Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial–temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal–vertical movement model results with spatial–temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004–2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle–fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) ( https://www.upwell.org/sptw ), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial–temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas

As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population‐level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well‐surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep‐sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.     

Effects of Multiple Levels of Social Organization on Survival and Abundance

Abstract: Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group‐level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age‐specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.     

Dependence of the Endangered Black‐Capped Vireo on Sustained Cowbird Management

Conservation‐reliant species depend on active management, even after surpassing recovery goals, for protection from persistent threats. Required management may include control of another species, habitat maintenance, or artificial recruitment. Sometimes, it can be difficult to determine whether sustained management is required. We used nonspatial stochastic population projection matrix simulation and a spatially explicit population model to estimate the effects of parasitism by a brood parasite, the Brown‐headed Cowbird (Moluthrus ater), on a population of endangered Black‐capped Vireos (Vireo atricapilla). We simulated parasitism as a percentage of breeding vireo pairs experiencing decreased fecundity due to cowbirds. We estimated maximum sustainable parasitism (i.e., highest percentage of parasitized vireo breeding pairs for which population growth is ≥1) with the nonspatial model under multiple scenarios designed to assess sensitivity to assumptions about population growth rate, demographic effects of parasitism, and spatial distribution of parasitism. We then used the spatially explicit model to estimate cumulative probabilities of the population falling below the population recovery target of 1000 breeding pairs for a range of parasitism rates under multiple scenarios. We constructed our models from data on vireos collected on the Fort Hood Military Reservation, Texas (U.S.A.). Estimates of maximum sustainable parasitism rates ranged from 9–12% in scenarios with a low (6%) vireo population growth rate to 49–60% in scenarios with a high (24%) growth rate. Sustained parasitism above 45–85%, depending on the scenario, would likely result in the Fort Hood Vireo population dropping below its recovery goal within the next 25 years. These estimates suggest that vireos, although tolerant of low parasitism rates, are a conservation‐reliant species dependent on cowbird management. Dependencia de Vireo atricapilla, Especie en Peligro, hacia el Manejo Sostenido de Moluthurs ater     

Rapid assessment of management options for promoting stock rebuilding in data-poor species under climate change

The development of species recovery plans requires considering likely outcomes of different management interventions, but the complicating effects of climate change are rarely evaluated. We examined how qualitative network models (QNMs) can be deployed to support decision making when data, time, and funding limitations restrict use of more demanding quantitative methods. We used QNMs to evaluate management interventions intended to promote the rebuilding of a collapsed stock of blue king crab (Paralithodes platypus) (BKC) around the Pribilof Islands (eastern Bering Sea) to determine how their potential efficacy may change under climate change. Based on stakeholder input and a literature review, we constructed a QNM that described the life cycle of BKC, key ecological interactions, potential climate-change impacts, relative interaction strengths, and uncertainty in terms of interaction strengths and link presence. We performed sensitivity analyses to identify key sources of prediction uncertainty. Under a scenario of no climate change, predicted increases in BKC were reliable only when stock enhancement was implemented in a BKC hatchery-program scenario. However, when climate change was accounted for, the intervention could not counteract its adverse impacts, which had an overall negative effect on BKC. The remaining management scenarios related to changes in fishing effort on BKC predators. For those scenarios, BKC outcomes were unreliable, but climate change further decreased the probability of observing recovery. Including information on relative interaction strengths increased the likelihood of predicting positive outcomes for BKC approximately 5–50% under the management scenarios. The largest gains in prediction precision will be made by reducing uncertainty associated with ecological interactions between adult BKC and red king crab (Paralithodes camtschaticus). Qualitative network models are useful options when data are limited, but they remain underutilized in conservation.     

Use of Coarse‐Resolution Models of Species’ Distributions to Guide Local Conservation Inferences

Abstract: Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter (Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time‐consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse‐resolution distribution data are available to define high‐quality areas at a scale that is practical for the application of concrete conservation measures.