Recent Shifts in the Occurrence, Cause, and Magnitude of Animal Mass Mortality Events

Mass mortality events (MMEs) are rapidly occurring catastrophic demographic events that punctuate background mortality levels. Individual MMEs are staggering in their observed magnitude: re- moving more than 90% of a population, resulting in the death of more than a billion individuals, or producing 700 million tons of dead biomass in a single event. Despite extensive documentation of individual MMEs, we have no understanding of the major features characterizing the occurrence and magnitude of MMEs, their causes, or trends through time. Thus, no framework exists for contextualizing MMEs in the wake of ongoing global and regional perturbations to natural systems. Here we present an analysis of 727 published MMEs from across the globe, affecting 2,407 animal populations. We show that the magnitude of MMEs has been intensifying for birds, fishes, and marine invertebrates; invariant for mammals; and decreasing for reptiles and amphibians. These shifts in magnitude proved robust when we accounted for an increase in the occurrence of MMEs since 1940. However, it remains unclear whether the increase in the occurrence of MMEs represents a true pattern or simply a perceived increase. Regardless, the increase in MMEs appears to be associated with a rise in disease emergence, biotoxicity, and events produced by multiple interacting stressors, yet temporal trends in MME causes varied among taxa and may be associated with increased de- tectability. In addition, MMEs with the largest magnitudes were those that resulted from multiple stressors, starvation, and disease. These results advance our understanding of rare demographic processes and their relationship to global and regional perturba- tions to natural systems

The under-ice microbiome of seasonally frozen lakes

Compared to the well-studied open water of the “growing” season, under-ice conditions in lakes are characterized by low and rather constant temperature, slow water movements, limited light availability, and reduced exchange with the surrounding landscape. These conditions interact with ice-cover duration to shape microbial processes in temperate lakes and ultimately influence the phenology of community and ecosystem processes. We review the current knowledge on microorganisms in seasonally frozen lakes. Specifically, we highlight how under-ice conditions alter lake physics and the ways that this can affect the distribution and metabolism of auto- and heterotrophic microorganisms. We identify functional traits that we hypothesize are important for understanding under-ice dynamics and discuss how these traits influence species interactions. As ice coverage duration has already been seen to reduce as air temperatures have warmed, the dynamics of the under-ice microbiome are important for understanding and predicting the dynamics and functioning of seasonally frozen lakes in the near future

Opportunities for behavioral rescue under rapid environmental change

Laboratory measurements of physiological and demographic tolerances are important in understanding the impact of climate change on species diversity; however, it has been recognized that forecasts based solely on these laboratory estimates overestimate risk by omitting the capacity for species to utilize microclimatic variation via behavioral adjustments in activity patterns or habitat choice. The complex, and often context‐dependent nature, of microclimate utilization has been an impediment to the advancement of general predictive models. Here, we overcome this impediment and estimate the potential impact of warming on the fitness of ectotherms using a benefit/cost trade‐off derived from the simple and broadly documented thermal performance curve and a generalized cost function. Our framework reveals that, for certain environments, the cost of behavioral thermoregulation can be reduced as warming occurs, enabling behavioral buffering (e.g., the capacity for behavior to ameliorate detrimental impacts) and “behavioral rescue” from extinction in extreme cases. By applying our framework to operative temperature and physiological data collected at an extremely fine spatial scale in an African lizard, we show that new behavioral opportunities may emerge. Finally, we explore large‐scale geographic differences in the impact of behavior on climate‐impact projections using a global dataset of 38 insect species. These multiple lines of inference indicate that understanding the existing relationship between thermal characteristics (e.g., spatial configuration, spatial heterogeneity, and modal temperature) is essential for improving estimates of extinction risk

Environmental variability in aquatic ecosystems: Avenues for future multifactorial experiments

The relevance of considering environmental variability for understanding and predicting biological responses to environmental changes has resulted in a recent surge in variability-focused ecological research. However, integration of findings that emerge across studies and identification of remaining knowledge gaps in aquatic ecosystems remain critical. Here, we address these aspects by: (1) summarizing relevant terms of variability research including the components (characteristics) of variability and key interactions when considering multiple environmental factors; (2) identifying conceptual frameworks for understanding the consequences of environmental variability in single and multifactorial scenarios; (3) highlighting challenges for bridging theoretical and experimental studies involving transitioning from simple to more complex scenarios; (4) proposing improved approaches to overcome current mismatches between theoretical predictions and experimental observations; and (5) providing a guide for designing integrated experiments across multiple scales, degrees of control, and complexity in light of their specific strengths and limitations

Appendix A. A figure showing mean density of total Daphnia following the invasion of Daphnia lumholtzi or Daphnia pulex into mesocosms with and without added cyanobacteria.

A figure showing mean density of total Daphnia following the invasion of Daphnia lumholtzi or Daphnia pulex into mesocosms with and without added cyanobacteria

Appendix A. Sensitivity analysis of mathematical model.

Sensitivity analysis of mathematical model

Appendix B. Temperature dependencies of the vital rates of Lepomis sunfish, Daphnia pulex, and Daphnia lumholtzi.

Temperature dependencies of the vital rates of Lepomis sunfish, Daphnia pulex, and Daphnia lumholtzi

Appendix C. Two-dimensional representations of the mathematical model.

Two-dimensional representations of the mathematical model

Data from: Uncertainty in geographic estimates of performance and fitness

1. Thermal performance curves (TPCs) have become key tools for predicting geographic distributions of performance by ectotherms. Such TPC-based predictions, however, may be sensitive to errors arising from diverse sources. 2. We analyzed potential errors that arise from common choices faced by biologists integrating TPCs with climate data by constructing case studies focusing on experimental sets of TPCs and simulating geographic patterns of mean performance. We first analyzed differences in geographic patterns of performance derived from two pairs of commonly used TPCs. Mean performance differed most (up to 30%) in regions with relatively constant mean temperatures similar to those at which the TPCs diverged the most. 3. We also analyzed the effects of thermal history by comparing geographic estimates derived from (1) a broad TPC based on short-term measurements of insect larvae (Manduca sexta) with a history of exposure to thermal variation versus (2) a narrow TPC based on long-term measurements of larvae held at constant temperatures. Estimated mean performance diverged by up to 40%, and differences were magnified in simulated future climates. 4. Finally, to quantify geographic error arising from statistical error in fitted TPCs, we propose and illustrate a bootstrapping technique for establishing 95% prediction intervals on mean performance at each location (pixel). 5. Collectively, our analyses indicate that error arising from several underappreciated sources can significantly affect the mean performance values derived from TPCs, and we suggest that the magnitudes of these errors should be estimated routinely in future studies