Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

Multifaceted aspects of synchrony between freshwater prokaryotes and protists

Community composition of freshwater prokaryotes and protists varies through time. Few studies contemporarily investigate temporal variation of these freshwater communities for more than 1 year. We compared the temporal patterns of prokaryotes and protists in three distinct habitats for 4 years (2014–2017) in Lake Tovel, a cold‐water lake. This lake showed a marked temperature increase in 2017 linked to altered precipitation patterns. We investigated whether microbial communities reflected this change across habitats and whether changes occurred at the same time and to the same extent. Furthermore, we tested the concept of hydrological year emphasizing the ecological effect of water renewal on communities for its explanatory power of community changes. Microbe diversity was assessed by Illumina sequencing of the V3–V4 hypervariable region of the 16S rRNA gene and 18S rRNA gene, and we applied co‐inertia analysis and asymmetric eigenvector maps modelling to infer synchrony and temporal patterns of prokaryotes and protists. When considering community composition, microbes were invariable in synchrony across habitats and indicated a temporal gradient linked to decreasing precipitation; however, when looking at temporal patterns, the extent of synchrony was reduced. Small‐scale patterns were similar across habitats and microbes and linked to seasonally varying environmental variables, while large‐scale patterns were different and partially linked to an ecosystem change as indicated by increasing water transparency and temperature and decreasing dissolved oxygen. Our advanced statistical approach outlined the multifaceted aspect of synchrony when linked to community composition and temporal patterns.     

Environmental and spatial processes influencing phytoplankton biomass along a reservoirs‐river‐floodplain lakes gradient: A metacommunity approach

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Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

Wind dispersal results in a gradient of dispersal limitation and environmental match among discrete aquatic habitats

Directional dispersal by wind and other dispersal agents may generate spatial patterns in passively dispersing metacommunities which cannot be detected by classical eigenvector methods based on Euclidean distances. We analysed zooplankton communities (Rotifera, Cladocera, Copepoda) in a cluster of soda pans distributed over a short spatial scale of 18 km and tested explicitly for directional signals in their spatial configuration. The study area is exposed to a prevailing northwestern wind direction. By applying asymmetric eigenvector maps (AEM), we were able to identify corresponding directionality in the spatial structure of communities. Furthermore, the match between community composition and environmental conditions exhibited a spatial pattern consistent with the prevailing wind corridor, with best match found downwind the dominant wind direction. We also found that classical eigenvector methods based on Euclidean distances underestimated the role of spatial processes in our data. Our study furthermore shows that dispersal limitation may constrain community assembly in highly mobile organisms even at spatial scales below 5 km.     

The role of environmental and spatial processes in structuring native and non‐native fish communities across thousands of lakes

Quantifying the role of spatial patterns is an important goal in ecology to further understand patterns of community composition. We quantified the relative role of environmental conditions and regional spatial patterns that could be produced by environmental filtering and dispersal limitation on fish community composition for thousands of lakes. A database was assembled on fish community composition, lake morphology, water quality, climatic conditions, and hydrological connectivity for 9885 lakes in Ontario, Canada. We utilized a variation partitioning approach in conjunction with Moran's Eigenvector Maps (MEM) and Asymmetric Eigenvector Maps (AEM) to model spatial patterns that could be produced by human‐mediated and natural modes of dispersal. Across 9885 lakes and 100 fish species, environmental factors and spatial structure explained approximately 19% of the variation in fish community composition. Examining the proportional role of spatial structure and environmental conditions revealed that as much as 90% of the explained variation in native species assemblage composition is governed by environmental conditions. Conversely on average, 67% of the explained variation in non‐native assemblage composition can be related to human‐mediated dispersal. This study highlights the importance of including spatial structure and environmental conditions when explaining patterns of community composition to better discriminate between the ecological processes that underlie biogeographical patterns of communities composed of native and non‐native fish species.     

Inferring the Relative Resilience of Alternative States

Ecological systems may occur in alternative states that differ in ecological structures, functions and processes. Resilience is the measure of disturbance an ecological system can absorb before changing states. However, how the intrinsic structures and processes of systems that characterize their states affects their resilience remains unclear. We analyzed time series of phytoplankton communities at three sites in a floodplain in central Spain to assess the dominant frequencies or “temporal scales” in community dynamics and compared the patterns between a wet and a dry alternative state. The identified frequencies and cross-scale structures are expected to arise from positive feedbacks that are thought to reinforce processes in alternative states of ecological systems and regulate emergent phenomena such as resilience. Our analyses show a higher species richness and diversity but lower evenness in the dry state. Time series modeling revealed a decrease in the importance of short-term variability in the communities, suggesting that community dynamics slowed down in the dry relative to the wet state. The number of temporal scales at which community dynamics manifested, and the explanatory power of time series models, was lower in the dry state. The higher diversity, reduced number of temporal scales and the lower explanatory power of time series models suggest that species dynamics tended to be more stochastic in the dry state. From a resilience perspective our results highlight a paradox: increasing species richness may not necessarily enhance resilience. The loss of cross-scale structure (i.e. the lower number of temporal scales) in community dynamics across sites suggests that resilience erodes during drought. Phytoplankton communities in the dry state are therefore likely less resilient than in the wet state. Our case study demonstrates the potential of time series modeling to assess attributes that mediate resilience. The approach is useful for assessing resilience of alternative states across ecological and other complex systems.     

