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Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
Non-consumptive effects of predators within ecosystems can alter the behavior of individual prey species, and have cascading effects on other trophic levels. In this context, an understanding of non-consumptive predator effects on the whole prey community is crucial for predicting community structure and composition, hence biodiversity patterns. We used an individual-based, spatially-explicit modelling approach to investigate the consequences of landscapes of fear on prey community metrics. The model spans multiple hierarchical levels from individual home range formation based on food availability and perceived predation risk to consequences on prey community structure and composition. This mechanistic approach allowed us to explore how important factors such as refuge availability and foraging strategy under fear affect prey community metrics. Fear of predators affected prey space use, such as home range formation. These adaptations had broader consequences for the community leading to changes in community structure and composition. The strength of community responses to perceived predation risk was driven by refuge availability in the landscape and the foraging strategy of prey animals. Low refuge availability in the landscape strongly decreased diversity and total biomass of prey communities. Additionally, body mass distributions in prey communities facing high predation risk were shifted towards small prey animals. With increasing refuge availability the consequences of non-consumptive predator effects were reduced, diversity and total biomass of the prey community increased. Prey foraging strategies affected community composition. Under medium refuge availability, risk-averse prey communities consisted of many small animals while risk-taking prey communities showed a more even body mass distribution. Our findings reveal that non-consumptive predator effects can have important implications for prey community diversity and should therefore be considered in the context of conservation and nature management.
Home range size and resource use of breeding and non-breeding white storks along a land use gradient
(2018)
Biotelemetry is increasingly used to study animal movement at high spatial and temporal resolution and guide conservation and resource management. Yet, limited sample sizes and variation in space and habitat use across regions and life stages may compromise robustness of behavioral analyses and subsequent conservation plans. Here, we assessed variation in (i) home range sizes, (ii) home range selection, and (iii) fine-scale resource selection of white storks across breeding status and regions and test model transferability. Three study areas were chosen within the Central German breeding grounds ranging from agricultural to fluvial and marshland. We monitored GPS-locations of 62 adult white storks equipped with solar-charged GPS/3D-acceleration (ACC) transmitters in 2013-2014. Home range sizes were estimated using minimum convex polygons. Generalized linear mixed models were used to assess home range selection and fine-scale resource selection by relating the home ranges and foraging sites to Corine habitat variables and normalized difference vegetation index in a presence/pseudo-absence design. We found strong variation in home range sizes across breeding stages with significantly larger home ranges in non-breeding compared to breeding white storks, but no variation between regions. Home range selection models had high explanatory power and well predicted overall density of Central German white stork breeding pairs. Also, they showed good transferability across regions and breeding status although variable importance varied considerably. Fine-scale resource selection models showed low explanatory power. Resource preferences differed both across breeding status and across regions, and model transferability was poor. Our results indicate that habitat selection of wild animals may vary considerably within and between populations, and is highly scale dependent. Thereby, home range scale analyses show higher robustness whereas fine-scale resource selection is not easily predictable and not transferable across life stages and regions. Such variation may compromise management decisions when based on data of limited sample size or limited regional coverage. We thus recommend home range scale analyses and sampling designs that cover diverse regional landscapes and ensure robust estimates of habitat suitability to conserve wild animal populations.
A challenge for eco-evolutionary research is to better understand the effect of climate and landscape changes on species and their distribution. Populations of species can respond to changes in their environment through local genetic adaptation or plasticity, dispersal, or local extinction. The individual-based modeling (IBM) approach has been repeatedly applied to assess organismic responses to environmental changes. IBMs simulate emerging adaptive behaviors from the basic entities upon which both ecological and evolutionary mechanisms act. The objective of this review is to summarize the state of the art of eco-evolutionary IBMs and to explore to what degree they already address the key responses of organisms to environmental change. In this, we identify promising approaches and potential knowledge gaps in the implementation of eco-evolutionary mechanisms to motivate future research. Using mainly the ISI Web of Science, we reveal that most of the progress in eco-evolutionary IBMs in the last decades was achieved for genetic adaptation to novel local environmental conditions. There is, however, not a single eco-evolutionary IBM addressing the three potential adaptive responses simultaneously. Additionally, IBMs implementing adaptive phenotypic plasticity are rare. Most commonly, plasticity was implemented as random noise or reaction norms. Our review further identifies a current lack of models where plasticity is an evolving trait. Future eco-evolutionary models should consider dispersal and plasticity as evolving traits with their associated costs and benefits. Such an integrated approach could help to identify conditions promoting population persistence depending on the life history strategy of organisms and the environment they experience.
Intraspecific trait variation (ITV) is thought to play a significant role in community assembly, but the magnitude and direction of its influence are not well understood. Although it may be critical to better explain population persistence, species interactions, and therefore biodiversity patterns, manipulating ITV in experiments is challenging. We therefore incorporated ITV into a trait‐ and individual‐based model of grassland community assembly by adding variation to the plants’ functional traits, which then drive life‐history tradeoffs. Varying the amount of ITV in the simulation, we examine its influence on pairwise‐coexistence and then on the species diversity in communities of different initial sizes. We find that ITV increases the ability of the weakest species to invade most, but that this effect does not scale to the community level, where the primary effect of ITV is to increase the persistence and abundance of the competitively‐average species. Diversity of the initial community is also of critical importance in determining ITV's efficacy; above a threshold of interspecific diversity, ITV does not increase diversity further. For communities below this threshold, ITV mainly helps to increase diversity in those communities that would otherwise be low‐diversity. These findings suggest that ITV actively maintains diversity by helping the species on the margins of persistence, but mostly in habitats of relatively low alpha and beta diversity.
Moving in the Anthropocene
(2018)
Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.
The impact of inter-annual rainfall variability on African savannas changes with mean rainfall
(2018)
Savannas are mixed tree-grass ecosystems whose dynamics are predominantly regulated by resource competition and the temporal variability in climatic and environmental factors such as rainfall and fire. Hence, increasing inter-annual rainfall variability due to climate change could have a significant impact on savannas. To investigate this, we used an ecohydrological model of stochastic differential equations and simulated African savanna dynamics along a gradient of mean annual rainfall (520–780 mm/year) for a range of inter-annual rainfall variabilities. Our simulations produced alternative states of grassland and savanna across the mean rainfall gradient. Increasing inter-annual variability had a negative effect on the savanna state under dry conditions (520 mm/year), and a positive effect under moister conditions (580–780 mm/year). The former resulted from the net negative effect of dry and wet extremes on trees. In semi-arid conditions (520 mm/year), dry extremes caused a loss of tree cover, which could not be recovered during wet extremes because of strong resource competition and the increased frequency of fires. At high mean rainfall (780 mm/year), increased variability enhanced savanna resilience. Here, resources were no longer limiting and the slow tree dynamics buffered against variability by maintaining a stable population during ‘dry’ extremes, providing the basis for growth during wet extremes. Simultaneously, high rainfall years had a weak marginal benefit on grass cover due to density-regulation and grazing. Our results suggest that the effects of the slow tree and fast grass dynamics on tree-grass interactions will become a major determinant of the savanna vegetation composition with increasing rainfall variability.