Assessing cetacean populations using integrated population models: an example with Cook Inlet beluga whales.

Effective conservation and management of animal populations requires knowledge of abundance and trends. For many species, these quantities are estimated using systematic visual surveys. Additional individual-level data are available for some species. Integrated population modeling (IPM) offers a mechanism for leveraging these data sets into a single estimation framework. IPMs that incorporate both population- and individual-level data have previously been developed for birds, but have rarely been applied to cetaceans. Here, we explore how IPMs can be used to improve the assessment of cetacean populations. We combined three types of data that are typically available for cetaceans of conservation concern: population-level visual survey data, individual-level capture-recapture data, and data on anthropogenic mortality. We used this IPM to estimate the population dynamics of the Cook Inlet population of beluga whales (CIBW; Delphinapterus leucas) as a case study. Our state-space IPM included a population process model and three observational submodels: (1) a group detection model to describe group size estimates from aerial survey data; (2) a capture-recapture model to describe individual photographic capture-recapture data; and (3) a Poisson regression model to describe historical hunting data. The IPM produces biologically plausible estimates of population trajectories consistent with all three data sets. The estimated population growth rate since 2000 is less than expected for a recovering population. The estimated juvenile/adult survival rate is also low compared to other cetacean populations, indicating that low survival may be impeding recovery. This work demonstrates the value of integrating various data sources to assess cetacean populations and serves as an example of how multiple, imperfect data sets can be combined to improve our understanding of a population of interest. The model framework is applicable to other cetacean populations and to other taxa for which similar data types are available.

Using a mark-recapture model to estimate beaching probability of seabirds killed in nearshore waters during the Deepwater Horizon oil spill.

During a marine oil spill, injured birds often die on the water, some eventually washing ashore, but others becoming waterlogged and sinking or being scavenged before reaching the shoreline. Birds that disappear before they can be deposited on the shoreline are difficult to enumerate, but they commonly represent a large fraction of total oil spill-related mortality. As part of the process of quantifying the overall impact to seabirds resulting from the Deepwater Horizon oil spill, it was necessary to estimate the number of dead birds lost at sea. We conducted a study to estimate the beaching probability of birds that died at sea in the Gulf of Mexico in the areas most heavily used by seabirds and impacted by the spill. Using a mark-recapture analysis to derive the beaching probability from our field study data, we estimated that dead birds afloat at sea had about a 0.1414 probability of beaching in areas searched during the Deepwater Horizon oil spill. Sensitivity analyses of our model and requisite assumptions suggested that if our assumptions were violated, the "true" beaching probability could be anywhere between 0.11 and 0.16. These estimates are much lower than beaching probabilities estimated for seabirds killed during the Exxon Valdez oil spill in the waters of Alaska, for example, likely reflect higher rates of decomposition and scavenging in the warmer waters of the Gulf of Mexico. Our estimate suggests that bird carcasses that washed onshore during the Deepwater Horizon oil spill represented only 14% of those killed at sea during the spill.

Mechanism matters: the cause of fluctuations in boom-bust populations governs optimal habitat restoration strategy.

Many populations exhibit boom-bust dynamics in which abundance fluctuates dramatically over time. Past research has focused on identifying whether the cause of fluctuations is primarily exogenous, e.g., environmental stochasticity coupled with weak density dependence, or endogenous, e.g., over-compensatory density dependence. Far fewer studies have addressed whether the mechanism responsible for boom-bust dynamics matters with respect to at-risk species management. Here, we ask whether the best strategy for restoring habitat across a landscape differs under exogenously vs. endogenously driven boom-bust dynamics. We used spatially explicit individual-based models to assess how butterfly populations governed by the two mechanisms would respond to habitat restoration strategies that varied in the level of resource patchiness, from a single large patch to multiple patches spaced at different distances. Our models showed that the restoration strategy that minimized extinction risk and boom-bust dynamics would be markedly different depending on the governing mechanism. Exogenously governed populations fared best in a single large habitat patch, whereas for endogenously driven populations, boom-bust dynamics were dampened and extinction risk declined when the total restored area was split into multiple patches with low to moderate inter-patch spacing. Adding environmental stochasticity to the endogenous model did not alter this result. Habitat fragmentation lowered extinction risk in the endogenously driven populations by reducing their growth rate, precluding both "boom" phases and, more importantly, "bust" phases. Our findings suggest that (1) successful restoration will depend on understanding the causes of fluctuations in at-risk populations, (2) the level and pattern of spatiotemporal environmental heterogeneity will also affect the ideal management approach, and (3) counterintuitively, for at-risk species with endogenously governed boom-bust dynamics, lowering the intrinsic population growth rate may decrease extinction risk.

A framework for developing objective and measurable recovery criteria for threatened and endangered species.

For species listed under the U.S. Endangered Species Act (ESA), the U.S. Fish and Wildlife Service and National Marine Fisheries Service are tasked with writing recovery plans that include "objective, measurable criteria" that define when a species is no longer at risk of extinction, but neither the act itself nor agency guidelines provide an explicit definition of objective, measurable criteria. Past reviews of recovery plans, including one published in 2012, show that many criteria lack quantitative metrics with clear biological rationale and are not meeting the measureable and objective mandate. I reviewed how objective, measureable criteria have been defined implicitly and explicitly in peer-reviewed literature, the ESA, other U.S. statutes, and legal decisions. Based on a synthesis of these sources, I propose the following 6 standards be used as minimum requirements for objective, measurable criteria: contain a quantitative threshold with calculable units, stipulate a timeframe over which they must be met, explicitly define the spatial extent or population to which they apply, specify a sampling procedure that includes sample size, specify a statistical significance level, and include justification by providing scientific evidence that the criteria define a species whose extinction risk has been reduced to the desired level. To meet these 6 standards, I suggest that recovery plans be explicitly guided by and organized around a population viability modeling framework even if data or agency resources are too limited to complete a viability model. When data and resources are available, recovery criteria can be developed from the population viability model results, but when data and resources are insufficient for model implementation, extinction risk thresholds can be used as criteria. A recovery-planning approach centered on viability modeling will also yield appropriately focused data-acquisition and monitoring plans and will facilitate a seamless transition from recovery planning to delisting.