Moran's Eigenvector Maps Research Articles

Can We Compare Effect Size of Spatial Genetic Structure Between Studies and Species Using Moran Eigenvector Maps?

As the field of landscape genetics is progressing toward comparative empirical studies and meta-analysis, it is important to know how best to compare the strength of spatial genetic structure between studies and species. Moran’s Eigenvector Maps are a promising method that does not make an assumption of isolation-by-distance in a homogeneous environment but can discern cryptic structure that may result from multiple processes operating in heterogeneous landscapes. MEMgene uses spatial filters from Moran’s Eigenvector Maps as predictor variables to explain variation in a genetic distance matrix, and it returns adjusted R2 as a measure of the amount of genetic variation that is spatially structured. However, it is unclear whether, and under which conditions, this value can be used to compare the degree of spatial genetic structure (effect size) between studies. This study addresses the fundamental question of comparability at two levels: between independent studies (meta-analysis mode) and between species sampled at the same locations (comparative mode). We used published datasets containing 9,900 haploid, biallelic, neutral loci simulated on a quasi-continuous, square landscape under four demographic scenarios (island model, isolation-by-distance, expansion from one or two refugia). We varied the genetic resolution (number of individuals and loci) and the number of random sampling locations. We considered two measures of effect size, the MEMgene adjusted R2 and multivariate Moran’s I, which is related to Moran’s Eigenvector Maps. Both metrics were highly sensitive to the number of locations, even when using standardized effect sizes, SES, and the number of individuals sampled per location, but not to the number of loci. In comparative mode, using the same Moran Eigenvector Maps for all species, even those with missing values at some sampling locations, reduced bias due to the number of locations under isolation-by-distance (stationary process) but increased it under expansion from one or two refugia (non-stationary process). More robust measures of effect size need to be developed before the strength of spatial genetic structure can be accurately compared, either in a meta-analysis of independent empirical studies or within a comparative, multispecies landscape genetic study.

Consistent and transient drivers of freshwater zooplankton communities

AbstractAimInferences of the predominant processes that structure communities are commonly based on single ‘snapshots’ in time, which may miss temporally transient but important mechanisms. In this study, we compared key environmental and spatial drivers of zooplankton composition across multiple years to quantify shifts in their relative importance through time, and to identify any drivers of temporal change.LocationSouthern Ontario, Canada.TaxonZooplankton.MethodsZooplankton were collected from 29 lakes in southern Ontario, Canada over four years (2013–2016). Variation partitioning was used to quantify the relative importance of independent and covarying components of local environmental conditions, regional east‐west and north‐south compositional trends, and inter‐lake geographic distance in each year. Measured environmental metrics included aspects of lake morphology and chemistry, and spatial relationships were quantified using lake latitude/longitude coordinates and Moran's Eigenvector Maps (MEMs). Redundancy analyses (RDAs) were also used to compare the influence of individual environmental and spatial variables across years.ResultsMost local‐scale and regional‐scale community processes were consistently important across all surveyed years, but some were less consistent. Specifically, geomorphology was always an important driver of local environmental and regional spatial community patterns. This occurred because local community composition was strongly affected by whether a lake was shallower versus deeper, and due to spatial clusters of shallow and deep lakes that produced negative spatial autocorrelation in community composition. Conversely, the individual influences of lake chemistry and spatial east‐west compositional trends were important in some years and not in others, potentially due to inter‐annual shifts in the predominant environmental variables and extreme weather events.Main conclusionsA single‐year community snapshot can provide insight into consistent or slowly changing community structuring processes, such as those driven by geomorphology, but may not completely capture temporally transient mechanisms. Furthermore, snapshots collected during anomalous seasons or years may misrepresent which mechanisms are predominantly determining community composition. Future efforts to understand local and regional community drivers would therefore benefit from considering which processes are likely temporally ‘consistent’ versus ‘transient’, and studies with more variable components would benefit from considering or controlling for temporal shifts in their importance.

