Metacommunity‐scale biodiversity regulation and the self‐organised emergence of macroecological patterns

There exist a number of key macroecological patterns whose ubiquity suggests that the spatio‐temporal structure of ecological communities is governed by some universal mechanisms. The nature of these mechanisms, however, remains poorly understood. Here, we probe spatio‐temporal patterns in species richness and community composition using a simple metacommunity assembly model. Despite making no a priori assumptions regarding biotic spatial structure or the distribution of biomass across species, model metacommunities self‐organise to reproduce well‐documented patterns including characteristic species abundance distributions, range size distributions and species area relations. Also in agreement with observations, species richness in our model attains an equilibrium despite continuous species turnover. Crucially, it is in the neighbourhood of the equilibrium that we observe the emergence of these key macroecological patterns. Biodiversity equilibria in models occur due to the onset of ecological structural instability, a population‐dynamical mechanism. This strongly suggests a causal link between local community processes and macroecological phenomena.     

Landscape structure,dispersal and the evolution of antagonistic plant–herbivore interactions

Different species have different dispersal capabilities and in the field, species interact with each other within dynamic, heterogeneous and complex landscapes. While plants and certain herbivore species may disperse considerable distances by means of seed dispersal or flight, other herbivores (e.g. root‐feeding nematodes or non‐winged insect herbivores) are more limited in their dispersal capacities. This difference in dispersal capabilities results in mosaics of plant–herbivore interactions that shift over time and space leading to spatio‐temporal variation in both the presence and absence of the species and their interactions. We developed an individual based simulation model in which we examined how multi‐species interactions are affected by their mobility within structurally complex landscapes. The main objective was to address the consequences for the arms race between plant defence and herbivore resistance to changes in fundamental landscape and community attributes. We demonstrate that feedbacks between landscape structure, community structure and the specific dispersal rate of the species involved affect the evolutionary dynamics between plants and herbivore antagonists. While three‐species interactions result in increased plant defence and herbivore resistance, effects of dispersal have diverse effects depending on the prevailing landscape structure.     

Zero‐inflated spatio‐temporal models for disease mapping

In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio‐temporal count data have excess zeros. To that end, we consider random effects in zero‐inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio‐temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B‐spline smoothing over the temporal dimension are proposed. The analysis of these complex models is computationally difficult from the frequentist perspective. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning method provides a frequentist approach to mixed models that is also computationally convenient. We propose to use data cloning, which yields to maximum likelihood estimation, to conduct frequentist analysis of zero‐inflated spatio‐temporal modeling of disease incidence. One of the advantages of the data cloning approach is that the prediction and corresponding standard errors (or prediction intervals) of smoothing disease incidence over space and time is easily obtained. We illustrate our approach using a real dataset of monthly children asthma visits to hospital in the province of Manitoba, Canada, during the period April 2006 to March 2010. Performance of our approach is also evaluated through a simulation study.     

Spatio‐temporal variation in European starling reproductive success at multiple small spatial scales

Understanding population dynamics requires spatio‐temporal variation in demography to be measured across appropriate spatial and temporal scales. However, the most appropriate spatial scale(s) may not be obvious, few datasets cover sufficient time periods, and key demographic rates are often incompletely measured. Consequently, it is often assumed that demography will be spatially homogeneous within populations that lack obvious subdivision. Here, we quantify small‐scale spatial and temporal variation in a key demographic rate, reproductive success (RS), within an apparently contiguous population of European starlings. We used hierarchical cluster analysis to define spatial clusters of nest sites at multiple small spatial scales and long‐term data to test the hypothesis that small‐scale spatio‐temporal variation in RS occurred. RS was measured as the number of chicks alive ca. 12 days posthatch either per first brood or per nest site per breeding season (thereby incorporating multiple breeding attempts). First brood RS varied substantially among spatial clusters and years. Furthermore, the pattern of spatial variation was stable across years; some nest clusters consistently produced more chicks than others. Total seasonal RS also varied substantially among spatial clusters and years. However, the magnitude of variation was much larger and the pattern of spatial variation was no longer temporally consistent. Furthermore, the estimated magnitude of spatial variation in RS was greater at smaller spatial scales. We thereby demonstrate substantial spatial, temporal, and spatio‐temporal variation in RS occurring at very small spatial scales. We show that the estimated magnitude of this variation depended on spatial scale and that spatio‐temporal variation would not have been detected if season‐long RS had not been measured. Such small‐scale spatio‐temporal variation should be incorporated into empirical and theoretical treatments of population dynamics.     

