Using spatially expanding populations as a tool for evaluating landscape planning: The reintroduced Persian fallow deer as a case study

Assessing Global Efforts in the Selection of Vertebrates as Umbrella Species for Conservation

The umbrella-species strategy has been proposed as an attainable tool to achieve multi-species and community conservation with limited investment. There have been many umbrella-related studies since the concept's inception; thus, a summary of global study efforts and recommended umbrella species is important for understanding advances in the field and facilitating conservation applications. Here, we collated 213 recommended umbrella species of terrestrial vertebrates from 242 scientific articles published during 1984-2021 and analyzed their geographic patterns, biological features, and conservation statuses to identify global trends in the selection of umbrella species. We found a considerable geographic bias: most studies and, consequently, recommended umbrella species are from the Northern Hemisphere. There is also a strong taxonomic bias, with grouses (order Galliformes) and large carnivores being the most popular umbrella species and amphibians and reptiles being largely overlooked. In addition, wide-ranging and non-threatened species were frequently recommended as umbrella species. Given the observed biases and trends, we caution that appropriate species need to be chosen for each location, and it is important to confirm that popular, wide-ranging species are effective umbrella species. Moreover, amphibians and reptiles should be investigated for their potential as umbrella species. The umbrella-species strategy has many strengths and, if applied appropriately, may be one of the best options in today's conservation research and funding landscape.

Long-term reevaluation of spatially explicit models as a means for adaptive wildlife management

We evaluated a 20-yr-old spatially explicit model (SEM) that predicted the spatial expansion of reintroduced Persian fallow deer in northern Israel. Using the current distribution of the deer and based on multi-model inference we assessed the accuracy of the SEM's prediction and what other factors affected the population's current distribution. If the SEM's projection was still valid, the leading model in the multi-model inference would include only the SEM's projection as an explanatory variable with a good fit. Different leading models would reveal key variables overlooked when the SEM was constructed or changes in the landscape unforeseen at the time, thus assisting adaptive management and decision-making. We assessed deer presence from camera trap encounter counts analyzed using N-mixture models. Models included various combinations of seven predictors: the 20-yr predictions of an SEM developed during the initial phases of the reintroduction, three key landscape characteristics on which the SEM was originally based but updated to reflect current conditions, distance from the release site, elevation, and the distribution of gray wolves (a predator that was absent from the area when the SEM was developed). Competing models were ranked by Akaike information criterion (AIC). Wolf distribution was the key predictor explaining the current deer distribution, appearing in all three leading models (∆AIC < 2.0) and carrying 71% of the AIC weight (coefficient = -14.86 ± 5.6 [mean ± SE]). Of these three models, the SEM 20-yr prediction appeared in two, but explained only a fraction of the variance (coefficient = 0.001 ± 0.08). The contribution of all other predictors was negligible. While the SEM failed to accurately predict the 20-yr deer distribution, the divergence between its projection and reality pointed to the probable cause (wolves) of this discrepancy. The inclusion of the SEM prediction in the leading models indicates that had the wolves not spread to the study area, the predictions would still have merit suggesting that long-term SEMs can potentially be robust. Long-term reevaluation of SEMs can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.

Applying circuit theory and landscape linkage maps to reintroduction planning for California Condors

Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor (Gymnogyps californianus) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.

Computational Population Biology: Linking the inner and outer worlds of organisms

Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable, framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents, simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way population biologists communicate and share model components and the models themselves, fit, test, refute, and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology.

Effectiveness of multiple release sites in reintroduction of Persian fallow deer

Releasing animals in more than one location may increase or decrease the probability of success of a reintroduction project, yet the question of how many release sites to use has received little attention. We used empirical data from the reintroduction program of the Persian fallow deer (Dama mesopotamica) (Galilee region in northern Israel) in an individual-based spatially explicit simulation model to assess the effects of releasing deer from multiple sites. We examined whether multiple release sites increase reintroduction success, and if so, whether the optimal number of sites for a given scenario can be determined and whether the outcome differs if animals are released alternately (i.e., the location of the release alternates yearly between sites) or consecutively (i.e., one release site is used for several years, then another is used, and so forth). We selected 8 potential release sites in addition to the original site and simulated the release of 180 individuals at a rate of 10 individuals per year in different combinations of the original site and 1-4 additional sites. In our model, releasing animals into the wild at multiple sites produced higher population growth and greater spatial expansion than releasing animals at only one site and a consecutive-release approach was superior to an alternate-release approach. We suggest that through the use of simulation modeling that is based on empirical data from previous releases, managers can make better-informed decisions regarding the use of multiple release sites and greatly improve the probability of reintroduction success.