Ecology最新文献

Changes in phenology are a conspicuous fingerprint of climate change, leading to fears that phenological mismatches among interacting species will be a leading cause of population declines and extinction. We used quantile regression to analyze museum collection data and estimate changes in the phenological overlap of Baltimore checkerspot butterflies and 12 common nectar plant species over several decades in two geographic regions. We combined these museum data with field estimates of each species' flower density and nectar sugar production to estimate changes in resource availability caused by shifts in phenological overlap. Phenological overlap (measured as the proportion of plant flowering during the flight period of an average butterfly) decreased through time, primarily because the flowering period of nectar plants was longer, but the flight period of butterflies was shorter in recent years. Our study was also motivated by the hypothesis that phenological mismatches may be more severe in the southern region due to a midsummer dearth in floral resources, but this hypothesis was not supported by our data. Although phenological overlap was somewhat smaller in the southern region, changes in overlap through time were similar in both regions. When phenological overlap was weighted by nectar sugar production of different species, the overlap increased in the southern region but decreased in the northern region (the opposite of our prediction). Overall, nectar resources were much more abundant at study sites in our northern region than in our southern region, possibly due to differences in land management. Our study demonstrates the complexities of phenological mismatch of interacting species and highlights that phenological changes may have small impacts on population viability.

Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two-scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time-varying proportionality parameters. This two-step approach ensures that the long-term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two-scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200-fold increase over 30 years for some subpopulations. The two-scale model predicted very sensible growths, never larger than a threefold increase over the 30-year time horizon, which is more in accordance with this species' life history. This two-scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full-dispersal models. Because the only data required are simple counts, this model could be useful to many large-scale wildlife monitoring programs.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species–habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, “spatial survey” denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture–recapture or telemetry. Step selection functions (SSFs—the canonical framework for telemetry) and habitat selection functions (HSFs—the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

Predators regulate communities through top-down control in many ecosystems. Because most studies of top-down control last less than a year and focus on only a subset of the community, they may miss predator effects that manifest at longer timescales or across whole food webs. In southeastern US salt marshes, short-term and small-scale experiments indicate that nektonic predators (e.g., blue crab, fish, terrapins) facilitate the foundational grass, Spartina alterniflora, by consuming herbivorous snails and crabs. To test both how nekton affect marsh processes when the entire animal community is present, and how prior results scale over time, we conducted a 3-year nekton exclusion experiment in a Georgia salt marsh using replicated 19.6 m² plots. Our nekton exclusions increased densities of plant-grazing snails and juvenile deposit-feeding fiddler crab and, in Year 2, reduced predation on tethered juvenile snails, indicating that nektonic predators control these key macroinvertebrates. However, in Year 3, densities of mesopredatory benthic mud crabs increased threefold in nekton exclusions, erasing the tethered snails' predation refuge. Nekton exclusion had no effect on Spartina biomass, likely because the observed mesopredator release suppressed grazing snail densities and elevated densities of fiddler crabs, whose burrowing alleviates soil stresses. Structural equation modeling supported the hypotheses that nektonic predators and mesopredators control invertebrate communities, with nektonic predators having stronger total effects on Spartina than mud crabs by controlling densities of species that both suppress (grazers) and facilitate (fiddler crabs) plant growth. These findings highlight that salt marshes can be resilient to multiyear reductions in nektonic predators if mesopredators are present and that multiple pathways of trophic control manifest in different ways over time to mediate community dynamics. These results highlight that larger scale and longer-term experiments can illuminate community dynamics not previously understood, even in well-studied ecosystems such as salt marshes.

The replacement of grasses by shrubs or bare ground (xerification) is a primary form of landscape change in drylands globally with consequences for ecosystem services. The potential for wild herbivores to trigger or reinforce shrubland states may be underappreciated, however, and comparative analyses across herbivore taxa are sparse. We sought to clarify the relative effects of domestic cattle, native rodents, native lagomorphs, and exotic African oryx (Oryx gazella) on a Chihuahuan Desert grassland undergoing shrub encroachment. We then asked whether drought periods, wet season precipitation, or interspecific grass–shrub competition modified herbivore effects to alter plant cover, species diversity, or community composition. We established a long-term experiment with hierarchical herbivore exclosure treatments and surveyed plant foliar cover over 25 years. Cover of honey mesquite (Prosopis glandulosa) proliferated, responding primarily to climate, and was unaffected by herbivore treatments. Surprisingly, cattle and African oryx exclusion had only marginal effects on perennial grass cover at their current densities. Native lagomorphs interacted with climate to limit perennial grass cover during wet periods. Native rodents strongly decreased plant diversity, decreased evenness, and altered community composition. Overall, we found no evidence of mammalian herbivores facilitating or inhibiting shrub encroachment, but native small mammals interacting with climate drove dynamics of herbaceous plant communities. Ongoing monitoring will determine whether increased perennial grass cover from exclusion of native lagomorphs and rodents slows the transition to a dense shrubland.