Identifying resilience mechanisms to recurrent ecosystem perturbations

The complex nature of ecological systems limits the unambiguous determination of mechanisms that drive resilience to natural disturbance or anthropogenic stress. Using eight-year time series data from boreal lakes with and without bloom formation of an invasive alga (Gonyostomum semen, Raphidophyceae), we studied resilience of phytoplankton communities in relation to recurring bloom impacts. We first characterized phytoplankton community dynamics in both lake types using univariate metrics of community structure (evenness, species richness, biovolume and Simpson diversity). All metrics, except species richness, were substantially altered and showed an inherent stronger variability in bloom lakes relative to reference lakes. We assessed resilience mechanisms using a multivariate time series modelling technique. The models captured clear successional dynamics of the phytoplankton communities in all lakes, whereby different groups of species were substituted sequentially over the ice-free period. The models also identified that G. semen impacts in bloom lakes were only manifested within a single species group, not across species groups, highlighting the rapid renewal of the phytoplankton communities upon bloom collapse. These results provide empirical support of the cross-scale resilience model. Cross-scale resilience could provide an explanation for the paradox that similar species richnesses are seen in bloom-forming lakes and reference lakes despite the clear difference between the community features of the two different sets of lakes investigated.     

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

Aiming to elucidate whether large‐scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in‐lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in‐lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.     

森林害虫发生的空间模式研究进展

森林昆虫种群表现出多样的时空模式,空间同步性是其中最常见的。回顾了森林昆虫空间同步性的特点、形成机制及研究方法方面的进展。森林害虫发生的同步性是广泛存在的,但不同昆虫种类的同步性大小不同。空间同步性常随距离的增大而下降,还与时间尺度有关。Moran效应和扩散是解释空间同步性的两种主要机制,通常Moran效应的影响要比扩散大。从虫害发生数据的获取、同步性的度量及成因3个方面介绍了空间同步性的研究方法方面的进展。利用树轮生态学原理重建森林虫害发生历史的方法可在事后获取可靠的数据,很值得国内研究者借鉴和应用。在空间自相关度量上,空间统计学方法和地统计学方法都是非常有力的手段,但由于不能处理多时间点数据而限制了其在同步性研究中的应用。在同步性成因研究中,利用变异分解将基于距离的Moran特征向量图(dbMEM)作为空间变量研究害虫发生的驱动力是比较新颖的研究方法。     

New Directions for Understanding the Spatial Resilience of Social–Ecological Systems

The concept of spatial resilience has brought a new focus on the influence of multi-scale processes on the dynamics of ecosystems. Initial ideas about spatial resilience focused on coral reefs and emphasized escalating anthropogenic disturbances across the broader seascape. This perspective resonated with a new awareness of global drivers of change, such as growth in international trade and shifts in climate, and the need to respond by scaling up governance and management. We review recent trends and emerging ideas in spatial resilience, using coral reefs and dependent communities as exemplars of multi-scale social–ecological systems. Despite recent advances, management and governance of ecosystems remain spatially fragmented and constrained to small scales. Temporally, many interventions still miss or ignore warning signals and struggle to cope with history, politics, long-term cumulative pressures, feedbacks, and sudden surprises. Significant recent progress has been made in understanding the relevance of spatial and temporal scale, heterogeneity, networks, the importance of place, and multi-scale governance. Emerging themes include better integration of ecology and conservation with social and economic science, and incorporating temporal dynamics in spatial analyses. A better understanding of the multi-scale spatial and temporal processes that drive the resilience of linked social-ecosystems will help address the widespread mismatch between the scales of ongoing ecological change and effective long-term governance of land- and seascapes.     

SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S‐SDM) starts with constituent species to approximate the properties of assemblages. Here, we propose to unify the two approaches in a single ‘spatially explicit species assemblage modelling’ (SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio‐temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.     

Modeling body size evolution in Felidae under alternative phylogenetic hypotheses

The use of phylogenetic comparative methods in ecological research has advanced during the last twenty years, mainly due to accurate phylogenetic reconstructions based on molecular data and computational and statistical advances. We used phylogenetic correlograms and phylogenetic eigenvector regression (PVR) to model body size evolution in 35 worldwide Felidae (Mammalia, Carnivora) species using two alternative phylogenies and published body size data. The purpose was not to contrast the phylogenetic hypotheses but to evaluate how analyses of body size evolution patterns can be affected by the phylogeny used for comparative analyses (CA). Both phylogenies produced a strong phylogenetic pattern, with closely related species having similar body sizes and the similarity decreasing with increasing distances in time. The PVR explained 65% to 67% of body size variation and all Moran's I values for the PVR residuals were non-significant, indicating that both these models explained phylogenetic structures in trait variation. Even though our results did not suggest that any phylogeny can be used for CA with the same power, or that "good" phylogenies are unnecessary for the correct interpretation of the evolutionary dynamics of ecological, biogeographical, physiological or behavioral patterns, it does suggest that developments in CA can, and indeed should, proceed without waiting for perfect and fully resolved phylogenies.     

Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics

Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.     

Hierarchical Dynamics of Ecological Communities: Do Scales of Space and Time Match?

Theory posits that community dynamics organize at distinct hierarchical scales of space and time, and that the spatial and temporal patterns at each scale are commensurate. Here we use time series modeling to investigate fluctuation frequencies of species groups within invertebrate metacommunities in 26 boreal lakes over a 20-year period, and variance partitioning analysis to study whether species groups with different fluctuation patterns show spatial signals that are commensurate with the scale-specific fluctuation patterns identified. We identified two groups of invertebrates representing hierarchically organized temporal dynamics: one species group showed temporal variability at decadal scales (slow patterns of change), whilst another group showed fluctuations at 3 to 5-year intervals (faster change). This pattern was consistently found across all lakes studied. A spatial signal was evident in the slow but not faster-changing species groups. As expected, the spatial signal for the slow-changing group coincided with broad-scale spatial patterns that could be explained with historical biogeography (ecoregion delineation, and dispersal limitation assessed through a dispersal trait analysis). In addition to spatial factors, the slow-changing groups correlated with environmental variables, supporting the conjecture that boreal lakes are undergoing environmental change. Taken together our results suggest that regionally distinct sets of taxa, separated by biogeographical boundaries, responded similarly to broad-scale environmental change. Not only does our approach allow testing theory about hierarchically structured space-time patterns; more generally, it allows assessing the relative role of the ability of communities to track environmental change and dispersal constraints limiting community structure and biodiversity at macroecological scales.     

Riverine landscape dynamics and ecological risk assessment

1. The aim of ecological risk assessments is to evaluate the likelihood that ecosystems are adversely affected by human‐induced disturbance that brings the ecosystem into a new dynamic equilibrium with a simpler structure and lower potential energy. The risk probability depends on the threshold capacity of the system (resistance) and on the capacity of the system to return to a state of equilibrium (resilience). 2. There are two complementary approaches to assessing ecological risks of riverine landscape dynamics. The reductionist approach aims at identifying risk to the ecosystem on the basis of accumulated data on simple stressor–effect relationships. The holistic approach aims at taking the whole ecosystem performance into account, which implies meso‐scale analysis. 3. Landscape patterns and their dynamics represent the physical framework of processes determining the ecosystem's equilibrium. Assessing risks of landscape dynamics to riverine ecosystems implies addressing complex interactions of system components (e.g. population dynamics and biogeochemical cycles) occurring at multiple scales of space and time. 4. One of the most important steps in ecological risk assessment is to establish clear assessment endpoints (e.g. vital ecosystem and landscape attributes). Their formulation must recognise that riverine ecosystems are dynamic, structurally complex and composed of both deterministic and stochastic components. 5. Remote sensing (geo)statistics and geographical information systems are primary tools for quantifying spatial and temporal components of riverine ecosystem and landscape attributes. 6. The difficulty to experiment at the riverine landscape level means that ecological risk management is heavily dependent on models. Current models are targeted towards simulating ecological risk at levels ranging from single species to habitats, food webs and meta‐populations to ecosystems and entire riverine landscapes, with some including socio‐economic considerations.     

Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account

The hindcast of shifts in the geographical ranges of species as estimated by ecological niche modelling (ENM) has been coupled with phylogeographical patterns, allowing the inference of past processes that drove population differentiation and genetic variability. However, more recently, some studies have suggested that maps of environmental suitability estimated by ENM may be correlated to species' abundance, raising the possibility of using environmental suitability to infer processes related to population demographic dynamics and genetic variability. In both cases, one of the main problems is that there is a wide variation in ENM development methods and climatic models. In this study, we analyse the relationship between heterozygosity (He) and environmental suitability from multiple ENMs for 25 population estimates for Dipteryx alata, a widely distributed, endemic tree species of the Cerrado region of central Brazil. We propose a new approach for generating a statistical distribution of correlations under randomly generated ENM. The confidence intervals from these distributions indicate how model selection with different properties affects the ability to detect a correlation of interest (e.g. the correlation between He and suitability). Additionally, our approach allows us to explore which particular ensemble of ENMs produces the better result for finding an association between environmental suitability and He. Caution is necessary when choosing a method or a climatic data set for modelling geographical distributions, but the new approach proposed here provides a conservative way to evaluate the ability of ensembles to detect patterns of interest.     

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.