The recovery of acid‐damaged zooplankton communities in Canadian Lakes: the relative importance of abiotic,biotic and spatial variables

1. Acidification has damaged biota in thousands of lakes and streams throughout eastern North America. Fortunately, reduced emissions of sulphur dioxide and nitrogen oxides beginning in the 1960s have allowed pH levels in many affected systems to increase. Determining the extent of biological and pH recovery in these systems is necessary to assess the success of emissions reductions programmes. 2. Although there have been promising signs of biological recovery in many systems, recovery has occurred more slowly than expected for some taxa. Past studies with crustacean zooplankton indicate that a mixture of local abiotic variables, biotic variables and dispersal processes may influence the structure of recovering communities. However, most studies have been unable to determine the relative importance of these three groups of variables. 3. We assessed chemical and biological recovery of acid‐damaged lakes in Killarney Park, Ontario. In addition, we assessed the relative importance of local abiotic variables, biotic variables and dispersal processes for structuring recovering communities. We collected zooplankton community data, abiotic and biotic data from 45 Killarney Park lakes. To assess the recovery of zooplankton communities, we compared zooplankton data collected in 2005 to a survey conducted for the same lakes in 1972–73 using several univariate measures of community structure, as well as multivariate methods based on relative species abundances. To determine the factors influencing the structure of recovering zooplankton communities, we used hierarchical partitioning for univariate measures and spatial modelling and variation partitioning techniques for multivariate analyses. 4. Our survey revealed significant pH increases for the majority of sampled lakes but univariate measures of community structure, such as species richness and diversity, indicated that only minor changes have occurred in many acid‐damaged lakes. Hierarchical partitioning identified several variables that may influence our univariate measures of recovery, including pH, dissolved organic carbon (DOC) levels, fish presence/absence, lake surface area and lake elevation. 5. Multivariate methods revealed a shift in communities through time towards a structure more typical of neutral lakes, providing some evidence for recovery. Variation partitioning suggested that the structure of recovering copepod communities was influenced most by dispersal processes and abiotic variables, while biotic (Chaoborus densities, fish presence/absence) and abiotic variables were more important for cladoceran zooplankton. 6. Our results indicate that the recovery of zooplankton communities in Killarney Park is not yet complete, despite decades of emission reductions. The importance of variables related to acidification, such as pH and DOC, indicates that further chemical recovery may be necessary. The differing importance of abiotic, biotic and dispersal processes for structuring copepod versus cladoceran zooplankton might indicate that different management approaches and expectations for recovery are needed for these groups.     

Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste

Soil-transmitted helminths (STHs) are parasitic intestinal worms that infect almost a fifth of the global population. Sustainable control of STHs requires understanding the complex interaction of factors contributing to transmission. Identifying risk factors has mainly relied on logistic regression models where the underlying assumption of independence between variables is not always satisfied. Previously demonstrated risk factors including water, sanitation and hygiene (WASH) access and behaviours, and socioeconomic status are intrinsically linked. Similarly, environmental factors including climate, soil and land attributes are often strongly correlated. Alternative methods such as recursive partitioning and Bayesian networks can handle correlated variables, but there are no published studies comparing these methods with logistic regression in the context of STH risk factor analysis. Baseline cross-sectional data from school-aged children in the (S)WASH-D for Worms study were used to compare risk factors identified from modelling the same data using three different statistical techniques. Outcomes of interest were infection with Ascaris spp. and any hookworm species (Necator americanus, Ancylostoma duodenale, and Ancylostoma ceylanicum). Mixed-effects logistic regression identified the fewest risk factors. Recursive partitioning identified the most WASH and demographic risk factors, while Bayesian networks identified the most environmental risk factors. Recursive partitioning produced classification trees that visualised potentially at-risk population sub-groups. Bayesian networks helped visualise relationships between variables and enabled interactive modelling of outcomes based on different scenarios for the predictor variables of interest. Model performance was similar across all techniques. Risk factors identified across all techniques were vegetation for Ascaris spp., and cleaning oneself with water after defecating for hookworm. This study adds to the limited body of evidence exploring alternative data modelling approaches in identifying risk factors for STH infections. Our findings suggest these approaches can provide novel insights for more robust interpretation.     

