution of pathogens transmitted by flea vectors (most notably, plague). Importantly, the effect of environmental gradients on flea turnover was direct rather than being mediated by the response of their hosts to these gradients. In contrast to flea turnover, rodent turnover responded to geographic distance, altitude and annual variation in precipitation. In addition, the shape of the relationships between turnover and the temperature and precipitation gradients differed between fleas and rodents. Flea beta diversity and host beta diversity The effect of host beta diversity on flea beta diversity is not especially surprising. It has been reported earlier that a similarity in parasite assemblages correlates positively with a similarity in host assemblages (Krasnov et al., 2005, 2010; Vinarski et al., 2007). In other words, parasite beta diversity is expected to follow host beta diversity due to the obvious reasons that (1) the main habitat of a parasite is its host, and (2) even highly host opportunistic parasites are able to exploit only a limited ensemble of host species. However, this pattern may differ at different scales and depending on the definition of the ‘assemblage’. For example, Vinarski et al. (2007) found that the between-location similarity of component communities (i.e. the assemblage of parasites in a population of conspecific hosts; see definitions in Poulin, 2007) of gamasid mites was weakly (if at all) affected by the between-location similarity in the species composition of all small mammals, whereas the between-location similarity of Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

compound communities (i.e. the assemblage of parasites in the populations of all host species) of these parasites correlated strongly with similarity in host species composition. In this study, flea assemblages in a locality (= cell) undoubtedly represent a compound community, so the pattern revealed by both the linear and nonlinear analyses conform to that found by Vinarski et al. (2007). However, the GDM indicated that the rate of flea turnover along the host turnover gradient is not constant. Instead, it is rather slow at relatively low values of rodent turnover and rapid at its high values. In other words, flea turnover abruptly increased at some threshold level of host dissimilarity. One of the reasons behind this pattern of flea turnover might be that host assemblages at the left end of the host turnover gradient share many species and are thus not dissimilar enough from the fleas’ perspective. Indeed, given the rather loose host specificity of the majority of flea species (Krasnov, 2008), many host species can be almost equally suitable for a given flea species (e.g. Shenbrot et al., 2007). However, at some level of host compositional dissimilarity (when the number of shared species declines to some threshold value and continues to drop reaching zero), fleas start to perceive a difference between host assemblages and respond to this difference by changing the species composition of their communities. We recognize, however, that this explanation is rather speculative and warrants further investigation. Relative effects of environment versus host beta diversity Both linear and nonlinear analyses demonstrated that host beta diversity explained more variation in flea turnover than environmental variables did. This is rather unexpected because our earlier studies demonstrated that flea assemblages in a locality within a region are mainly assembled via environmental filters and shaped to a lesser extent by host species composition, although the latter definitely plays a role (Krasnov et al., 2015). It is thus possible that parasite alpha and beta diversity are governed by different rules. The structure of host assemblages represents a more important factor than environment when affecting parasite beta diversity, whereas environment is a more important factor than host species composition when affecting parasite alpha diversity at a local scale (Krasnov et al., 2015). Nevertheless, the relative effects of environment versus host species composition may differ within a parasite taxon between groups of species with different ecological characteristics. For example, Krasnov et al. (2010) found that dissimilarity in the species composition of highly host-specific fleas across the Palearctic was mainly affected by environmental dissimilarity, whereas dissimilarity for host opportunistic fleas was affected mainly by dissimilarity in host species composition. These relative effects may also differ depending on whether component or compound (see above) parasite assemblages are considered. The influence of environmental dissimilarity on the beta diversity of component communities of gamasid mites was 1887

R. Maestri et al. more important than that of host turnover, whereas the opposite was true for their compound communities (Vinarski et al., 2007).

