Ecological Monographs最新文献

Despite the well known scale-dependency of ecological interactions, relatively little attention has been paid to understanding the dynamic interplay between various spatial scales. This is especially notable in metacommunity theory, where births and deaths dominate dynamics within patches (the local scale), and dispersal and environmental stochasticity dominate dynamics between patches (the regional scale). By considering the interplay of local and regional scales in metacommunities, the fundamental processes of community ecology—selection, drift, and dispersal—can be unified into a single theoretical framework. Here, we analyze three related spatial models that build on the classic two-species Lotka–Volterra competition model. Two open-system models focus on a single patch coupled to a larger fixed landscape by dispersal. The first is deterministic, while the second adds demographic stochasticity to allow ecological drift. Finally, the third model is a true metacommunity model with dispersal between a large number of local patches, which allows feedback between local and regional scales and captures the well studied metacommunity paradigms as special cases. Unlike previous simulation models, our metacommunity model allows the numerical calculation of equilibria and invasion criteria to precisely determine the outcome of competition at the regional scale. We show that both dispersal and stochasticity can lead to regional outcomes that are different than predicted by the classic Lotka–Volterra competition model. Regional exclusion can occur when the nonspatial model predicts coexistence or founder control, due to ecological drift or asymmetric stochastic switching between basins of attraction, respectively. Regional coexistence can result from local coexistence mechanisms or through competition-colonization or successional-niche trade-offs. Larger dispersal rates are typically competitively advantageous, except in the case of local founder control, which can favor intermediate dispersal rates. Broadly, our models demonstrate the importance of feedback between local and regional scales in competitive metacommunities and provide a unifying framework for understanding how selection, drift, and dispersal jointly shape ecological communities.

Multiyear periods (≥4 years) of extreme rainfall are increasing in frequency as climate continues to change, yet there is little understanding of how rainfall amount and heterogeneity in biophysical properties affect state changes in a sequence of wet and dry periods. Our objective was to examine the importance of rainfall periods, their legacies, and vegetation and soil properties to either the persistence of woody plants or a shift toward perennial grass dominance and a state reversal. We examined a 28-year record of rainfall consisting of a sequence of multiyear periods (average, dry, wet, dry, average) for four ecosystem types in the Jornada Basin. We analyzed relationships between above ground net primary production (ANPP) and rainfall for three plant functional groups that characterize alternative states (perennial grasses, other herbaceous plants, dominant shrubs). A multimodel comparison was used to determine the relative importance of rainfall, soil, and vegetation properties. For perennial grasses, the greatest mean ANPP in mesquite- and tarbush-dominated shrublands occurred in the wet period and in the dry period following the wet period in grasslands. Legacy effects in grasslands were asymmetric, where the lowest production was found in a dry period following an average period, and the greatest production occurred in a dry period following a wet period. For other herbaceous plants, in contrast, the greatest ANPP occurred in the wet period. Mesquite was the only dominant shrub species with a significant positive response in the wet period. Rainfall amount was a poor predictor of ANPP for each functional group when data from all periods were combined. Initial herbaceous biomass at the plant scale, patch-scale biomass, and soil texture at the landscape scale improved the predictive relationships of ANPP compared with rainfall alone. Under future climate, perennial grass production is expected to benefit the most from wet periods compared with other functional groups with continued high grass production in subsequent dry periods that can shift (desertified) shrublands toward grasslands. The continued dominance by shrubs will depend on the effects that rainfall has on perennial grasses and the sequence of high- and low-rainfall periods rather than the direct effects of rainfall on shrub production.

