Assessing the sensitivity and resistance of communities in a changing environment.

We propose that the ecological resilience of communities to permanent changes of the environment can be based on how variation in the overall abundance of individuals affects the number of species. Community sensitivity is defined as the ratio between the rate of change in the log expected number of species and the rate of change in the log expected number of individuals in the community. High community sensitivity means that small changes in the total abundance strongly impact the number of species. Community resistance is the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. A small value of community resistance means that the community can only endure a small reduction in abundance before it is expected to lose one species. Based on long-term studies of four bird communities in European deciduous forests at different latitudes large differences were found in the resilience to environmental perturbations. Estimating the variance components of the species abundance distribution revealed how different processes contributed to the community sensitivity and resistance. Species heterogeneity in the population dynamics was the largest component, but its proportion varied among communities. Species-specific response to environmental fluctuations was the second major component of the variation in abundance. Estimates of community sensitivity and resistance based on data only from a single year were in general larger than those based on estimates from longer time series. Thus, our approach can provide rapid and conservative assessment of the resilience of communities to environmental changes also including only short-term data. This study shows that a general ecological mechanism, caused by increased strength of density dependence due to reduction in resource availability, can provide an intuitive measure of community resilience to environmental variation. Our analyses also illustrate the importance of including specific assumptions about how different processes affect community dynamics. For example, if stochastic fluctuations in the environment affect all species in a similar way, the sensitivity and resistance of the community to environmental changes will be different from communities in which all species show independent responses.

Analyzing dynamic species abundance distributions using generalized linear mixed models.

Understanding the mechanisms of ecological community dynamics and how they could be affected by environmental changes is important. Population dynamic models have well known ecological parameters that describe key characteristics of species such as the effect of environmental noise and demographic variance on the dynamics, the long-term growth rate, and strength of density regulation. These parameters are also central for detecting and understanding changes in communities of species; however, incorporating such vital parameters into models of community dynamics is challenging. In this paper, we demonstrate how generalized linear mixed models specified as intercept-only models with different random effects can be used to fit dynamic species abundance distributions. Each random effect has an ecologically meaningful interpretation either describing general and species-specific responses to environmental stochasticity in time or space, or variation in growth rate and carrying capacity among species. We use simulations to show that the accuracy of the estimation depends on the strength of density regulation in discrete population dynamics. The estimation of different covariance and population dynamic parameters, with corresponding statistical uncertainties, is demonstrated for case studies of fish and bat communities. We find that species heterogeneity is the main factor of spatial and temporal community similarity for both case studies.

Contrasting patterns of density-dependent selection at different life stages can create more than one fast-slow axis of life-history variation.

There has been much recent research interest in the existence of a major axis of life-history variation along a fast-slow continuum within almost all major taxonomic groups. Eco-evolutionary models of density-dependent selection provide a general explanation for such observations of interspecific variation in the "pace of life." One issue, however, is that some large-bodied long-lived "slow" species (e.g., trees and large fish) often show an explosive "fast" type of reproduction with many small offspring, and species with "fast" adult life stages can have comparatively "slow" offspring life stages (e.g., mayflies). We attempt to explain such life-history evolution using the same eco-evolutionary modeling approach but with two life stages, separating adult reproductive strategies from offspring survival strategies. When the population dynamics in the two life stages are closely linked and affect each other, density-dependent selection occurs in parallel on both reproduction and survival, producing the usual one-dimensional fast-slow continuum (e.g., houseflies to blue whales). However, strong density dependence at either the adult reproduction or offspring survival life stage creates quasi-independent population dynamics, allowing fast-type reproduction alongside slow-type survival (e.g., trees and large fish), or the perhaps rarer slow-type reproduction alongside fast-type survival (e.g., mayflies-short-lived adults producing few long-lived offspring). Therefore, most types of species life histories in nature can potentially be explained via the eco-evolutionary consequences of density-dependent selection given the possible separation of demographic effects at different life stages.

Characteristics of temporal changes in communities where dynamics differ between species.

Communities with different phenotypic variation among species can have identical species abundance distributions, although their temporal dynamics may be very different. By using stochastic species abundance models, both the lognormal and beta prime abundance distributions can be obtained with either homogeneous or heterogeneous dynamics among species. Assuming that anthropogenic activity disturbs the communities such that species' carrying capacities are decreasing deterministically, the structure of the communities are studied using simulations. In order to construct homogeneous communities with reasonable variation in abundance, the parameter values describing the dynamics of the species can be unrealistic in terms of long return times to equilibrium. Species in heterogeneous communities can have stronger density regulation, while maintaining the same variation in abundance, by assuming heterogeneity in one of the dynamical parameters. The heterogeneity generates variation in carrying capacity among species, while reducing the temporal stochasticity. If carrying capacity decreases, changes in community structure occur at a much slower rate for the homogeneous compared to the heterogeneous communities. Even over short time periods, the difference in response to deterministic changes in carrying capacity between homogeneous and heterogeneous community models can be substantial, making the heterogeneous model a recommended starting point for community analysis.

Changing environments causing time delays in population dynamics.

We use a linear diffusion process to approximate a stochastic density regulated population model where parameters can change through time. Contrary to stationary models, there is a difference between the expected value and the carrying capacity of a population at any given time. This time delay can be considerable and depends on the vital rates of the population and the magnitude of the change. We emphasize the importance of acknowledging this difference when assessing viability of populations. As an illustration, we consider the population of Norwegian spring spawning herring and its collapse in the 1960s. Based on our analysis, the stock was already at a critical level a decade before the collapse was observed.