Ecology. The doctoral fellowship of R.M. was supported by CAPES-Brazil. REFERENCES

Distance decay of similarity is not universal Surprisingly, the GDM demonstrated no effect of geographic distance on flea beta diversity, although this factor, to some extent, affected host beta diversity. The distance decay of similarity had earlier been considered as one of the most pervasive biogeographic patterns holding for both free-living and parasitic organisms (Nekola & White, 1999; Soininen et al., 2007; Poulin & Krasnov, 2010). However, during the last decade, more and more studies have suggested that the distance decay of similarity is not as universal as was previously thought. For example, Oliva & Gonzalez (2005) tested for the distance decay of similarity in parasite assemblages of four species of marine fish and did not find this pattern in one of these species. Perez-del-Olmo et al. (2009) reported the distance decay of similarity in species composition for infracommunities (= assemblages of parasites harboured by a host individual), but not for component communities of the teleost fish Boops boops along the coast of Spain. Vinarski et al. (2007) found that the similarity in gamasid mite assemblages was generally unaffected by geographic distance. In some cases, the lack of the distance decay of similarity could be attributed to the small scale of consideration (Nakaoka et al., 2006). However, in other cases, the distance decay of similarity results merely from covariation between geographic distance and, for example, environmental differences, so the ‘pure’ effect of geographic distance on community dissimilarity was found to be minor (if not absent) (Jones et al., 2013). Linear versus nonlinear approach One of the most important results of our study is that the nonlinear models fitted the data substantially better (i.e. explained substantially more variation) than the linear models. Despite the fact that both approaches produced qualitatively similar results, the nonlinear approach allowed us to understand better the behaviour of flea turnover along environmental and host-related gradients. In particular, it became evident that the rates of flea turnover along these gradients are not constant but rather vary in different portions of the gradients. The comparison of the species turnover patterns along gradients, revealed by linear and nonlinear modelling, suggests that the rate of this turnover may likely be overestimated or underestimated or both when the former is used. ACKNOWLEDGEMENTS This study was partly supported by the Israel Science Foundation (grant number 26/12 to B.R.K.). This is publication no. 926 of the Mitrani Department of Desert 1888

Baselga, A. (2008) Determinants of species richness, endemism and turnover in European longhorn beetles. Ecography, 31, 263–271. Baselga, A. (2010) Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19, 134–143. Baselga, A., Orme, D., Villeger, S., de Bortoli, J. & Leprieur, F. (2013) betapart: partitioning beta diversity into turnover and nestedness components. R package version 1.3. https:// CRAN.R-poject.org/package=betapart. Accessed April 2017. Bishop, T.R., Robertson, M.P., van Rensburg, B.J. & Parr, C.L. (2015) Contrasting species and functional beta diversity in montane ant assemblages. Journal of Biogeography, 42, 1776–1786. Borcard, D. & Legendre, P. (2002) All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153, 51–68. Borcard, D., Legendre, P. & Drapeau, P. (1992) Partialling out the spatial component of ecological variation. Ecology, 73, 1045–1055. Buckley, L.B. & Jetz, W. (2008) Linking global turnover of species and environments. Proceedings of the National Academy of Sciences USA, 105, 17836–17841. Caminade, C., Kovats, S., Rocklov, J., Tompkins, A.M., Morse, A.P., Col on-Gonzalez, F.J., Stenlund, H., Martens, P. & Lloyd, S.J. (2014) Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences USA, 111, 3286–3291. Condit, R., Pitman, N., Leigh, E.G., Jr, Chave, J., Terborgh, ~ez, P., Aguilar, S., Valencia, R., Villa, J., Foster, R.B., N un G., Muller-Landau, H.C., Losos, E. & Hubbell, S.P. (2002) Beta-diversity in tropical forest trees. Science, 295, 666– 669. Dean, S.R. & Meola, R.W. (2002) Factors influencing sperm transfer and insemination in cat fleas (Siphonaptera: Pulicidae) fed on an artificial membrane system. Journal of Medical Entomology, 39, 475–479. Diniz-Filho, J.A.F. & Bini, L.M. (2005) Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecology and Biogeography, 14, 177–185. Dorzhgotov, D. (ed) (2009) National atlas of Mongolia, 2nd edn. Mongol Ulsyn Shinzhl_ekh Ukhaany Akademi, Gazarzu̇ ˘ın Khu̇ r_ee_ l_en, Ulaanbaatar [in Mongolian]. Fellis, K.J. & Esch, G.W. (2005a) Autogenic-allogenic status affects interpond community similarity and species-area relationship of macroparasites in the bluegill sunfish, Lepomis macrochirus, from a series of freshwater ponds in the Piedmont area of North Carolina. Journal of Parasitology, 91, 764–767. Fellis, K.J. & Esch, G.W. (2005b) Variation in life cycle affects the distance decay of similarity among bluegill Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