Environment and Space Rule, but Time Also Matters for the Organization of Tropical Pond Metacommunities

Metacommunities are dynamic systems, but the influence of time independently of environmental change in their configuration has been rarely considered. Temporary ponds are excellent ecosystem models, as they have well-defined boundaries in time and space; their communities are relatively isolated through a landscape matrix, and the progress of time leads to major changes through ecological succession and in habitat suitability related to hydroperiod dynamics. Therefore, strong temporal effects are expected to influence their metacommunity structure. We surveyed 30 temporary ponds along the dry tropical region of western Costa Rica and Nicaragua at three different moments of their hydroperiod: shortly after the infilling of the water bodies, at the middle of the hydroperiod and just before desiccation. We obtained data on 56 environmental variables (including limnological, climatic, landscape, hydrogeomorphological and conservation state), and used geographic coordinates to build spatial variables (Moran Eigenvector Maps). We collected biological samples and estimated the specific abundance of phytoplankton, zooplankton and benthic invertebrates. To evaluate the relative role of environmental, spatial and temporal (sequential sampling season) effects in metacommunity organization of the 646 species found, we used variation partitioning with distance-based redundancy analyses for each group of organisms. We repeated the analyses for selected taxonomic groups, separately for each sampling season, to check how the variance explained by spatial and environmental factors varied between sampling periods. The inclusion of time in the analysis highlighted that pure temporal effects explained part of metacommunity variance in almost every group, being as important as spatial or even environmental effects for some groups of organisms. In contrast to the assumed low environmental constraints in tropical areas (i.e. high and stable temperatures), we found strong environmental effects. Passive dispersers were more influenced by environmental factors than active ones. We also found a positive relationship between the body size of the different groups of organism and the magnitude of the temporal effect, interpreted as related to generation time. Finally, when analyzing each sampling period separately, we found differences in the relative role of environment and space at different sampling periods, showing that snapshot surveys may not be representative of highly dynamic metacommunities.

Seasonal patterns of ecological uniqueness of anuran metacommunities along different ecoregions in Western Brazil.

Beta diversity can be portioned into local contributions to beta diversity (LCBD), which represents the degree of community composition uniqueness of a site compared to regionally sampled sites. LCBD can fluctuate among seasons and ecoregions according to site characteristics, species dispersal abilities, and biotic interactions. In this context, we examined anuran seasonal patterns of LCBD in different ecoregions of Western Brazil, and assessed their correlation with species richness and if environmental (climatic variables, pond area and ecoregions) and/or spatial predictors (spatial configuration of sampling sites captured by distance-based Moran's Eigenvector Maps) would drive patterns of LCBD. We sampled anurans in 19 ponds in different ecoregions in the Mato Grosso do Sul state, Western Brazil, during one dry and one rainy season. We found that LCBD patterns were similar between seasons with sites tending to contribute in the same way for community composition uniqueness during the dry and rainy season. Among studied ecoregions, Cerrado showed higher LCBD values in both seasons. In addition, LCBD was negatively correlated with species richness in the dry season. We also found that LCBD variation was explained by ecoregion in the dry season, but in the rainy season both environmental and spatial global models were non-significant. Our results reinforce the compositional uniqueness of the Cerrado ecoregion when compared to the other ecoregions in both seasons, which may be caused by the presence of species with different requirements that tolerate different conditions caused by seasonality.

The mechanisms generating community phylogenetic patterns change with spatial scale.

Studies that test community assembly hypotheses in observational communities frequently evaluate patterns for plots or entire communities, yet studies that examine assembly patterns across spatial scales show that they are greatly influenced by scale. Here, we test the spatial dependency of patterns of relatedness and plant height for all individual herbaceous plants along five 40-m old-field transects (Southern Ontario, Canada). We identified each individual plant and measured its distance along the transect and its height, and we constructed a molecular phylogeny for all observed species. To uncover the scale at which community phylogenetic and trait similarities shift, we used partial Mantel correlograms and distance-based Moran Eigenvector Maps (dbMEMs). We found that communities shift from significantly overdispersed at relatively smaller scales (i.e., < 15m) to spatially clustered at larger scales, showing that assembly mechanism influence depends on scale of observation. This pattern was observed for both phylogeny and height, but was the strongest when considering phylogeny only. These results reveal the importance of spatial scale when examining community phylogenetic or trait patterns, where finding support for one assembly mechanism at a single scale does not necessarily mean that other mechanisms are also not important for structuring community composition and diversity.

