Ecological Monographs最新文献

Species turnover with elevation is a widespread phenomenon and provides valuable information on why and how ecological communities might reorganize as the climate warms. It is commonly assumed that species interactions are more likely to set warm range limits, while physiological tolerances determine cold range limits. However, most studies are from temperate systems and rely on correlations between thermal physiological traits and range limits; little is known about how physiological traits and biotic interactions change simultaneously along continuous thermal gradients. We used a combination of correlational and experimental approaches to investigate communities of Drosophila flies in rainforests of the Australian Wet Tropics, where there is substantial species turnover with elevation. Our experiments quantified individual-level and population-level responses to temperature, as well as the impact of interspecific competition under different temperature regimes. Species' distributions were better explained by their performance at extreme temperatures than by their thermal optima. Upper thermal limits varied less among species than lower thermal limits. Nonetheless, these small differences were associated with differences in the centered elevation of distribution. Low-elevation species were not those with the lowest tolerance to cold, suggesting that cold temperatures were not limiting their abundance at high elevations. Instead, under upland temperature regimes, abundances of these low-elevation species were reduced by competition with a high-elevation species, in both short- and long-term competition experiments. Our results demonstrate that high-elevation species are confined to their current ranges by high temperatures at lower elevations, indicating that their ranges will be highly sensitive to future warming. Counter to expectation, species interactions strongly influenced community composition at cooler, high-elevation sites. Together, these results raise the possibility that tropical communities differ from better-studied temperate communities in terms of the relative importance of biotic interactions and abiotic factors in shaping community composition and how the impact of these factors will change as temperatures increase.

The contribution of genetic adaptation and plasticity to intraspecific phenotypic variability remains insufficiently studied in long-lived plants, as well as the relevance of neutral versus adaptive processes determining such divergence. We examined the importance of phylogeographic structure and climate in modulating genetic and plastic changes and their interdependence in fitness-related traits of a widespread Mediterranean conifer (Pinus pinaster). Four marker-based, previously defined neutral classifications along with two ad hoc climate-based categorizations of 123 range-wide populations were analyzed for their capacity to summarize genetic and plastic effects of height growth and survival (age 20) in 15 common gardens. The plasticity of tree height and differential survival were interpreted through mixed modeling accounting for heteroscedasticity in the genotype-by-environment dataset. The analysis revealed a slight superiority of phylogeographic classifications over climate categorizations on the explanation of genetic and plastic effects, which suggests that neutral processes can be at least as important as isolation by climate as a driving factor of evolutionary divergence in a prevalent pine. The best phylogeographic classification involved eight geographically discrete genetic groups, which explained 92% (height) and 52% (survival) of phenotypic variability, including between-group mean differentiation and differential expression across trials. For height growth, there was high predictability of plastic group responses described by different reaction norm slopes, which were unrelated to between-group mean differentiation. The latter differences (amounting to ca. 40% among groups) dominated intraspecific performance across trials. Local adaptation was evident for genetic groups tested in their native environments in terms of tree height and, especially, survival. This finding was supported by Q_ST > F_ST estimates. Additionally, our range-wide evaluation did not support a general adaptive syndrome by which less reactive groups to ameliorated conditions would be associated with high survival and low growth. In fact, a lack of relationship between mean group differentiation, indicative of genetic adaptation, and predictable group plasticity for height growth suggests different evolutionary trajectories of these mechanisms of phenotypic divergence. Altogether, the existence of predictable adaptive-trait phenotypic variation for the species, involving both genetic differentiation and plastic effects, should facilitate integrating genomics and environment into decision-making tools to assist forests in coping with climate change.