A Multi-Scale Approach to Investigating the Red-Crowned Crane–Habitat Relationship in the Yellow River Delta Nature Reserve,China: Implications for Conservation

The red-crowned crane (Grus japonensis (Statius Müller, 1776)) is a rare and endangered species that lives in wetlands. In this study, we used variance partitioning and hierarchical partitioning methods to explore the red-crowned crane–habitat relationship at multiple scales in the Yellow River Delta Nature Reserve (YRDNR). In addition, we used habitat modeling to identify the cranes’ habitat distribution pattern and protection gaps in the YRDNR. The variance partitioning results showed that habitat variables accounted for a substantially larger total and pure variation in crane occupancy than the variation accounted for by spatial variables at the first level. Landscape factors had the largest total (45.13%) and independent effects (17.42%) at the second level. The hierarchical partitioning results showed that the percentage of seepweed tidal flats were the main limiting factor at the landscape scale. Vegetation coverage contributed the greatest independent explanatory power at the plot scale, and patch area was the predominant factor at the patch scale. Our habitat modeling results showed that crane suitable habitat covered more than 26% of the reserve area and that there remained a large protection gap with an area of 20,455 ha, which accounted for 69.51% of the total suitable habitat of cranes. Our study indicates that landscape and plot factors make a relatively large contribution to crane occupancy and that the focus of conservation effects should be directed toward landscape- and plot-level factors by enhancing the protection of seepweed tidal flats, tamarisk-seepweed tidal flats, reed marshes and other natural wetlands. We propose that efforts should be made to strengthen wetland restoration, adjust functional zoning maps, and improve the management of human disturbance in the YRDNR.     

Hierarchical partitioning as an interpretative tool in multivariate inference

Abstract Much of biogeography, conservation and evolutionary biology, and ecology involves very large spatial and temporal extents. Direct manipulation to test hypotheses is usually almost impossible at appropriate scales so that multivariate modelling and especially regression are used to draw causal inferences about which ‘independent’ variables influence the distribution and abundances of species. Such inferences clearly are crucial for the successful management of biological resources and for conserving threatened species. A succession of regression approaches has arisen, many of which yield inconsistent implications. The main problem has been the quest for one (the ‘best’ or the ‘optimal’) regression model from which the impacts of independent variables are inferred. This note is to draw the attention of ecologists to a relatively recent method, hierarchical partitioning, that does not aim to identify a best regression model as such but rather uses all models in a regression hierarchy to distinguish those variables that have high independent correlations with the dependent variable. Such variables are likely to be most influential in controlling variation in the dependent variable. Hierarchical partitioning is not to be regarded as a substitute for experimental manipulation when that is appropriate, but it is likely to produce better deductions than common regression approaches in the many ecological situations in which manipulation is impossible or of doubtful value.     

A systematic comparison and evaluation of biclustering methods for gene expression data

MOTIVATION: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. RESULTS: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.     

Responses of Aquatic Insect Functional Diversity to Landscape Changes in Atlantic Forest

The Atlantic Forest domain, one of the 25 world's hotspots for biodiversity, has experienced dramatic changes in its landscape. While the loss of species diversity is well documented, functional diversity has not received the same amount of attention. In this study, we evaluated functional diversity of insects in streams utilizing three indices: functional diversity (FD), functional dispersion (FDis), and functional divergence (FDiv), seeking to understand the roles of three predictor sets in explaining functional patterns: (1) bioclimatic and landscape variables; (2) spatial variables; and (3) local environmental variables. We determined the amount of variation in different measures of functional diversity that was explained by each predictor set and their interplays using variation partitioning. Our study showed that variation in functional diversity is better explained by a set of variables linked to different scales dependent on spatial structures, indicating the importance of landscape and mainly environmental variables in the functional organization of aquatic insect communities, and that the relative importance of predictor sets depends on the indices considered. Variation in FD was better explained by the interplay among the three predictor sets and by local environmental variables, whereas variation in FDis was better explained by spatial variables and by the interplay between environmental and spatial variables. Variation in FDiv was not significantly explained by any predictors. Our study adds more evidence on the harmful effects caused by landscape changes on biodiversity in the Atlantic Forest, suggesting that these effects also influence the functional organization of stream insect communities.     

Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape

In many areas of the northern Mediterranean Basin the abundance of forest and scrubland vegetation is increasing, commensurate with decreases in agricultural land use(s). Much of the land use/cover change (LUCC) in this region is associated with the marginalization of traditional agricultural practices due to ongoing socioeconomic shifts and subsequent ecological change. Regression-based models of LUCC have two purposes: (i) to aid explanation of the processes driving change and/or (ii) spatial projection of the changes themselves. The independent variables contained in the single ‘best’ regression model (that is, that which minimizes variation in the dependent variable) cannot be inferred as providing the strongest causal relationship with the dependent variable. Here, we examine the utility of hierarchical partitioning and multinomial regression models for, respectively, explanation and prediction of LUCC in EU Special Protection Area 56, ‘Encinares del río Alberche y Cofio’ (SPA 56) near Madrid, Spain. Hierarchical partitioning estimates the contribution of regression model variables, both independently and in conjunction with other variables in a model, to the total variance explained by that model and is a tool to isolate important causal variables. By using hierarchical partitioning we find that the combined effects of factors driving land cover transitions varies with land cover classification, with a coarser classification reducing explained variance in LUCC. We use multinomial logistic regression models solely for projecting change, finding that accuracies of maps produced vary by land cover classification and are influenced by differing spatial resolutions of socioeconomic and biophysical data. When examining LUCC in human-dominated landscapes such as those of the Mediterranean Basin, the availability and analysis of spatial data at scales that match causal processes is vital to the performance of the statistical modelling techniques used here.     

Amphibian species richness across environmental gradients

Large‐scale field patterns are a fundamental source of inferences on processes responsible for variation in species richness among habitats. We examined species richness of larval amphibian communities in 37 ponds over seven years on the Univ. of Michigan's E. S. George Reserve. Ordination of the community incidence matrix indicated a strong major axis of variation in species associations that was correlated with pond hydroperiod, surface area and forest canopy cover. Communities were significantly nested with those species found in ponds with high canopy cover, small area and short hydroperiod being nested subsets of those found in ponds with contrasting characteristics. Presence of fish had strong negative effects on species richness; relaxation of this effect also was apparent when fish were extirpated from ponds by drought. We employed a model selection analysis to identify the most appropriate statistical model for predicting the long‐term average species richness of these ponds from local abiotic and biotic (predator and competitor density) factors. A model including only the abiotic factors was overwhelmingly superior for the anurans; hierarchical partitioning indicated that area and canopy cover alone accounted for over 70% of the independent effects of predictor variables. The global model including both abiotic and biotic factors was the best supported model for the caudates, and correspondingly hierarchical partitioning suggested that area, hydroperiod, invertebrate predators and caudate biomass all accounted for 9–16% of the independent effects. Overall, biotic factors accounted for much less of the variation in species richness than abiotic factors. The patterns in larger, open‐canopy ponds provided little evidence of competitive effects on species richness, though there were patterns consistent with competitive effects in small, closed‐canopy ponds. The unusual temporal and spatial extent of these data enabled us to critically evaluate ideas regarding patterns in larval amphibian communities, and the effects of area, disturbance (hydroperiod) and productivity (canopy cover) on species richness of these communities. These results have important implications to the conservation of amphibian species richness in freshwater wetlands, which are among the most threatened ecosystems worldwide.     

Collinearity in ecological niche modeling: Confusions and challenges

Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.     

Three ecological factors influencing riverine fish diversity in the Shubuto River system,Japan: habitat capacity,habitat heterogeneity and immigration

A major goal of stream ecology is to identify environmental gradients that shape riverine communities. We examined the relative importance of three ecological factors that have been hypothesized to influence a longitudinal pattern of fish diversity: habitat capacity, heterogeneity and immigration of diadromous fishes. Field surveys were carried out in the entire network of the Shubuto River system, Hokkaido, Japan. A hierarchical partitioning approach revealed that distance from the sea, a proxy for immigration potential of diadromous fishes, had the greatest explanatory capacity, by which 24.9 % of variation in fish species richness was explained. Habitat capacity (approximated by catchment area) was also identified as a significant predictor of fish diversity, whereas habitat heterogeneity brought little improvement to the model performance. These results reflect the fish fauna of the Shubuto River system, in which diadromous fishes are dominant in both abundance and species richness.     

Uncovering the drivers of host‐associated microbiota with joint species distribution modelling

In addition to the processes structuring free‐living communities, host‐associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host‐specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model‐based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host‐specific factors are in structuring the microbiota; and (c) co‐occurrence networks to visualize microbe‐to‐microbe associations.     

Prey diversity as a driver of resource partitioning between river‐dwelling fish species

Although food resource partitioning among sympatric species has often been explored in riverine systems, the potential influence of prey diversity on resource partitioning is little known. Using empirical data, we modeled food resource partitioning (assessed as dietary overlap) of coexisting juvenile Atlantic salmon (Salmo salar) and alpine bullhead (Cottus poecilopus). Explanatory variables incorporated into the model were fish abundance, benthic prey diversity and abundance, and several dietary metrics to give a total of seventeen potential explanatory variables. First, a forward stepwise procedure based on the Akaike information criterion was used to select explanatory variables with significant effects on food resource partitioning. Then, linear mixed‐effect models were constructed using the selected explanatory variables and with sampling site as a random factor. Food resource partitioning between salmon and bullhead increased significantly with increasing prey diversity, and the variation in food resource partitioning was best described by the model that included prey diversity as the only explanatory variable. This study provides empirical support for the notion that prey diversity is a key driver of resource partitioning among competing species.     