Unraveling the effects of water–sediment conditions and spatial patterns on Unionida assemblages in seasonally connected floodplain lakes

Floodplain lakes are good metacommunity systems to study the environmental and spatial processes structuring local assemblages. They are more connected during high-water periods and are more isolated during low-water periods. We evaluated the effects of lake spatial patterning and water and sediment conditions on Unionida species assemblages. Moran Eigenvector Maps were used to generate spatial variables representing spatial patterns at different scales. We sampled 35 lakes from the Pantanal floodplain, Brazil. To understand the effects of environmental and spatial variables, we performed Redundancy Analyses and variation partitioning to separate environmental and spatial pattern effects. Environmental variables explained almost twice the variation in the Pantanal mussel assemblages than did spatial variables. Unionida species presence was driven mainly by variations in sediment coarse sand and silt contents. The weak spatial patterns observed may be related to increased connectivity between lakes during floods, which facilitates mussel host fish dispersal. Mussel abundances were driven mainly by organic matter availability, but varied between species. Changes in lake connectivity can affect the regional sediment dynamics and affect mussel assemblages.

Untangling the determinants of macrophyte beta diversity in tropical floodplain lakes: insights from ecological uniqueness and species contributions

Deconstructing beta diversity patterns into site or species contributions is a modern approach to understand the factors affecting variation in biodiversity. In this context, estimating the Local Contribution to Beta Diversity (LCBD) and the individual Species Contribution to Beta diversity (SCBD) have been shown to be a good approach to improve knowledge of the drivers of beta diversity. We examined the beta diversity of macrophytes surveyed at 49 floodplain lakes in the Pantanal Wetlands in Brazil during the dry season. We found that, unexpectedly, total species richness was not correlated to LCBD values, but the number of rare species per site was possibly related to LCBD values. Three variables from Moran’s Eigenvector Maps (MEM), water transparency and organic matter were the main variables related to LCBD values. The species with highest contributions to beta diversity were those that occurred at approximately half of the surveyed sites. The same patterns were observed when analysing macrophyte data divided by life forms. Of the life forms, floating macrophytes contributed most to beta diversity. The understanding of which factors drive variation in LCBD and which kind of species most contribute to SCBD are fundamental to performing efficient conservation and restoration programs to maintain the structural, functional and ecological diversity of the macrophyte communities in dynamic floodplain ecosystems.

Metacommunity Structure of Stream Insects across Three Hierarchical Spatial scales.

A major challenge in community ecology is to understand the underlying factors driving metacommunity (i.e., a set of local communities connected through species dispersal) dynamics. However, little is known about the effects of varying spatial scale on the relative importance of environmental and spatial (i.e., dispersal related) factors in shaping metacommunities and on the relevance of different dispersal pathways. Using a hierarchy of insect metacommunities at three spatial scales (a small, within‐stream scale, intermediate, among‐stream scale, and large, among‐sub‐basin scale), we assessed whether the relative importance of environmental and spatial factors shaping metacommunity structure varies predictably across spatial scales, and tested how the importance of different dispersal routes vary across spatial scales. We also studied if different dispersal ability groups differ in the balance between environmental and spatial control. Variation partitioning showed that environmental factors relative to spatial factors were more important for community composition at the within‐stream scale. In contrast, spatial factors (i.e., eigenvectors from Moran's eigenvector maps) relative to environmental factors were more important at the among‐sub‐basin scale. These results indicate that environmental filtering is likely to be more important at the smallest scale with highest connectivity, while dispersal limitation seems to be more important at the largest scale with lowest connectivity. Community variation at the among‐stream and among‐sub‐basin scales were strongly explained by geographical and topographical distances, indicating that overland pathways might be the main dispersal route at the larger scales among more isolated sites. The relative effect of environmental and spatial factors on insect communities varied between low and high dispersal ability groups; this variation was inconsistent among three hierarchical scales. In sum, our study indicates that spatial scale, connectivity, and dispersal ability jointly shape stream metacommunities.