Understanding ecological dynamics has been a central topic in ecology since its origins. Yet, identifying dynamic regimes remains a research frontier for modern ecology. The concept of ecological dynamic regime (EDR) emerged to emphasize the dynamic property of steady states in nature and refers to the fluctuations of ecosystems around some trend or average. Identifying and characterizing EDRs is of utmost importance in the current context of global change since they form the reference against which post-disturbance dynamics must be compared to assess ecological resilience. However, the implementation of EDRs in empirical science is still challenging given the high dimensionality and stochasticity of ecological data and the large volume of data required to distinguish stochastic dynamics from general and predictable dynamics. The era of big data and the recent advances in quantitative ecology and data science offer an opportunity to study dynamic regimes using empirical approaches from a new perspective. This paper presents a novel methodological framework to describe EDRs from a set of ecological trajectories defined by the temporal changes of state variables in a multidimensional state space. In our framework, we formally define EDRs and include analytical tools to identify, characterize, and compare EDRs based on their geometric characteristics. More specifically, we propose different ways to identify EDRs from empirical data, develop a new algorithm to identify representative trajectories summarizing the main dynamic patterns, propose a set of metrics to describe the internal distribution of ecological trajectories, and define a dissimilarity index to compare two or more dynamic regimes based on their shape and position in the state space. We used artificial data to illustrate the different elements of our framework and applied our analyses to real data, using permanent sampling plots of Canadian boreal forests as an example. Overall, our framework contributes to filling the gap between theoretical and empirical ecology by providing robust analytical tools to assess ecological resilience and study ecosystem dynamics from a multidimensional perspective and considering the variability of natural systems.

Based on sampling data, we propose a rigorous standardization method to measure and compare beta diversity across datasets. Here beta diversity, which quantifies the extent of among-assemblage differentiation, relies on Whittaker's original multiplicative decomposition scheme, but we use Hill numbers for any diversity order q ≥ 0. Richness-based beta diversity (q = 0) quantifies the extent of species identity shift, whereas abundance-based (q > 0) beta diversity also quantifies the extent of difference among assemblages in species abundance. We adopt and define the assumptions of a statistical sampling model as the foundation for our approach, treating sampling data as a representative sample taken from an assemblage. The approach makes a clear distinction between the theoretical assemblage level (unknown properties/parameters of the assemblage) and the sampling data level (empirical/observed statistics computed from data). At the assemblage level, beta diversity for N assemblages reflects the interacting effect of the species abundance distribution and spatial/temporal aggregation of individuals in the assemblage. Under independent sampling, observed beta (= gamma/alpha) diversity depends not only on among-assemblage differentiation but also on sampling effort/completeness, which in turn induces dependence of beta on alpha and gamma diversity. How to remove the dependence of richness-based beta diversity on its gamma component (species pool) has been intensely debated. Our approach is to standardize gamma and alpha based on sample coverage (an objective measure of sample completeness). For a single assemblage, the iNEXT method was developed, through interpolation (rarefaction) and extrapolation with Hill numbers, to standardize samples by sampling effort/completeness. Here we adapt the iNEXT standardization to alpha and gamma diversity, that is, alpha and gamma diversity are both assessed at the same level of sample coverage, to formulate standardized, coverage-based beta diversity. This extension of iNEXT to beta diversity required the development of novel concepts and theories, including a formal proof and simulation-based demonstration that the resulting standardized beta diversity removes the dependence of beta diversity on both gamma and alpha values, and thus reflects the pure among-assemblage differentiation. The proposed standardization is illustrated with spatial, temporal, and spatiotemporal datasets, while the freeware iNEXT.beta3D facilitates all computations and graphics.

Alternative states defined by tree-canopy dominance result in different ecosystem functioning and shape habitat conditions for the understory vegetation. One example in the boreal forest is the alternation between broadleaf deciduous and coniferous forests. Disturbances related to natural fires and human land uses have produced changes in tree-canopy dominance in the boreal region where coniferous forests change to broadleaved forests, affecting understory community dynamics and their related ecosystem processes and functions. To analyze the factors driving changes in understory vegetation and the resistance of its vegetation to shifts between alternative states, we compared the effects of changes in the system between two contrasting boreal forest types (black spruce vs. trembling aspen) in adjacent stands with similar topoedaphic conditions. We performed a 5-year in situ experiment using alternative states as a theoretical framework including two approaches: (1) the ecosystem approach, manipulating environmental conditions of light, litter, and nutrients in each forest type to determine the main mechanisms associated with tree-canopy dominance that affect the diversity and composition of understory communities; and (2) the community approach, physically exchanging understory communities between alternative states, to determine their resistance under a new tree-canopy dominance through time, as well as the resilience of the forest understory after a small-scale disturbance. Results indicate that the understory vegetation of trembling aspen forests were resistant through time both after changes in local conditions in the ecosystem approach and in the new black spruce-dominated alternative state in the community approach. In contrast, mosses and ericaceous plants that typically dominate the forest floor of black spruce forests were negatively affected by the physical effect of broadleaf litter addition in our ecosystem approach and they were not resistant when transplanted to trembling aspen forests in the community approach, as they decreased in abundance and were invaded by aspen understory community species over time. The understory vegetation is a key forest ecosystem driver that can contribute to maintain the resilience of the boreal system and help to preserve their ecosystem services, which is a key aspect to consider in forest management faced with the effects of climate change.

