Variability in primary productivity determines metapopulation dynamics

Spatiotemporal connectivity dynamics in spatially structured populations

Connectivity is a fundamental concept linking dispersal to the emergent dynamics and persistence of spatially structured populations. Functional measures of connectivity typically seek to integrate aspects of landscape structure and animal movement to describe ecologically meaningful connectedness at the landscape and population scale. Despite this focus on function, traditional measures of landscape connectivity assume it is a static property of the landscape, hence abstracting out the underlying spatiotemporal population dynamics. Connectivity is, arguably, a dynamic property of landscapes, and is inherently related to the spatial distribution of individuals and populations across the landscape. Static representations of connectivity potentially overlook this variation and therefore adopting a dynamic approach should offer improved insights about connectivity and associated ecological processes. Using a large-scale, long-term time series of occupancy data from a metapopulation of water voles Arvicola amphibius, we tested competing hypotheses about how considering the dynamic nature of connectivity improves the ability of spatially explicit occupancy models to recover population dynamics. Iteratively relaxing standing assumptions of connectivity metrics, these models ranged from spatially and temporally fixed connectivity metrics that are widely applied, to the more flexible, but lesser used model that allowed temporally varying connectivity measures that incorporate spatiotemporally dynamic patch occupancy states. Our results provide empirical evidence that demographic weighting using patch occupancy dynamics and temporal variability in connectivity measures are important for describing metapopulation dynamics. We highlight the implications of commonly held assumption in connectivity modelling and demonstrate how they result in different and highly variable predictions of metapopulation capacity. Thus, we argue that the concept of connectivity and its potential applications would benefit from recognizing inherent spatiotemporal variation in connectivity that is explicitly linked to underlying ecological state variables.

Predictability and transferability of local biodiversity environment relationships

Background

Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures-such as species richness or abundance-are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted.

Methods

Here I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within ("predictability") and across similar ("transferability") biodiversity surveys.

Results

I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question-particular for transferability-our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.

The spatial and temporal distribution of females influence the evolution of testes size in Australian rodents

Male-male competition after mating (sperm competition) favours adaptations in male traits, such as elevated sperm numbers facilitated by larger testes. Ultimately, patterns of female distribution will affect the strength of sperm competition by dictating the extent to which males are able to prevent female remating. Despite this, our understanding of how the spatial and temporal distributions of mating opportunities have shaped the evolutionary course of sperm competition is limited. Here, we use phylogenetic comparative methods to explore interspecific variation in testes size in relation to patterns of female distribution in Australian rodents. We find that as mating season length (temporal distribution of females) increases, testes size decreases, which is consistent with the idea that it is difficult for males to prevent females from remating when overlap among oestrous females is temporally concentrated. Additionally, we find that social species (spatially clustered) have smaller testes than non-social species (spatially dispersed). This result suggests that males may be effective in monopolizing reproduction within social groups, which leads to reduced levels of sperm competition relative to non-social species where free-ranging females cannot be controlled. Overall, our results show that patterns of female distribution, in both space and time, can influence the strength of post-mating sexual selection among species.

Ecosystem Functioning Influences Species Fitness at Upper Trophic Levels

Global change is severely affecting ecosystem functioning and biodiversity globally. Remotely sensed ecosystem functional attributes (EFAs) are integrative descriptors of the environmental change—being closely related to the processes directly affecting food chains via trophic cascades. Here we tested if EFAs can explain the species fitness at upper trophic levels. We took advantage of a long-term time series database of the reproductive success of the Golden Eagle (Aquila chrysaetos)—an apex predator at the upper trophic level—over a 17-year period across a bioclimatic gradient (NW Spain; c. 29,575 km2). We computed a comprehensive database of EFAs from three MODIS satellite-products related to the carbon cycle, heat dynamics and radiative balance. We also assessed possible time-lag in the response of the Golden Eagle to fire, a critical disruptor of the surface energy budget in our region. We explored the role of EFAs on the fitness of the Golden Eagle with logistic-exposure nest survival models. Our models showed that the reproductive performance of the Golden Eagle is influenced by spatiotemporal variations in land surface temperature, albedo and vegetation productivity (AUC values from 0.71 to 0.8; ΣWi EFAs from 0.66 to 1). Fire disturbance also affected ecological fitness of this apex predator—with a limited effect at 3 years after fire (a time-lagged response to surface energy budget disruptions; ΣWi Fire = 0.62). Our study provides evidence for the influence of the matter and energy fluxes between land surface and atmosphere on the reproductive success of species at upper trophic levels.

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).

Influence of local and landscape factors on distributional dynamics: a species-centred, fitness-based approach

In spatially structured populations, distributional dynamics are driven by the quantity, connectivity and quality of habitat. Because these drivers are rarely measured directly and simultaneously at relevant scales, information on their relative importance remains unclear. I assessed the influence of both direct and indirect measures of local habitat quality, and of landscape habitat amount and connectivity on long-term territory occupancy dynamics of non-migratory pygmy owls. Direct measures of local habitat quality based on territory-specific reproductive output had greater effects on distribution than landscape factors, but only when spatio-temporal fluxes in performance linked to environmental stochasticity and intraspecific competition were considered. When habitat quality was measured indirectly based on habitat structure, however, landscape factors had greater effects. Although all landscape factors were important, measures of landscape connectivity that were uncorrelated with habitat amount and based on attributes of matrix structure and habitat configuration that influence dispersal movements had greater effects than habitat effective area (amount weighted by quality). Moreover, the influence of connectivity (but not habitat effective area) depended on local habitat quality. Such results suggest the relative importance of local habitat quality in driving distribution has been underestimated and that conservation strategies should vary spatially depending on both local and landscape contexts.