Accounting for imperfect observation and estimating true species distributions in modelling biological invasions

The documentation of biological invasions is often incomplete with records lagging behind the species’ actual spread to a spatio‐temporally heterogeneous extent. Such imperfect observation bears the risk of underestimating the already realised distribution of the invading species, misguiding management efforts and misjudging potential future impacts. In this paper, we develop a hierarchical modelling framework which disentangles the determinants of the invasion and observation processes, models spatio‐temporal heterogeneity in detection patterns, and infers the actual, yet partly undocumented distribution of the species at any particular time. We illustrate the model with a case study application to the invasion of common ragweed Ambrosia artemisiifolia in Austria. The invasion part of the model reconstructs the historical spread of this species across a grid of ～ 6 × 6 km² cells as driven by spatio‐temporal variation in physical site conditions, propagule production, dispersal, and ‘background’ introductions from unknown sources. The observation part models the detection of the species’ occurrences based on heterogeneous sampling efforts, human population density, and estimated local invasion level. We fitted the hierarchical model using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). The actual spread of A. artemisiifolia concentrated on the climatically well‐suited lowlands and was mainly driven by spatio‐temporal propagule pressure from source cells with long‐distance dispersal occurring rather frequently. Annual detection probabilities were estimated to vary between about 1 and up to 28%, depending mainly on sampling intensity. The model suggested that by 2005 about half of the actual distribution of the species was not yet documented. Our hierarchical model offers a flexible means to account for imperfect observation and spatio‐temporal variability in detection efficiency. Inferences can be used to disentangle aspects of the invasion dynamics itself from patterns of data collection, develop improved future surveying schemes, and design more efficient invasion management strategies.     

Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching

Shoot apical meristems (SAMs) of higher plants harbor stem‐cell niches. The cells of the stem‐cell niche are organized into spatial domains of distinct function and cell behaviors. A coordinated interplay between cell growth dynamics and changes in gene expression is critical to ensure stem‐cell homeostasis and organ differentiation. Exploring the causal relationships between cell growth patterns and gene expression dynamics requires quantitative methods to analyze cell behaviors from time‐lapse imagery. Although technical breakthroughs in live‐imaging methods have revealed spatio‐temporal dynamics of SAM‐cell growth patterns, robust computational methods for cell segmentation and automated tracking of cells have not been developed. Here we present a local graph matching‐based method for automated‐tracking of cells and cell divisions of SAMs of Arabidopsis thaliana. The cells of the SAM are tightly clustered in space which poses a unique challenge in computing spatio‐temporal correspondences of cells. The local graph‐matching principle efficiently exploits the geometric structure and topology of the relative positions of cells in obtaining spatio‐temporal correspondences. The tracker integrates information across multiple slices in which a cell may be properly imaged, thus providing robustness to cell tracking in noisy live‐imaging datasets. By relying on the local geometry and topology, the method is able to track cells in areas of high curvature such as regions of primordial outgrowth. The cell tracker not only computes the correspondences of cells across spatio‐temporal scale, but it also detects cell division events, and identifies daughter cells upon divisions, thus allowing automated estimation of cell lineages from images captured over a period of 72 h. The method presented here should enable quantitative analysis of cell growth patterns and thus facilitating the development of in silico models for SAM growth.     

A Markov approach for describing post-fire succession of vegetation

Vegetation dynamics in the coastal area of the Seto Inland Sea region in Japan, where wild fires occur frequently, were described using a stationary Markov model. In this region, vegetation types ofMiscanthus-Pleioblastus grassland,Lespedeza-Mallotus scrub,Pinus-Rhododendron forest andCrassocephalum-Erechtites community have been identified, and these show cyclic succession under the influence of fires. The model uses parameters determining fire frequency and rate of successional change to analyze the effect of variation in these parameters on the areal ratio of each vegetation type at equilibrium and on the time taken for one vegetation type to succeed another (elapsed successional time). The effect of fire frequency differs between hypothetical habitats with high and low productivity. A policy for vegetation management in areas of high and low productivity is proposed. The advantages and limitations of applying Markov models to studies of vegetation succession are also discussed.     