Timing to temperature: Egg‐laying dates respond to temperature and are under stronger selection at northern latitudes

AbstractGlobal climate change produces spatially variable patterns of environmental change. This could put migratory species at risk as the synchrony between migration timing and suitable breeding conditions could become mismatched. For migratory birds, whether the timing of egg laying is a plastic trait that can vary in response to environmental change has been sparsely studied across regions and systems and thus remains poorly known. We investigated the effects of temperature variability and climate warming on the breeding phenology of purple martins (Progne subis), a long‐distance migratory songbird, using a 20‐yr data set comprised of 28,165 records of nest timing and fledgling success spanning the entire breeding range (25–54° N). We discovered that purple martins lay eggs earlier in warmer springs and fledge more young when they lay earlier. After controlling for spatial patterns in the data with Moran's eigenvector maps, we found that selection favored earlier breeding in most years, particularly at more northern latitudes. However, selection pressure for earlier breeding did not increase over the 20‐yr period, perhaps owing to high variability in temperature across years. Our results therefore demonstrate plasticity in the timing of egg laying in response to temperature variation and climate change over 20 yr across the range of this widely distributed, long‐distance migrant. Whether these plastic responses are common or sufficiently matched to climate change among other declining migratory songbird species should be further investigated.

Coordinated Metacommunity Assembly and Spatial Distribution of Multiple Microbial Kingdoms within a Lake.

Freshwater planktonic communities comprise a tremendous diversity of microorganisms. This study investigated the distribution patterns of microbial kingdoms (bacteria, fungi, protists, and microbial metazoans) within a lake ecosystem. Water samples were collected from 50 sites along the shoreline in a lake during an early eutrophication period, and MiSeq sequencing was performed with different marker genes. Metacommunity analyses revealed a bimodal occupancy-frequency distribution and a Clementsian gradient persisting throughout all microbial kingdoms, suggesting similar regional processes in all kingdoms. Variation partitioning revealed that environmental characteristics, macrophyte/macroinvertebrate composition, space coordinates, and distance-based Moran's eigenvector maps (dbMEM) together could explain up to 29% of the community variances in microbial kingdoms. Kingdom synchrony results showed strong couplings between kingdoms (R2 ≥ 0.31), except between Fungi and Metazoa (R2 = 0.09). Another variation partitioning revealed that microbial kingdoms could well explain their community variances up to 73%. Interestingly, the kingdom Protista was best synchronized with the other kingdoms. A correlation network showed that positive associations between kingdoms outnumbered the negative ones and that the kingdom Protista acted as a hub among kingdoms. Module analysis showed that network modules included multi-kingdom associations that were prevalent. Our findings suggest that protists coordinate community assembly and distribution of other kingdoms, and inter-kingdom interactions are a key determinant in shaping their community structures in a freshwater lake.

Host plant associations and geography interact to shape diversification in a specialist insect herbivore.

