Ecological Monographs最新文献

Specifying, assessing, and selecting among candidate statistical models is fundamental to ecological research. Commonly used approaches to model selection are based on predictive scores and include information criteria such as Akaike's information criterion, and cross validation. Based on data splitting, cross validation is particularly versatile because it can be used even when it is not possible to derive a likelihood (e.g., many forms of machine learning) or count parameters precisely (e.g., mixed-effects models). However, much of the literature on cross validation is technical and spread across statistical journals, making it difficult for ecological analysts to assess and choose among the wide range of options. Here we provide a comprehensive, accessible review that explains important—but often overlooked—technical aspects of cross validation for model selection, such as: bias correction, estimation uncertainty, choice of scores, and selection rules to mitigate overfitting. We synthesize the relevant statistical advances to make recommendations for the choice of cross-validation technique and we present two ecological case studies to illustrate their application. In most instances, we recommend using exact or approximate leave-one-out cross validation to minimize bias, or otherwise k-fold with bias correction if k < 10. To mitigate overfitting when using cross validation, we recommend calibrated selection via our recently introduced modified one-standard-error rule. We advocate for the use of predictive scores in model selection across a range of typical modeling goals, such as exploration, hypothesis testing, and prediction, provided that models are specified in accordance with the stated goal. We also emphasize, as others have done, that inference on parameter estimates is biased if preceded by model selection and instead requires a carefully specified single model or further technical adjustments.

Climate warming is considered to be among the most serious of anthropogenic stresses to the environment, because it not only has direct effects on biodiversity, but it also exacerbates the harmful effects of other human-mediated threats. The associated consequences are potentially severe, particularly in terms of threats to species preservation, as well as in the preservation of an array of ecosystem services provided by biodiversity. Among the most affected groups of animals are insects—central components of many ecosystems—for which climate change has pervasive effects from individuals to communities. In this contribution to the scientists' warning series, we summarize the effect of the gradual global surface temperature increase on insects, in terms of physiology, behavior, phenology, distribution, and species interactions, as well as the effect of increased frequency and duration of extreme events such as hot and cold spells, fires, droughts, and floods on these parameters. We warn that, if no action is taken to better understand and reduce the action of climate change on insects, we will drastically reduce our ability to build a sustainable future based on healthy, functional ecosystems. We discuss perspectives on relevant ways to conserve insects in the face of climate change, and we offer several key recommendations on management approaches that can be adopted, on policies that should be pursued, and on the involvement of the general public in the protection effort.

Ecosystem stability has intrigued ecologists for decades, and the realization that the global climate was changing has sharpened and focused this interest. One possible early warning signal of decreasing stability is increasing variability in ecosystems over time with increasing climate variability. Determining climate change effects on community stability, however, requires long-term studies of structure and underlying dynamics, including bottom-up and top-down effects in natural ecosystems. Although relevant datasets were rare in the early years of community ecology, such information has increased in recent decades. We investigated spatiotemporal changes in mean and variability of ecological subsidies (nutrients, phytoplankton, prey colonization), performance metrics of a dominant space occupier (mussels) and its primary predator (sea stars), and sea star predation rates on mussels in relation to climatic oscillations, temperature, and disease on rocky shores. The research involved annually repeated multiyear (~1999–2018), multisite (13 sites nested within five regions along ~260 km of the Oregon coast) observations, measurements, and experiments. We analyzed associations between environmental variables and ecological performance of key elements of the sea star-mussel-dominated mid intertidal system. We found that upwelling declined in some regions, but became more variable across all study regions. Air and water temperatures oscillated, but their mean and variation increased through time, with peak values coinciding with the 2014–2016 combined El Niño and Marine Heat Wave. Ecological subsidies generally declined during the study period but increased in variability. Excepting growth rate, mussel (Mytilus californianus) performance (condition index, reproductive output) generally decreased and became more variable. Primarily due to a sea star wasting epidemic, reproductive output of the top predator Pisaster ochraceus decreased and became more variable, and predation rate on mussels decreased. Analyses indicated that the primary drivers of these changes were temperature-related environmental factors. As declining means and increasing variability of ecological performances can typify destabilizing ecosystems, and environmental trends are toward ever more stressful conditions, the outlook for this iconic ecosystem is discouraging. Immediate and rapid action to mitigate and ultimately reverse climate change likely is the only option available to prevent an irreversible shift in the future of this, and most other ecosystems.