Predicting the Amount of Litterfall in Forests of the World

Total litterfall and leaf litterfall were examined for 389 forestsites throughout the world using multiple regression, consideringlatitude, altitude and precipitation as predictor variables.In its best model, Ig total litterfall was negatively related(r² = 0.58) to latitude and altitude, while in the best modelfor lg leaf litterfall there was a negative relationship (r²= 0.35) with only one variable, latitude. When tropical forestswere considered separately however, lg leaf litterfall was positivelyrelated to precipitation, and negatively related to altitude(r² = 0.43). The predictive power of all the regression equationswas low, with large percentage values of scatter about the fittedvalues for all equations. In addition, there was no relationshipbetween the ratio of non-leaf material to leaf material in thelitter with any of the predictor variables. Litterfall, forests, tropical forests, latitude, altitude, precipitation, regression models     

New insights into butterfly-environment relationships using partitioning methods

Variation partitioning and hierarchical partitioning are novel statistical approaches that provide deeper understanding of the importance of different explanatory variables for biodiversity patterns than traditional regression methods. Using these methods, the variation in occupancy and abundance of the clouded apollo butterfly (Parnassius mnemosyne L.) was decomposed into independent and joint effects of larval and adult food resources, microclimate and habitat quantity. The independent effect of habitat quantity variables (habitat area and connectivity) captured the largest fraction of the variation in the clouded apollo patterns, but habitat connectivity had a major contribution only for occupancy data. The independent effects of resources and microclimate were higher on butterfly abundance than on occupancy. However, a considerable amount of variation in the butterfly patterns was accounted for by the joint effects of predictors and may thus be causally related to two or all three groups of variables. Abundance of the butterfly in the surroundings of the focal grid cell had a significant effect in all analyses, independently of the effects of other predictors. Our results encourage wider applications of partitioning methods in biodiversity studies.     

Scales of association: hierarchical linear models and the measurement of ecological systems

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance–covariance parameters in hierarchically structured data. Although hierarchical models have occasionally been used in the analysis of ecological data, their full potential to describe scales of association, diagnose variance explained, and to partition uncertainty has not been employed. In this paper we argue that the use of the HLM framework can enable significantly improved inference about ecological processes across levels of organization. After briefly describing the principals behind HLM, we give two examples that demonstrate a protocol for building hierarchical models and answering questions about the relationships between variables at multiple scales. The first example employs maximum likelihood methods to construct a two-level linear model predicting herbivore damage to a perennial plant at the individual- and patch-scale; the second example uses Bayesian estimation techniques to develop a three-level logistic model of plant flowering probability across individual plants, microsites and populations. HLM model development and diagnostics illustrate the importance of incorporating scale when modelling associations in ecological systems and offer a sophisticated yet accessible method for studies of populations, communities and ecosystems. We suggest that a greater coupling of hierarchical study designs and hierarchical analysis will yield significant insights on how ecological processes operate across scales.     

Species turnover of amphibians and reptiles in eastern China: disentangling the relative effects of geographic distance and environmental difference

Spatial turnover of species lies at the heart of macroecology and conservation biogeography. However, our knowledge of the causes of species turnover remains poor, particularly for herpetofaunas including amphibians and reptiles. Here, using regression, variance partitioning, and hierarchical partitioning analyses, we examine the relationships of species turnover in herpetofaunas among provinces in eastern China with respect to geographic distance and environmental difference. We found that species turnover in herpetofaunas is moderately to strongly correlated with geographic distance and difference in most environmental variables examined between provinces. Geographic distance and environmental difference together explain 87.1 and 89.9% of the variance of species turnover for amphibians and reptiles, respectively. Variance partitioning analysis indicated that most variance in species turnover is explained by the joint effect of geographic distance and environmental difference. Beyond this shared variance, environmental difference is a stronger predictor of species turnover than geographic distance, particularly for reptiles. Hierarchical partitioning analysis showed that energy-related variables explained more variance in species turnover for both amphibians and reptiles, compared with water-related variables. The independent effects of water-related variables are slightly higher for amphibians than for reptiles whereas the independent effects of energy-related variables are slightly higher for reptiles than amphibians. These patterns are consistent with different ecophysiological requirements of the two taxa. Our results have important implications for predicting changes in biodiversity of herpetofaunas under climate change scenarios. Global warming will affect the immigration and local extinction of both amphibians and reptiles, and precipitation change may affect amphibians more strongly, compared with its effect on reptiles.     