Parasite beta diversity along gradients sunfish parasite communities. Journal of Parasitology, 91, 1484–1486. Ferrier, S., Drielsma, M., Manion, G. & Watson, G. (2002) Extended statistical approaches to modeling spatial pattern in biodiversity in north-east New South Wales: II. Community-level modeling. Biodiversity and Conservation, 11, 2309–2338. Ferrier, S., Manion, G., Elith, J. & Richardson, K. (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13, 252–264. Fielden, L.J., Krasnov, B.R., Khokhlova, I.S. & Arakelyan, M.S. (2004) Respiratory gas exchange in the desert flea Xenopsylla ramesis (Siphonaptera: Pulicidae): response to temperature and blood-feeding. Comparative Biochemistry and Physiology A, 137, 557–565. Fitzpatrick, M.C., Sanders, N.J., Normand, S., Svenning, J.-C., Ferrier, S., Gove, A.D. & Dunn, R.R. (2013) Environmental and historical imprints on beta diversity: insights from variation in rates of species turnover along gradients. Proceedings of the Royal Society B: Biological Sciences, 280, 20131201. Gong, Y.-L., Li, Z.-L. & Ma, L.-M. (2004) Further research of the bloodsucking activities of the flea Citellophilus tesquorum sungaris. Acta Parasitologica et Medica Entomologica Sinica, 11, 47–49 [in Chinese]. Heeb, P., K€ olliker, M. & Richner, H. (2000) Bird-ectoparasite interactions, nest humidity, and ectoparasite community structure. Ecology, 81, 958–968. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978. Jones, M.M., Ferrier, S., Condit, R., Manion, G., Aguilar, S. & Perez, R. (2013) Strong congruence in tree and fern community turnover in response to soils and climate in Central Panama. Journal of Ecology, 101, 506–516. Koleff, P., Gaston, K.J. & Lennon, J.J. (2003) Measuring beta diversity for presence-absence data. Journal of Animal Ecology, 72, 367–382. Krasnov, B.R. (2008) Functional and evolutionary ecology of fleas. A model for ecological parasitology. Cambridge University Press, Cambridge. Krasnov, B.R., Khokhlova, I.S., Fielden, L.J. & Burdelova, N.V. (2001) The effect of air temperature and humidity on the survival of pre-imaginal stages of two flea species (Siphonaptera: Pulicidae). Journal of Medical Entomology, 38, 629–637. Krasnov, B.R., Shenbrot, G.I., Mouillot, D., Khokhlova, I.S. & Poulin, R. (2005) Spatial variation in species diversity and composition of flea assemblages in small mammalian hosts: geographic distance or faunal similarity? Journal of Biogeography, 32, 633–644. Krasnov, B.R., Mouillot, D., Shenbrot, G.I., Khokhlova, I.S. & Poulin, R. (2010) Deconstructing spatial patterns in species composition of ectoparasite communities: the relative contribution of host composition, environmental variables Journal of Biogeography 44, 1880–1890 ª 2017 John Wiley & Sons Ltd