In the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. The slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here we compare taxonomic and functional community compositional responses to a long-term (17-year) warming experiment in Svalbard, Norway, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by 12 size- and leaf economics-related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and mass increased and leaf δ¹⁵N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting the functional resilience of those communities, dominated by long-lived plants, due to either phenotypic plasticity or genotypic variation, which most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO₂ fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long-term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.

Ecologists are often interested in answering causal questions from observational data but generally lack the training to appropriately infer causation. When applying statistical analysis (e.g., generalized linear model) on observational data, common statistical adjustments can often lead to biased estimates between variables of interest due to processes such as confounding, overcontrol, and collider bias. To overcome these limitations, we present an overview of structural causal modeling (SCM), an emerging causal inference framework that can be used to determine cause-and-effect relationships from observational data. The SCM framework uses directed acyclic graphs (DAGs) to visualize researchers' assumptions about the causal structure of a system or process under study. Following this, a DAG-based graphical rule known as the backdoor criterion can be applied to determine statistical adjustments (or lack thereof) required to determine causal relationships from observational data. In the presence of unobserved confounding variables, an additional rule called the frontdoor criterion can be employed to determine causal effects. Here, we use simulated ecological examples to review how the backdoor and frontdoor criteria can return accurate causal estimates between variables of interest, as well as how biases can arise when these criteria are not used. We further provide an overview of studies that have applied the SCM framework in ecology. SCM, along with its application of DAGs, has been widely used in other disciplines to make valid causal inferences from observational data. Their use in ecology holds tremendous potential for quantifying causal relationships and investigating a range of ecological questions without randomized experiments.

The alteration of the timing of biological events is one of the best documented effects of climate change, with overwhelming evidence across taxa. Many studies have investigated the phenology of consumers, especially birds. However, most of these studies have focused on specific phenophases, whereas a global analysis of avian phenological trends during recent climate change across different phases of the circannual cycle is still lacking. Here, we performed a comprehensive meta-analytic synthesis of the phenological responses (temporal shifts in days year⁻¹) of birds across different phenophases (prebreeding migration, breeding, and postbreeding migration) by summarizing more than 5500 time series from 684 species from five continents during 1811–2018. Our results confirm that avian taxa have advanced prebreeding migration and breeding by ~2–3 days per decade, whereas no significant temporal changes in the timing of postbreeding migration were documented. Advancement in the timing of prebreeding migration and breeding strongly depended on migratory behavior, with the advance being the weakest for long-distance migrants and the strongest for resident species. Diet generalists and primary consumers tended to advance prebreeding migration timing more than species with different dietary specializations. Increasing body size resulted in a larger advancement in the onset (but not in the mean date) of prebreeding migration and breeding, whereas phenological advances were larger in the northern than in the southern hemisphere. Our synthesis, covering most of the world, highlighted previously unappreciated patterns in avian phenological shifts over time, suggesting that specific life-history or ecological traits may drive different responses to climate change.