Investigating Morphospace Occupation in Multi-Scale Ecological and Evolutionary Data Using Regression Tree: Case Studies and Perspectives

A key challenge in ecology and evolutionary biology is to explain the origin, structure and temporal patterns of phenotypic diversity. With regard to the potentially complex determinism of phenotypic differences, the issue should be comprehended in a general view, across multiple scales and an increasing number of phenomic studies investigate shape variation through large taxonomic, biogeographic or temporal scales. In this context, there is an ever-increasing need to develop new tools for a coherent understanding of morphospace occupation by disentangling and quantifying the main determinants of phenotypic changes. The present study briefly introduce the possibility to use multivariate regression tree technique to cope with morphological data, as embedded in a geometric morphometric framework. It emphasizes that hierarchical partitioning methods produce a hierarchy between causal variables that may help analyzing complexity in multi-scale ecological and evolutionary data. I therefore suggest that morphological studies would benefit from the combined use of the classical statistical models with rapidly emerging and diversifying methods of machine-learning. Doing so allows one to primary explore in an extensive exploratory manner the hierarchy of nested organisational levels underlying morphological variation, and then conduct hypothesis-driven analysis by focusing on a relevant scale or by investigating the appropriate model that reflects hypothesized nested influence of explanatory variables. The outlined approach may help investigating morphospace occupation in an explicitly hierarchical quantitative context.     

Exploring associations between international trade and environmental factors with establishment patterns of exotic Scolytinae

Although invasion of exotic ambrosia beetles (fungus feeders) and bark beetles (phloem feeders) (Coleoptera: Curculionidae: Scolytinae) is considered a major threat to forest health worldwide, no studies have quantitatively investigated the anthropogenic and environmental factors shaping the biogeographical patterns of invasion by these insects across large spatial scales. The primary aim of this study was to assess the relative importance of international trade and several environmental variables of the recipient region on species richness of established exotic Scolytinae. As a reference, we also evaluated the relationships between the same environmental variables and species richness of native Scolytinae. Using an information-theoretic framework for model selection and hierarchical partitioning, we evaluated the relative importance of the potential drivers of species richness of native and exotic Scolytinae in 20 European countries and the 48 contiguous continental US states. Analyses were conducted separately for ambrosia and bark beetle species. Value of imports was a strong predictor of the number of exotic Scolytinae species in both regions. In addition, in the USA, warmer and wetter climate was positively linked to increased numbers of both native and exotic ambrosia beetles. Forest heterogeneity and climatic heterogeneity and secondarily forest area were key drivers in explaining patterns of species richness for native bark beetles but not for exotic species in both regions. Our findings suggest that if current infestation levels continue on imported plants and wood packaging material, increasing international trade will likely lead to more establishments of exotic Scolytinae with concomitant negative effects on forest health in both Europe and the USA. Compared to Europe the risk of invasion appears higher in the USA, especially for ambrosia beetles in the southeastern USA where the climate appears highly suitable for exotic establishment.     

Effect of field sampling design on variation partitioning in a dendritic stream network

Variation partitioning is one of the most frequently used method to infer the importance of environmental (niche based) and spatial (dispersal) processes in metacommunity structuring. However, the reliability of the method in predicting the role of the major structuring forces is less known. We studied the effect of field sampling design on the result of variation partitioning of fish assemblages in a stream network. Along with four different sample sizes, a simple random sampling from a total of 115 stream segments (sampling objects) was applied in 400 iterations, and community variation of each random sample was partitioned into four fractions: pure environmentally (landscape variables) explained, pure spatially (MEM eigenvectors) explained, jointly explained by environment and space, and unexplained variance. Results were highly sensitive to sample size. Even at a given sample size, estimated variance fractions had remarkable random fluctuation, which can lead to inconsistent results on the relative importance of environmental and spatial variables on the structuring of metacommunities. Interestingly, all the four variance fractions correlated better with the number of the selected spatial variables than with any design properties. Sampling interval proved to be a fundamentally influential sampling design property because it affected the number of the selected spatial variables. Our findings suggest that the effect of sampling design on variation partitioning is related to the ability of the eigenvectors to model complex spatial patterns. Hence, properties of the sampling design should be more intensively considered in metacommunity studies.