and geography. Global Ecology and Biogeography, 19, 515– 526. Krasnov, B.R., Mouillot, D., Khokhlova, I.S., Shenbrot, G.I. & Poulin, R. (2012) Compositional and phylogenetic dissimilarity of host communities drives compositional and phylogenetic dissimilarity of ectoparasite assemblages: geographic variation and scale-dependence. Parasitology, 139, 338–347. Krasnov, B.R., Shenbrot, G.I., Khokhlova, I.S., Stanko, M., Morand, S. & Mouillot, D. (2015) Assembly rules of ectoparasite communities across scales: combining patterns of abiotic factors, host composition, geographic space, phylogeny and traits. Ecography, 38, 184–197. Kreppel, K.S., Telfer, S., Rajerison, M., Morse, A. & Baylis, M. (2016) Effect of temperature and relative humidity on the development times and survival of Synopsyllus fonquerniei and Xenopsylla cheopis, the flea vectors of plague in Madagascar. Parasites & Vectors, 9, 82. Legendre, P. & Legendre, L. (1998) Numerical ecology, 2nd Engl edn. Elsevier, Amsterdam. Lennon, J.J., Koleff, P., Greenwood, J. & Gaston, K.J. (2001) The geographical structure of British bird distributions: diversity, spatial turnover and scale. Journal of Animal Ecology, 70, 966–979. Lichstein, J.W. (2007) Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecology, 188, 117–131. Maestri, R. & Patterson, B.D. (2016) Patterns of species richness and turnover for the South American rodent fauna. PLoS ONE, 11, e0151895. Manion, G., Lisk, M., Ferrier, S., Nieto-Lugilde, D. & Fitzpatrick, M.C. (2016). gdm: functions for Generalized Dissimilarity Modeling. R package version 1.2.3. Available at: https://CRAN.R-project.org/package=gdm. Marshall, A.G. (1981) The ecology of ectoparasitic insects. Academic Press, London. Melo, A.S. (2016) CommEcol: Community Ecology Analyses. R package version 1.6.4. Available at: https://CRAN.R-projec t.org/package=CommEcol. Melo, A.S., Rangel, T.F.L. & Diniz-Filho, J.A.F. (2009) Environmental drivers of beta-diversity patterns in New World birds and mammals. Ecography, 32, 226–236. Nakaoka, M., Ito, N., Yamamoto, T., Okuda, T. & Noda, T. (2006) Similarity of rocky intertidal assemblages along the Pacific coast of Japan: effects of spatial scales and geographic distance. Ecological Research, 21, 425–435. Nekola, J.C. & White, P.S. (1999) The distance decay of similarity in biogeography and ecology. Journal of Biogeography, 26, 867–878. Oksanen, J. & Tonteri, T. (1995) Rate of compositional turnover along gradients and total gradient length. Journal of Vegetation Science, 6, 815–824. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E. & Wagner, H. (2016) vegan: Community Ecology Package. R 1889