Insects provide key pollination services in most terrestrial biomes, but this service depends on a multistep interaction between insect and plant. An insect needs to visit a flower, receive pollen from the anthers, move to another conspecific flower, and finally deposit the pollen on a receptive stigma. Each of these steps may be affected by climate change, and focusing on only one of them (e.g., flower visitation) may miss important signals of change in service provision. In this study, we combine data on visitation, pollen transport, and single-visit pollen deposition to estimate functional outcomes in the high Arctic plant-pollinator network of Zackenberg, Northeast Greenland, a model system for global warming–associated impacts in pollination services. Over two decades of rapid climate warming, we sampled the network repeatedly: in 1996, 1997, 2010, 2011, and 2016. Although the flowering plant and insect communities and their interactions varied substantially between years, as expected based on highly variable Arctic weather, there was no detectable directional change in either the structure of flower-visitor networks or estimated pollen deposition. For flower-visitor networks compiled over a single week, species phenologies caused major within-year variation in network structure despite consistency across years. Weekly networks for the middle of the flowering season emerged as especially important because most pollination service can be expected to be provided by these large, highly nested networks. Our findings suggest that pollination ecosystem service in the high Arctic is remarkably resilient. This resilience may reflect the plasticity of Arctic biota as an adaptation to extreme and unpredictable weather. However, most pollination service was contributed by relatively few fly taxa (Diptera: Spilogona sanctipauli and Drymeia segnis [Muscidae] and species of Rhamphomyia [Empididae]). If these key pollinators are negatively affected by climate change, network structure and the pollination service that depends on it would be seriously compromised.

Global climate change will probably exacerbate crop losses from insect pests, reducing agricultural production, and threatening food security. To predict where crop losses will occur, scientists have mainly used correlative models of species' distributions, but such models are unreliable when extrapolated to future environments. To minimize extrapolation, we developed mechanistic and hybrid models that explicitly capture range-limiting processes, and we explored how incorporating mechanisms altered the projected impacts of climate change for an agricultural pest, the South American locust (Schistocerca cancellata). Because locusts are generalist herbivores surrounded by food, their population growth may be limited by thermal effects on digestion more than food availability. To incorporate this mechanism into a distribution model, we measured the thermal effects on the consumption and defecation of field-captured locusts and used these data to model energy gain in current and future climates. We then created hybrid models by using outputs of the mechanistic model as predictor variables in correlative models, estimating the potential distribution of gregarious outbreaking locusts based on multiple predictor sets, modeling algorithms, and climate scenarios. Based on the mechanistic model, locusts can assimilate relatively high amounts of energy throughout temperate and tropical South America; however, correlative and hybrid modeling revealed that most tropical areas are unsuitable for locusts. When estimating current distributions, the top-ranked model was always the one fit with mechanistic predictors (i.e., the hybrid model). When projected to future climates, top-ranked hybrid models projected range expansions that were 23%–30% points smaller than those projected by correlative models. Therefore, a combination of the correlative and mechanistic approaches bracketed the potential outcomes of climate change and enhanced confidence where model projections agreed. Because all models projected a poleward range expansion under climate change, agriculturists should consider enhanced monitoring and the management of locusts near the southern margin of the range.

Long-distance insect migration is poorly understood despite its tremendous ecological and economic importance. As a group, Nearctic hover flies (Diptera: Syrphidae: Syrphinae), which are crucial pollinators as adults and biological control agents as larvae, are almost entirely unrecognized as migratory despite examples of highly migratory behavior among several Palearctic species. Here, we examined evidence and mechanisms of migration for four hover fly species (Allograpta obliqua, Eupeodes americanus, Syrphus rectus, and Syrphus ribesii) common throughout eastern North America using stable hydrogen isotope (δ²H) measurements of chitinous tissue, morphological assessments, abundance estimations, and cold-tolerance assays. Although further studies are needed, nonlocal isotopic values obtained from hover fly specimens collected in central Illinois support the existence of long-distance fall migratory behavior in Eu. americanus, and to a lesser extent S. ribesii and S. rectus. Elevated abundance of Eu. americanus during the expected autumn migratory period further supports the existence of such behavior. Moreover, high phenotypic plasticity of morphology associated with dispersal coupled with significant differences between local and nonlocal specimens suggest that Eu. americanus exhibits a unique suite of morphological traits that decrease costs associated with long-distance flight. Finally, compared with the ostensibly nonmigratory A. obliqua, Eu. americanus was less cold tolerant, a factor that may be associated with migratory behavior. Collectively, our findings imply that fall migration occurs in Nearctic hover flies, but we consider the methodological limitations of our study in addition to potential ecological and economic consequences of these novel findings.