Disentangling the processes underlying geographic and environmental patterns of biodiversity challenges biologists as such patterns emerge from eco-evolutionary processes confounded by spatial autocorrelation among sample units. The herbivorous insect, Belonocnema treatae (Hymenoptera: Cynipidae), exhibits regional specialization on three plant species whose geographic distributions range from sympatry through allopatry across the southern United States. Using range-wide sampling spanning the geographic ranges of the three host plants and genotyping-by-sequencing of 1,217 individuals, we tested whether this insect herbivore exhibited host plant-associated genomic differentiation while controlling for spatial autocorrelation among the 58 sample sites. Population genomic structure based on 40,699 SNPs was evaluated using the hierarchical Bayesian model entropy to assign individuals to genetic clusters and estimate admixture proportions. To control for spatial autocorrelation, distance-based Moran's eigenvector mapping was used to construct regression variables summarizing spatial structure inherent among sample sites. Distance-based redundancy analysis (dbRDA) incorporating the spatial variables was then applied to partition host plant-associated differentiation (HAD) from spatial autocorrelation. By combining entropy and dbRDA to analyse SNP data, we unveiled a complex mosaic of highly structured differentiation within and among gall-former populations finding evidence that geography, HAD and spatial autocorrelation all play significant roles in explaining patterns of genomic differentiation in B.treatae. While dbRDA confirmed host association as a significant predictor of patterns of genomic variation, spatial autocorrelation among sites explained the largest proportion of variation. Our results demonstrate the value of combining dbRDA with hierarchical structural analyses to partition spatial/environmental patterns of genomic variation.

Generalist dispersal and gene flow of an endangered keystone specialist (Dipodomys ingens)

Abstract Movement ecology and dispersal capabilities inherently drive genetic structure across landscapes. Through understanding dispersal and gene flow of giant kangaroo rats (Dipodomys ingens), conservation efforts can be focused, and we can further understand how genetic structure persists in this highly endemic small mammal. Here, we genetically identify parent–offspring and sibship relationships among 239 giant kangaroo rats using 15 microsatellites in the northern part of the species range and describe the individual genetic-spatial variation using a Moran eigenvector map (MEM). We further employ two landscape genetic analyses (isolation by resistance [IBR] and least cost paths [LCPs]) and two individual-based genetic metrics (Dps and a codominant marker distance from GenAlEx) to determine landscape factors (precipitation, slope, vegetation community, and roads) that influence gene flow. We found 19 pairs of related individuals, of which 18 were less than 250 m apart, but one sibling pair was 5.52 km apart, suggesting greater dispersal capabilities than previously noted. We found hierarchal spatial genetic structure using a MEM, with 3–4 genetically similar regions and two genetically similar subregions. Finally, we found low correlative strength between landscape features and gene flow. IBR consistently outperformed LCPs, and there was evidence that regions with 250–350 mm of precipitation and slope ≤ 5° promoted connectivity. We recommend that managers focus on habitat protection rather than corridor maintenance, with the caveat that anthropogenic factors were minimally considered in this study.

Benthic communities and their drivers: A spatial analysis off the Antarctic Peninsula

AbstractMultiple environmental factors control benthic community patterns, and their relative importance varies with spatial scale. Since this variation is difficult to evaluate quantitatively, extensive sampling across a broad range of spatial scales is required. Here, we present a first case study on Southern Ocean shelf benthos, in which mega‐epibenthic communities and biota‐environment relationships have been explored at multiple spatial scales. The analyses encompassed 20 seafloor, water‐column, and sea‐ice parameters, as well as abundances of 18 mega‐epibenthic taxa in a total of 2799 high‐resolution seabed images taken at 28 stations at 32–786 m depth off the northern Antarctic Peninsula. Based on a priori nesting of sampling stations into ecoregions, subregions, and habitats, analyses indicated most pronounced patchiness levels at finest (within transects among adjacent seabed photos) and largest (among ecoregions) spatial scale considered. Using an alternative approach, explicitly involving the spatial distances between the geo‐referenced data, Moran's Eigenvector mapping (MEM) classified the continuum of spatial scales into four categories: broad (&gt; 60 km), meso (10–60 km), small (2–10 km), and fine (&lt; 2 km). MEM analyses generally indicated an increase in mega‐epibenthic community complexity with increasing spatial scale. Moreover, strong relationships between biota and environmental drivers were found at scales of &gt; 2 km. In contrast, few environmental variables contributed to explaining biotic structures at finer scales. These are likely rather determined by nonmeasured environmental variables, as well as biological traits and interactions that are assumed to be most effective at small spatial scales.