Genomic data are increasingly being integrated into macroecological forecasting, offering an evolutionary perspective that has been largely missing from global change biogeography. Genomic offset, which quantifies the disruption of genotype–environment associations under environmental change, allows for the incorporation of intraspecific climate-associated genomic differentiation into forecasts of habitat suitability. Gradient Forest (GF) is a commonly used approach to estimate genomic offset; however, major hurdles in the application of GF-derived genomic offsets are (1) an inability to interpret their absolute magnitude in an ecologically meaningful way and (2) uncertainty in how their implications compare with those of species-level approaches like Ecological Niche Models (ENMs). Here, we assess the climate change vulnerability of red spruce (Picea rubens), a cool-temperate tree species endemic to eastern North America, using both ENMs and GF modeling of genomic variation along climatic gradients. To gain better insights into climate change risks, we derive and apply two new threshold-based genomic offset metrics—Donor and Recipient Importance—that quantify the transferability of propagules between donor populations and recipient localities while minimizing disruption of genotype–environment associations. We also propose and test a method for scaling genomic offsets relative to contemporary genomic variation across the landscape. In three common gardens, we found a significant negative relationship between (scaled) genomic offsets and red spruce growth and higher explanatory power for scaled offsets than climate transfer distances. However, the garden results also revealed the potential effects of spatial extrapolation and neutral genomic differentiation that can compromise the degree to which genomic offsets represent maladaptation and highlight the necessity of using common garden data to evaluate offset-based predictions. ENMs and our novel genomic offset metrics forecasted drastic northward range shifts in suitable habitats. Combining inferences from our offset-based metrics, we show that a northward shift mainly will be required for populations in the central and northern parts of red spruce's current range, whereas southern populations might persist in situ due to climate-associated variation with less offset under future climate. These new genomic offset metrics thus yield refined, region-specific prognoses for local persistence and show how management could be improved by considering assisted migration.

The dynamic interactions between predators and their prey have two fundamental processes: numerical and functional responses. Numerical response is defined as predator growth rate as a function of prey density or both prey and predator densities [dP/dt = f(N, P)]. Functional response is defined as the kill rate by an individual predator being a function of prey density or prey and predator densities combined. Although there are relatively many studies on the functional response in mammalian predators, the numerical response remains poorly documented. We studied the numerical response of Eurasian lynx (Lynx lynx) to various densities of its primary prey species, roe deer (Capreolus capreolus), and to itself (lynx). We exploited an unusual natural situation, spanning three decades where lynx, after a period of absence in central and southern Sweden, during which roe deer populations had grown to high densities, subsequently recolonized region after region, from north to south. We divided the study area into seven regions, with increasing productivity from north to south. We found strong effects of both roe deer density and lynx density on lynx numerical response. Thus, both resources and intraspecific competition for these resources are important to understanding the lynx population dynamic. We built a series of deterministic lynx–roe deer models, and applied them to the seven regions. We found a very good fit between these Lotka–Volterra type models and the data. The deterministic models produced almost cyclic dynamics or dampened cycles in five of the seven regions. Thus, we documented population cycles in this large predator–large herbivore system, which is rarely done. The amplitudes in the dampened cycles decreased toward the south. Thus, the dynamics between lynx and roe deer became more stable with increasing carrying capacity for roe deer, which is related to higher productivity in the environment. This increased stability could be explained by variation in predation risk, where human presence can act as prey refugia, and by a more diverse prey guild that will weaken the direct interaction between lynx and roe deer.

Predicting the outcome of interactions between species is central to our current understanding of diversity maintenance. However, we have limited information about the robustness of many model-based predictions of species coexistence. This limitation is partly because several sources of uncertainty are often ignored when making predictions. Here, we introduce a framework to simultaneously explore how different mathematical models, different environmental contexts, and parameter uncertainty impact the probability of predicting species coexistence. Using a set of pairwise competition experiments on annual plants, we provide direct evidence that subtle differences between models lead to contrasting predictions of both coexistence and competitive exclusion. We also show that the effects of environmental context dependency and parameter uncertainty on predictions of species coexistence are not independent of the model used to describe population dynamics. Our work suggests that predictions of species coexistence and extrapolations thereof may be particularly vulnerable to these underappreciated founts of uncertainty.

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.