Spatial and temporal homogenisation of freshwater macrofaunal communities in ditches

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Fish communities diverge in species but converge in traits over three decades of warming

Describing the spatial and temporal dynamics of communities is essential for understanding the impacts of global environmental change on biodiversity and ecosystem functioning. Trait‐based approaches can provide better insight than species‐based (i.e. taxonomic) approaches into community assembly and ecosystem functioning, but comparing species and trait dynamics may reveal important patterns for understanding community responses to environmental change. Here, we used a 33‐year database of fish monitoring to compare the spatio‐temporal dynamics of taxonomic and trait structure in North Sea fish communities. We found that the majority of variation in both taxonomic and trait structure was explained by a pronounced spatial gradient, with distinct communities in the southern and northern North Sea related to depth, sea surface temperature, salinity and bed shear stress. Both taxonomic and trait structure changed significantly over time; however taxonomically, communities in the south and north diverged towards different species, becoming more dissimilar over time, yet they converged towards the same traits regardless of species differences. In particular, communities shifted towards smaller, faster growing species with higher thermal preferences and pelagic water column position. Although taxonomic structure changed over time, its spatial distribution remained relatively stable, whereas in trait structure, the southern zone of the North Sea shifted northward and expanded, leading to homogenization. Our findings suggest that global environmental change, notably climate warming, will lead to convergence towards traits more adapted for novel environments regardless of species composition.     

Insights into succession processes using temporally repeated habitat models: results from a long‐term study in a post‐mining landscape

Questions: The early phases of primary succession are governed by chance events and dispersal‐related processes in an environment that is largely free of competition. Thus, the predictability of vegetation patterns using environmental site factors can be expected to be low and spatial autocorrelation to be high. We asked whether the match between vegetation and environment becomes better in the course of succession, and whether vegetation types shift their realized niche with time. Location: Lignite mining region in Central Germany, the post‐mining landscape “Goitzsche” (Saxony‐Anhalt). Methods: Vegetation types were mapped in a 10‐m grid (total area 4.8 ha), starting in 1995, at 3‐year intervals until 2007. We used a temporal comparison of habitat models. We applied: GLS regression to partition the variation in coverage of vegetation types into environmental (soil pH) and spatial components; logistic regression to model the presence/absence of vegetation types along a soil acidity gradient; and autologistic regression allowing for soil acidity and neighbourhood effects. Results: For most vegetation types, the proportion of variation explained by space was high but declined during succession. The outcome of autologistic models suggests that soil acidity often plays a minor role compared to neighbourhood effects in the earlier phase of succession than 12 years later. For four vegetation types, the pH range in which the type was expected to be dominant clearly became smaller with time. These trends support the view that the role of processes related to chance and dispersal decrease with time, while those related to environmental filtering mediated by biotic interactions increase. Conclusions: We conclude that temporal comparisons of spatially explicit habitat models provide insights into changing biotic community processes and their effects on habitat specificity of species or their communities. Thus, this approach may be particularly important for analysis of ecological systems that are not in equilibrium with their environmental drivers.     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

Bayesian Finite Markov Mixture Model for Temporal Multi‐Tissue Polygenic Patterns

Finite mixture models can provide the insights about behavioral patterns as a source of heterogeneity of the various dynamics of time course gene expression data by reducing the high dimensionality and making clear the major components of the underlying structure of the data in terms of the unobservable latent variables. The latent structure of the dynamic transition process of gene expression changes over time can be represented by Markov processes. This paper addresses key problems in the analysis of large gene expression data sets that describe systemic temporal response cascades and dynamic changes to therapeutic doses in multiple tissues, such as liver, skeletal muscle, and kidney from the same animals. Bayesian Finite Markov Mixture Model with a Dirichlet Prior is developed for the identifications of differentially expressed time related genes and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. The proposed Bayesian models are applied to multiple tissue polygenetic temporal gene expression data and compared to a Bayesian model‐based clustering method, named CAGED. Results show that our proposed Bayesian Finite Markov Mixture model can well capture the dynamic changes and patterns for irregular complex temporal data (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Top predators determine how biodiversity is partitioned across time and space

Natural ecosystems are shaped along two fundamental axes, space and time, but how biodiversity is partitioned along both axes is not well understood. Here, we show that the relationship between temporal and spatial biodiversity patterns can vary predictably according to habitat characteristics. By quantifying seasonal and annual changes in larval dragonfly communities across a natural predation gradient we demonstrate that variation in the identity of top predator species is associated with systematic differences in spatio‐temporal β‐diversity patterns, leading to consistent differences in relative partitioning of biodiversity between time and space across habitats. As the size of top predators increased (from invertebrates to fish) habitats showed lower species turnover across sites and years, but relatively larger seasonal turnover within a site, which ultimately shifted the relative partitioning of biodiversity across time and space. These results extend community assembly theory by identifying common mechanisms that link spatial and temporal patterns of β‐diversity.     