Declines in habitat structural complexity have marked ecological outcomes, as currently observed in many of the world's ecosystems. Coral reefs have provided a model for such changes in marine ecosystems; still our understanding has been centered on corals and fishes at broad spatial scales when metazoan diversity on coral reefs is dominated by small cryptic taxa (herein: “cryptofauna”). Given the paucity of studies and high taxonomic complexity of the cryptofauna, both of which limit a priori hypotheses, we asked whether hierarchical structuring theory provides a compelling framework to impose order and quantify patterns. In general terms, we explored whether cryptic communities are sufficiently described by broad seascape parameters or limited by a set of processes operating at their distinctly nested microhabitat scale. To address this theory and gaps in knowledge for the cryptofauna, we characterized community structure in coral rubble, an eroded coral condition where biodiversity proliferates. Rubble was sampled along a depth and exposure gradient at Heron Island on the Great Barrier Reef, Australia, to parameterize environmental and morphological indicators of sessile taxa and motile cryptofauna communities. We used a hierarchical study framework from microhabitat to seascape scales, which were evaluated using nonstructured multivariate analyses and Bayesian structural equation modeling. While the nonstructured analyses showed the effects of seascape on the cryptobenthos and its community, this approach overlooked the finer hierarchical patterns in rubble ecology revealed only in the structured model. Seascape parameters (exposure and depth) influenced microhabitat complexity (i.e., rubble branchiness), which determined the cover of sessile organisms on rubble pieces, which shaped the motile cryptofauna community. Rubble is likely to be increasingly prevalent on coral reefs in the Anthropocene and is typically associated with low seascape-level complexity and reduced macrofaunal richness. Parallel with hierarchical structuring theory, we showed a similar response operating at the microhabitat scale whereby low rubble complexity (i.e., branchiness) reduced cryptobenthic structure, diversity and size spectra. In a future ocean, we expect there may be an initial increase in biodiversity and trophodynamic processes derived from branching rubble, but a delay in ecosystem-scale outcomes if coral, and thus rubble, generation and complexity is not sustained.

The temporal storage effect—that species coexist by partitioning abiotic niches that vary in time—is thought to be an important explanation for how species coexist. However, empirical studies that measure multiple mechanisms often find the storage effect is weak. We believe this mismatch is because of a shortcoming of theoretical models used to study the storage effect: that while the storage effect is described as having just three requirements (partitioning of temporal variation, buffered population growth, and a covariance between environment and density-dependence), models used to study the storage effect make four assumptions, which are mathematically subtle but biologically important. In this paper, we examine those assumptions. First, models assume that environmental variation leads to a rapid impact on density-dependence. We find that delays in density-dependence (including delays caused by competition between cohorts) weaken the storage effect. Second, models assume that intraspecific competition is almost identical to interspecific competition. We find that unless resource or predator partitioning are virtually absent, then variation-independent mechanisms will overshadow the benefits of the storage effect. Third, models assume even though there is vast variation in the environment, species are equally adapted on average (i.e., zero fitness-differences). We show that fitness differences are particularly problematic in the storage effect because specializing on temporally rare niches is far less effective than specializing on other types of rare niches. Finally, models assume that stochastic extinctions can be ignored, and invader growth can determine coexistence. We show that storage effects tend to reduce mean persistence times, even if invader growth rates are positive. These results suggest that the assumptions needed for the storage effect are strict: if the first or second assumption is relaxed, it will greatly weaken the stabilizing mechanism; if the third or fourth assumption is relaxed, it will create a diversity-destroying effect that may undermine coexistence. We examine three real-world communities—annual plants, tropical forests, and iguanid lizards—and find that empirical studies suggest that all three communities violate multiple assumptions. This suggests that the temporal storage effect is probably not an important explanation for species diversity in most systems.

Ongoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well studied groups, like butterflies, information relevant to species-specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a minuscule fraction of insect diversity, including a few well studied butterflies. Here we bring together heterogeneous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at-risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in the more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, including Vanessa annabella, Thorybes mexicanus, Euchloe ausonides, and Pholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

The encroachment of woody plants into grasslands is a global phenomenon with implications for biodiversity and ecosystem function. Understanding and predicting the pace of expansion and the underlying processes that control it are key challenges in the study and management of woody encroachment. Theory from spatial population biology predicts that the occurrence and speed of expansion should depend sensitively on the nature of conspecific density dependence. If fitness is maximized at the low-density encroachment edge, then shrub expansion should be “pulled” forward. However, encroaching shrubs have been shown to exhibit positive feedbacks, whereby shrub establishment modifies the environment in ways that facilitate further shrub recruitment and survival. In this case there may be a fitness cost to shrubs at low density causing expansion to be “pushed” from behind the leading edge. We studied the spatial dynamics of creosotebush (Larrea tridentata), which has a history of encroachment into Chihuahuan Desert grasslands over the past century. We used demographic data from observational censuses and seedling transplant experiments to test the strength and direction of density dependence in shrub fitness along a gradient of shrub density at the grass–shrub ecotone. We also used seed-drop experiments and wind data to construct a mechanistic seed-dispersal kernel, then connected demography and dispersal data within a spatial integral projection model (SIPM) to predict the dynamics of shrub expansion. Contrary to expectations based on potential for positive feedbacks, the shrub encroachment wave is “pulled” by maximum fitness at the low-density front. However, the predicted pace of expansion was strikingly slow (ca. 8 cm/year), and this prediction was supported by independent resurveys of the ecotone showing little to no change in the spatial extent of shrub cover over 12 years. Encroachment speed was acutely sensitive to seedling recruitment, suggesting that this population may be primed for pulses of expansion under conditions that are favorable for recruitment. Our integration of observations, experiments, and modeling reveals not only that this ecotone is effectively stalled under current conditions but also why that is so and how that may change as the environment changes.

Ameliorating the impacts of climate change on communities requires understanding the mechanisms of change and applying them to predict future responses. One way to prioritize efforts is to identify biotic multipliers, which are species that are sensitive to climate change and disproportionately alter communities. We first evaluate the mechanisms underlying the occupancy dynamics of marbled salamanders, a key predator in temporary ponds in the eastern United States We use long-term data to evaluate four mechanistic hypotheses proposed to explain occupancy patterns, including autumn flooding, overwintering predation, freezing, and winterkill from oxygen depletion. Results suggest that winterkill and fall flooding best explain marbled salamander occupancy patterns. A field introduction experiment supports the importance of winterkill via hypoxia rather than freezing in determining overwinter survival and rejects dispersal limitation as a mechanism preventing establishment. We build climate-based correlative models that describe salamander occupancy across ponds and years at two latitudinally divergent sites, a southern and middle site, with and without field-collected habitat characteristics. Correlative models with climate and habitat variation described occupancy patterns better than climate-only models for each site, but poorly predicted occupancy patterns at the site not used for model development. We next built hybrid mechanistic metapopulation occupancy models that incorporated flooding and winterkill mechanisms. Although hybrid models did not describe observed site-specific occupancy dynamics better than correlative models, they better predicted the other site's dynamics, revealing a performance trade-off between model types. Under future climate scenarios, models predict an increased occupancy of marbled salamanders, especially at the middle site, and expansion at a northern site beyond the northern range boundary. Evidence for the climate sensitivity of marbled salamanders combined with their disproportionate ecological impacts suggests that they might act as biotic multipliers of climate change in temporary ponds. More generally, we predict that top aquatic vertebrate predators will expand into temperate-boreal lakes as climate change reduces winterkill worldwide. Predaceous species with life histories sensitive to winter temperatures provide good candidates for identifying additional biotic multipliers. Building models that include biological mechanisms for key species such as biotic multipliers could better predict broad changes in communities and design effective conservation actions.