Local environmental controls of Atlantic Forest tree community assembly on a coastal continental island in southeastern Brazil

ABSTRACT Tropical forests with high species diversity are commonly found in rugged montane areas. We investigated causes of local tropical forest tree community assembly on a continental island with heterogeneous terrain. We recorded tree community (absolute species abundance), topography, soil, litter and location in 40 sampling units on two opposite sides of the island with similar heterogeneous terrain. We used transformation based Redundancy Analyses and variation partitioning to determine the contribution of environment (topography, soil and litter), spatial structure (geographic location and Moran Eigenvector Maps) and the shared effects of these to explain community assembly. The environment made a significant contribution to explain tree community patterns (species composition and abundance) across all models. Conversely, spatial structure showed minor impact. Contribution of strictly environmental effects and spatially structured environmental effects varied when evaluating each site independently as well as when evaluating the combined data. Evidence suggests that local tropical tree community assembly on heterogeneous terrain may be located much closer to the niche end of the hypothetic niche-neutral continuum. Findings indicate additionally that heterogeneity of environmental factors present in dissected mountainous terrain can affect the way tropical community assembly processes are perceived.

Testing and interpreting the shared space‐environment fraction in variation partitioning analyses of ecological data

Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space‐environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially‐constrained null models: torus‐translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly‐distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (&lt; 0.05). The statistical power was high (&gt; 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine‐scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.

Spatial patterns in sub‐Arctic benthos: multiscale analysis reveals structural differences between community components

AbstractAn important goal for ecosystem‐based management is to protect marine habitats and their associated fauna. Understanding the spatial structure and interrelationships of benthic communities and their underlying environmental drivers, therefore, is of great importance. The benthic community off the sub‐Arctic Lofoten‐Vesterålen islands was studied on multiple spatial scales, using theMEM(Moran's eigenvector maps) framework to identify spatial structure in broadscale (hundreds of kilometers), mesoscale (20 km), and fine‐scale (1.5 km) epifaunal and infaunal communities. A combination of eigenvector‐based multivariate analyses and variation partitioning on multiple sets of explanatory variables was used to identify characteristic species and environmental drivers that have a high importance in structuring the communities. Community structure of both epifauna and infauna strongly reflected the boundary of warm Atlantic (&gt;0.5°C) and cold Arctic water masses (&lt;0.5°C), which coincides approximately with the 800‐m isobath in this region. Apart from this dominant broadscale determinant, epifauna and infauna displayed different spatial patterns and drivers. Both components differ significantly across local marine landscapes; however, this characterization is not sufficient for infauna, where additional sediment characteristics play an important role in structuring the community across all spatial scales. Within marine landscapes (mesoscale), epifauna displays a spatial structure that, for the most part, could not be attributed to any of the included environmental drivers. The case of the Lofoten‐Vesterålen region demonstrated that the used analysis is a valuable tool in spatial planning, as it allows for comparison of the relative importance of individual environmental drivers for the studied faunal components, without losing the information about spatial patterns associated with individual drivers.

Optimizing the choice of a spatial weighting matrix in eigenvector‐based methods

Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.

Application of Moran Eigenvector Maps (MEM) to irregular sampling designs

Moran’s eigenvectors maps (MEM) are attractive mathematical objects as they are fairly simple to calculate and can be used in most studies of spatially-explicit data. There is, however, an aspect of MEM analysis that still requires some investigation: the effect of irregular sampling on their modeling performance. This study investigates empirically the behavior of MEMs under different irregularity schemes. It is focusing on simulated scenarios representing sampling designs frequently encountered in ecology. We advocate that MEMs can be computed and correctly used with data coming from irregularly designed sampling surveys, given some precautions. We suggest that when the sampling sites are equally spaced but do not cover the entire study area, the MEMs can be computed directly on the coordinates of the sampling sites without any important loss of information. Whereas, when the phenomenon of interest is tackled using randomly stratified sampling designs, the MEMs should be computed on a reconstructed space of regular sampling sites followed by removal of the missing sites, before analysis. This solution of rebuilding a (regular) sampling space guarantees to capture the underlying process under study, improves the modeling results and relaxes the impact of the choice of the weighting matrix on the computation of MEMs.