Predator–prey interactions are crucial components of populations and communities. Their dynamics depend on the covariation of traits of the interacting organisms, and there is increasing evidence that intraspecific trade-off relationships between defense and competitive traits are important drivers of trophic interactions. However, quantifying the relevant traits forming defense–competitiveness trade-offs and how these traits determine prey and predator fitness remains a major challenge. Here, we conducted feeding and growth experiments to assess multiple traits related to defense and competitiveness in six different strains of the green alga Chlamydomonas reinhardtii exposed to predation by the rotifer Brachionus calyciflorus. We found large differences in defense and competitive traits among prey strains and negative relationships between these traits for multiple trait combinations. Because we compared trait differences among strains whose ancestors evolved previously in controlled environments where selection favored either defense or competitiveness, these negative correlations suggest the presence of a trade-off between defense and competitiveness. These differences in traits and trade-offs translated into differences in prey and predator fitness, demonstrating the importance of intraspecific trade-offs in predicting the outcome of predator–prey interactions.

Human-caused global change produces biotic and abiotic conditions that increase the uncertainty and risk of failure of restoration efforts. A focus of managing for resiliency, that is, the ability of the system to respond to disturbance, has the potential to reduce this uncertainty and risk. However, identifying what drives resiliency might depend on how one measures it. An example of a system where identifying how the drivers of different aspects of resiliency can inform restoration under climate change is the northern coast of California, where kelp experienced a decline in coverage of over 95% due to the combination of an intense marine heat wave and the functional extinction of the primary predator of the kelp-grazing purple sea urchin, the sunflower sea star. Although restoration efforts focused on urchin removal and kelp reintroduction in this system are ongoing, the question of how to increase the resiliency of this system to future marine heat waves remains open. In this paper, we introduce a dynamical model that describes a tritrophic food chain of kelp, purple urchins, and a purple urchin predator such as the sunflower sea star. We run a global sensitivity analysis of three different resiliency metrics (recovery likelihood, recovery rate, and resistance to disturbance) of the kelp forest to identify their ecological drivers. We find that each metric depends the most on a unique set of drivers: Recovery likelihood depends the most on live and drift kelp production, recovery rate depends the most on urchin production and feedbacks that determine urchin grazing on live kelp, and resistance depends the most on feedbacks that determine predator consumption of urchins. Therefore, an understanding of the potential role of predator reintroduction or recovery in kelp systems relies on a comprehensive approach to measuring resiliency.

Deciphering the linkage between ecological stoichiometry and ecosystem functioning under anthropogenic nitrogen (N) deposition is critical for understanding the impact of afforestation on terrestrial carbon (C) sequestration. However, the specific changes in above- versus belowground stoichiometric asymmetry with stand age in response to long-term N addition remain poorly understood. In this study, we investigated changes in stoichiometry following a decadal addition of three levels of N (control, no N addition; low N addition, 20 kg N ha⁻¹ year⁻¹; high N addition, 50 kg N ha⁻¹ year⁻¹) in young, intermediate, and mature stands in three temperate larch plantations (Larix principis-rupprechtii) in North China. We found that low N addition had no impact on both above- (leaf and litter) and belowground (soil and microbe) stoichiometry. In contrast, high N addition resulted in significant asymmetry in above- versus belowground stoichiometry, which then diminished during stand development. Following 10 years of N inputs, the young and intermediate plantations transitioned from a state of relative N limitation to co-limitation by both N and phosphorus (P), whereas the mature plantation continued to experience relative N limitation. Conversely, soil microorganisms exhibited relative P limitation in all three plantations. Broader niche differentiation (N limitation for trees, but P limitation for microorganisms) under long-term N input may have been responsible for the faster attainment of stoichiometric homeostasis in mature plantations than in young plantations. Our findings provide stoichiometric-based insight into the operating mechanisms of large C sinks in young forests, particularly above- versus belowground C stock asymmetry, and highlight the need to consider the role of flexible stoichiometry when forecasting future forest C sinks.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species–environment associations. In this study, we evaluated how two components of temporal environmental variation—variability and predictability—impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations (“nodes”) distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.