Spatio-Temporal Variation in Age Structure and Abundance of the Endangered Snail Kite: Pooling across Regions Masks a Declining and Aging Population

Quantifying the age structure of free-ranging delphinid populations: Testing the accuracy of Unoccupied Aerial System photogrammetry

Understanding the population health status of long-lived and slow-reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population-level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age structure). We tested a novel and frequentist approach, using Unoccupied Aerial System (UAS) photogrammetry, to assess the population age structure of small delphinids. First, we measured the precision and accuracy of UAS photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (Tursiops truncatus). Using a log-transformed linear model, we estimated TL using the blowhole to dorsal fin distance (BHDF) for surfacing animals. To test the performance of UAS photogrammetry to age-classify individuals, we then used length measurements from a 35-year dataset from a free-ranging bottlenose dolphin community to simulate UAS estimates of BHDF and TL. We tested five age classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS-simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS-estimated BHDF. Our age classifiers performed best in predicting age-class when using broader and fewer (two and three) age-class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5%-93% of the individuals were correctly classified within 2 years of their actual age-class bin. Similar classification performances were obtained using both proxies. UAS photogrammetry is a non-invasive, inexpensive, and effective method to estimate TL and age-class of free-swimming dolphins. UAS photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.

The demographic contributions of connectivity versus local dynamics to population growth of an endangered bird

Conservation and management increasingly focus on connectivity, because connectivity driven by variation in immigration rates across landscapes is thought to be crucial for maintaining local population and metapopulation persistence. Yet, efforts to quantify the relative role of immigration on population growth across the entire range of species and over time have been lacking. We assessed whether immigration limited local and range-wide population growth of the endangered snail kite Rostrhamus sociabilis in Florida, USA, over 18 years using multi-state, reverse-time modelling that accounts for imperfect detection of individuals and unobservable states. Demographic contributions of immigration varied depending on the dynamics and geographic position of the local populations, were scale-dependent and changed over time. By comparing the relative contributions of immigration versus local demography for periods of significant change in local abundance, we found empirical evidence for a disproportionately large role of immigration in facilitating population growth of a centrally located population-a connectivity 'hub'. The importance of connectivity changed depending of the spatial scale considered, such that immigration was a more important driver of population growth at small versus large spatial scales. Furthermore, the contribution of immigration was much greater during time periods when local population size was small, emphasizing abundance-dependent rescue effects. Our findings suggest that efforts aimed at improving local breeding habitat will likely be most effective at increasing snail kite population growth. More broadly, our results provide much needed information on the role of connectivity for population growth, suggesting that connectivity conservation may have the greatest benefits when efforts focus on centrally located habitat patches and small populations. Furthermore, our results highlight that connectivity is highly dynamic over time and that interpreting the effects of connectivity at local scales may not transfer to region-wide dynamics.

Rapid morphological change of a top predator with the invasion of a novel prey

Invasive exotic species are spreading rapidly throughout the planet. These species can have widespread impacts on biodiversity, yet the ability for native species, particularly long-lived vertebrates, to respond rapidly to invasions remains mostly unknown. Here we provide evidence of rapid morphological change in the endangered snail kite (Rostrhamus sociabilis) across its North American range with the invasion of a novel prey, the island apple snail (Pomacea maculata), a much larger congener of the kite's native prey. In less than one decade since invasion, snail kite bill size and body mass increased substantially. Larger bills should be better suited to extracting meat from the larger snail shells, and we detected strong selection on increased size through juvenile survival. Using pedigree data, we found evidence of both genetic and environmental influences on trait expression and discovered that additive genetic variation in bill size increased with invasion. However, trends in predicted breeding values emphasize that recent morphological changes have been driven primarily by phenotypic plasticity rather than micro-evolutionary change. Our findings suggest that evolutionary change may be imminent and underscore that even long-lived vertebrates can respond quickly to invasive species. Furthermore, these results highlight that phenotypic plasticity may provide a crucial role for predators experiencing rapid environmental change.

Detecting population-environmental interactions with mismatched time series data

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.

Individual Movement Strategies Revealed through Novel Clustering of Emergent Movement Patterns

Understanding movement is critical in several disciplines but analysis methods often neglect key information by adopting each location as sampling unit, rather than each individual. We introduce a novel statistical method that, by focusing on individuals, enables better identification of temporal dynamics of connectivity, traits of individuals that explain emergent movement patterns, and sites that play a critical role in connecting subpopulations. We apply this method to two examples that span movement networks that vary considerably in size and questions: movements of an endangered raptor, the snail kite (Rostrhamus sociabilis plumbeus), and human movement in Florida inferred from Twitter. For snail kites, our method reveals substantial differences in movement strategies for different bird cohorts and temporal changes in connectivity driven by the invasion of an exotic food resource, illustrating the challenge of identifying critical connectivity sites for conservation in the presence of global change. For human movement, our method is able to reliably determine the origin of Florida visitors and identify distinct movement patterns within Florida for visitors from different places, providing near real-time information on the spatial and temporal patterns of tourists. These results emphasize the need to integrate individual variation to generate new insights when modeling movement data.