R. Maestri et al. package version 2.4-1. Available at: https://CRAN.R-projec t.org/package=vegan. Oliva, M.E. & Gonzalez, M.T. (2005) The decay of similarity over geographical distance in parasite communities of marine fishes. Journal of Biogeography, 32, 1327–1332. Perez-del-Olmo, A., Fernandez, M., Raga, J.A., Kostadinova, A. & Morand, S. (2009) Not everything is everywhere: the distance decay of similarity in a marine host-parasite system. Journal of Biogeography, 36, 200–209. Pool, T.K., Grenouillet, G. & Villeger, S. (2014) Species contribute differently to the taxonomic, functional, and phylogenetic alpha and beta diversity of freshwater fish communities. Diversity and Distributions, 20, 1235–1244. Poulin, R. (2003) The decay of similarity with geographical distance in parasite communities of vertebrate hosts. Journal of Biogeography, 30, 1609–1615. Poulin, R. (2007) Evolutionary ecology of parasites. From individuals to communities. Princeton University Press, Princeton, NJ. Poulin, R. (2014) Parasite biodiversity revisited: frontiers and constraints. International Journal for Parasitology, 44, 581– 589. Poulin, R. & Krasnov, B.R. (2010) Similarity and variability in parasite assemblages across geographical space. The biogeography of host-parasite interactions (ed. by S. Morand and B.R. Krasnov), pp. 115–128. Oxford University Press, Oxford. Poulin, R. & Morand, S. (2000) The diversity of parasites. Quarterly Review of Biology, 75, 277–293. Qian, H. (2009) Beta diversity in relation to dispersal ability for vascular plants in North America. Global Ecology and Biogeography, 18, 327–332. Qian, H., Ricklefs, R.E. & White, P.S. (2005) Beta diversity of angiosperms in temperate floras of eastern Asia and eastern North America. Ecology Letters, 8, 15–22. R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/. Accessed April 2017. Rangel, T.F.L., Diniz-Filho, J.A.F. & Bini, L.M. (2006) Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography, 15, 321–327. Rosauer, D.F., Ferrier, S., Williams, K.J., Manion, G., Keogh, J.S. & Laffan, S.W. (2014) Phylogenetic generalised dissimilarity modelling: a new approach to analysing and predicting spatial turnover in the phylogenetic composition of communities. Ecography, 37, 21–32. Seifertova, M., Vyskocilova, M., Morand, S. & Simkova, A. (2008) Metazoan parasites of freshwater cyprinid fish (Leuciscus cephalus): testing biogeographical hypotheses of species diversity. Parasitology, 135, 1417–1435. Shenbrot, G.I., Krasnov, B.R. & Lu, L. (2007) Geographic range size and host specificity in ectoparasites: a case study with Amphipsylla fleas and rodent hosts. Journal of Biogeography, 34, 1679–1690.

1890

Sobek, S., Steffan-Dewenter, I., Scherber, C. & Tscharntke, T. (2009) Spatiotemporal changes of beetle communities across a tree diversity gradient. Diversity and Distributions, 15, 660–670. Soininen, J., McDonald, R. & Hillebrand, H. (2007) The distance decay of similarity in ecological communities. Ecography, 30, 3–12. Svenning, J.C. & Skov, F. (2007) Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecology Letters, 10, 453–460. Thieltges, D.W., Ferguson, M.A.D., Jones, C.S., Krakau, M., de Montaudouin, X., Noble, L.R., Reise, K. & Poulin, R. (2009) Distance decay of similarity among parasite communities of three marine invertebrate hosts. Oecologia, 160, 163–173. Thrush, S.F., Hewitt, J.E., Cummings, V.G., Norkko, A. & Chiantore, M. (2010) b-diversity and species accumulation in Antarctic coastal benthos: influence of habitat, distance and productivity on ecological connectivity. PLoS ONE, 5, e11899. Vashchenok, V.S. (1993) Factors regulating egg production in fleas Leptopsylla segnis (Leptopsyllidae: Siphonaptera). Parazitologiya, 27, 382–388 [in Russian]. Vinarski, M.V., Korallo, N.P., Krasnov, B.R., Shenbrot, G.I. & Poulin, R. (2007) Decay of similarity of gamasid mite assemblages parasitic on Palaearctic small mammals: geographic distance, host species composition or environment? Journal of Biogeography, 34, 1691–1700. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Appendix S1 Sources for information on flea and rodent distribution. Appendix S2 Compiling datasets of flea and rodent distribution across Mongolia. BIOSKETCHES Renan Maestri is interested in understanding macroecological patterns of diversity and the factors driving morphological diversification and evolution at large spatial scales. Georgy Shenbrot is a senior researcher at Ben-Gurion University of the Negev. His research interests lie in the areas of community ecology of desert mammals and systematics of rodents. Boris Krasnov is a Professor of Ecology at Ben-Gurion University of the Negev. His main interests are evolutionary ecology of host–parasite relationships and population ecology and ecophysiology of heamatophagous ectoparasites.

Editor: Richard Ladle