Geographic isolation and larval dispersal shape seascape genetic patterns differently according to spatial scale.

Genetic variation, as a basis of evolutionary change, allows species to adapt and persist in different climates and environments. Yet, a comprehensive assessment of the drivers of genetic variation at different spatial scales is still missing in marine ecosystems. Here, we investigated the influence of environment, geographic isolation, and larval dispersal on the variation in allele frequencies, using an extensive spatial sampling (47 locations) of the striped red mullet (Mullus surmuletus) in the Mediterranean Sea. Univariate multiple regressions were used to test the influence of environment (salinity and temperature), geographic isolation, and larval dispersal on single nucleotide polymorphism (SNP) allele frequencies. We used Moran's eigenvector maps (db‐MEMs) and asymmetric eigenvector maps (AEMs) to decompose geographic and dispersal distances in predictors representing different spatial scales. We found that salinity and temperature had only a weak effect on the variation in allele frequencies. Our results revealed the predominance of geographic isolation to explain variation in allele frequencies at large spatial scale (>1,000 km), while larval dispersal was the major predictor at smaller spatial scale (<1,000 km). Our findings stress the importance of including spatial scales to understand the drivers of spatial genetic variation. We suggest that larval dispersal allows to maintain gene flows at small to intermediate scale, while at broad scale, genetic variation may be mostly shaped by adult mobility, demographic history, or multigenerational stepping‐stone dispersal. These findings bring out important spatial scale considerations to account for in the design of a protected area network that would efficiently enhance protection and persistence capacity of marine species.

Using functional trait diversity patterns to disentangle the scale‐dependent ecological processes in a subtropical forest

Abstract Disentangling ecological processes that influence community assembly and species diversity across spatial scales remains a major goal of community ecology. Community assembly processes influence spatial patterns of species diversity through their interactions with key functional traits. Hence, quantifying spatial patterns of functional trait diversity (FD) represents a useful tool for disentangling the relative importance of abiotic filtering, biotic interactions, random assembly and dispersal limitation across spatial scales. Here, we measured 12 traits of 112 study species in a 20‐ha fully mapped subtropical forest plot. The individuals of the 112 study species account for 99% of all living stems with diameter at breast height ≥ 1 cm. We studied important functional traits related to physiological processes of plants including resource acquisition (e.g. CO2 assimilation rate and leaf nutrient concentration) and drought tolerance (e.g. stem hydraulic conductivity and leaf turgor loss point). Additionally, species abundance, spatial locations (x‐ and y‐coordinates for each individual of the 112 study species) as well as topographic and soil variables that represent potentially important attributes of the physical environment of the plot were also included in our dataset. We employed two FD‐based tests (comparing FD within communities to those from random communities, distance‐based Moran's eigenvector maps (MEM) and redundancy analysis‐based variance partitioning), and one spatial analysis (inhomogeneous bivariate pair correlation analysis) to quantify the spatial patterns of FD of the plot at multiple spatial scales (400, 900, 1,600, 2,500 and 10,000 m2). We demonstrate that abiotic filtering is the major determinant responsible for trait convergence at relatively small scales (400, 900 and 1,600 m2), whereas dispersal limitation becomes dominant, causing the weakening of trait convergence at relatively large scales (2,500 and 10,000 m2). Our results highlight the relative contributions of different ecological processes to community assembly at different spatial scales, which can be distinguished using the diversity patterns of key functional traits. Also, our integrated approaches constitute a useful study design to disentangle variable ecological processes in shaping community assembly across spatial scales. A plain language summary is available for this article.