Plant diversity increases spatio‐temporal niche complementarity in plant‐pollinator interactions

Ongoing biodiversity decline impairs ecosystem processes, including pollination. Flower visitation, an important indicator of pollination services, is influenced by plant species richness. However, the spatio‐temporal responses of different pollinator groups to plant species richness have not yet been analyzed experimentally. Here, we used an experimental plant species richness gradient to analyze plant–pollinator interactions with an unprecedented spatio‐temporal resolution. We observed four pollinator functional groups (honeybees, bumblebees, solitary bees, and hoverflies) in experimental plots at three different vegetation strata between sunrise and sunset. Visits were modified by plant species richness interacting with time and space. Furthermore, the complementarity of pollinator functional groups in space and time was stronger in species‐rich mixtures. We conclude that high plant diversity should ensure stable pollination services, mediated via spatio‐temporal niche complementarity in flower visitation.     

Markov and Semi‐Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components

Summary Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time‐varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi‐Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi‐Markovian manner. The underlying semi‐Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi‐Markov chain represent—in the corresponding growth phase—both the influence of time‐varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi‐Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation–maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates.     

Vectors with autonomy: what distinguishes animal‐mediated nutrient transport from abiotic vectors?

Animal movements are important drivers of nutrient redistribution that can affect primary productivity and biodiversity across various spatial scales. Recent work indicates that incorporating these movements into ecosystem models can enhance our ability to predict the spatio‐temporal distribution of nutrients. However, the role of animal behaviour in animal‐mediated nutrient transport (i.e. active subsidies) remains under‐explored. Here we review the current literature on active subsidies to show how the behaviour of active subsidy agents makes them both ecologically important and qualitatively distinct from abiotic processes (i.e. passive subsidies). We first propose that animal movement patterns can create similar ecological effects (i.e. press and pulse disturbances) in recipient ecosystems, which can be equal in magnitude to or greater than those of passive subsidies. We then highlight three key behavioural features distinguishing active subsidies. First, organisms can transport nutrients counter‐directionally to abiotic forces and potential energy gradients (e.g. upstream). Second, unlike passive subsidies, organisms respond to the patterns of nutrients that they generate. Third, animal agents interact with each other. The latter two features can form positive‐ or negative‐feedback loops, creating patterns in space or time that can reinforce nutrient hotspots in places of mass aggregations and/or create lasting impacts within ecosystems. Because human‐driven changes can affect both the space‐use of active subsidy species and their composition at both population (i.e. individual variation) and community levels (i.e. species interactions), predicting patterns in nutrient flows under future modified environmental conditions depends on understanding the behavioural mechanisms that underlie active subsidies and variation among agents' contributions. We conclude by advocating for the integration of animal behaviour, animal movement data, and individual variation into future conservation efforts in order to provide more accurate and realistic assessments of changing ecosystem function.     

Population and evolutionary dynamics in spatially structured seasonally varying environments

Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature. Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.     

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

气候变化对森林演替的影响

森林演替是森林生态动力源驱动下森林再生的生态学过程,自20世纪初建立群落演替理论以来,演替研究成为生态学研究中的热点．客观准确地认识森林演替规律,研究森林演替动力学机理及其模型,是科学管理森林生态系统的需要;对于天然林保护工程与森林植被的恢复重建,具有重要的理论与实际意义．干扰是森林循环的驱动力,导致森林生态系统时空异质性,是更新格局和生态学过程的主要影响因素．它可改变资源的有效性,干扰导致的林隙是森林循环的起点．回顾了目前演替研究的几种方法,即马尔科夫模型、林窗模型(GAP)、陆地生物圈模型(BIOME)和非线性演替模式．介绍了气候变化对森林演替的影响;并在已有成果的基础上,提出了目前研究存在的问题及未来的发展方向．     

Stable baselines of temporal turnover underlie high beta diversity in tropical arthropod communities

While the high species diversity of tropical arthropod communities has often been linked to marked spatial heterogeneity, their temporal dynamics have received little attention. This study addresses this gap by examining spatio‐temporal variation in the arthropod communities of a tropical montane forest in Honduras. By employing DNA barcode analysis and Malaise trap sampling across 4 years and five sites, 51,596 specimens were assigned to 8,193 presumptive species. High beta diversity was linked more strongly to elevation than geographic distance, decreasing by 12% when only the dominant species were considered. When sampling effort was increased by deploying more traps at a site, beta diversity only decreased by 2%, but extending sampling across years decreased beta diversity by 27%. Species inconsistently detected among years, likely transients from other settings, drove the low similarity in species composition among traps only a few metres apart. The dominant, temporally persistent species substantially influenced the cyclic pattern of change in community composition among years. This pattern likely results from divergence–convergence dynamics, suggesting a stable baseline of temporal turnover in each community. The overall results establish that large sample sizes are necessary to reveal species richness, but are not essential for quantifying beta diversity. This study further highlights the need for standardized methods of sampling and species identification to generate the comparative data required to evaluate biodiversity change in space and time.