Marine heatwave challenges solutions to human-wildlife conflict.

Despite the increasing frequency and magnitude of extreme climate events, little is known about how their impacts flow through social and ecological systems or whether management actions can dampen deleterious effects. We examined how the record 2014-2016 Northeast Pacific marine heatwave influenced trade-offs in managing conflict between conservation goals and human activities using a case study on large whale entanglements in the U.S. west coast's most lucrative fishery (the Dungeness crab fishery). We showed that this extreme climate event diminished the power of multiple management strategies to resolve trade-offs between entanglement risk and fishery revenue, transforming near win-win to clear win-lose outcomes (for whales and fishers, respectively). While some actions were more cost-effective than others, there was no silver-bullet strategy to reduce the severity of these trade-offs. Our study highlights how extreme climate events can exacerbate human-wildlife conflict, and emphasizes the need for innovative management and policy interventions that provide ecologically and socially sustainable solutions in an era of rapid environmental change.

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991-2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.

Pregnancy is a drag: hydrodynamics, kinematics and performance in pre- and post-parturition bottlenose dolphins (Tursiops truncatus).

Constraints on locomotion could be an important component of the cost of reproduction as carrying an increased load associated with eggs or developing fetuses may contribute to decreased locomotor performance for females across taxa and environments. Diminished performance could increase susceptibility to predation, yet the mechanism(s) by which gravidity and pregnancy affect locomotion remains largely unexplored. Here we demonstrate that morphology, hydrodynamics and kinematics were altered during pregnancy, providing a mechanism for diminished locomotor performance in two near-term pregnant (10 days pre-parturition) bottlenose dolphins (Tursiops truncatus). Near-term pregnancy resulted in a 56 ± 13% [corrected] increase in frontal surface area, coinciding with dramatic increases in drag forces while gliding. For example, pregnant females encountered 80 N of drag at 1.7 m s(-1) whereas that magnitude of drag was not encountered until speed doubled for females 18 months post-parturition. Indeed, drag coefficients based on frontal surface area were significantly greater during pregnancy (C(d,F)=0.22 ± 0.04) than at 18 months post-parturition (C(d,F)=0.09 ± 0.01). Pregnancy also induced a gait change as stroke amplitude and distance per stroke were reduced by 13 and 14%, respectively, compared with non-pregnant periods (1-24 months post-parturition). This was concomitant with a 62 and 44% reduction in mean and maximum swim speeds, respectively, during the pregnancy period. Interestingly, attack speeds of known predators of dolphins surpass maximum speeds for the pregnant dolphins in this study. Thus, pregnant dolphins may be more susceptible to predation. This study demonstrates unequivocally that changes in morphology, hydrodynamics and kinematics are associated with diminished performance during pregnancy in dolphins.

Novel computational identification of highly selective biomarkers of pollutant exposure.

The use of in vivo biosensors to acquire environmental pollution data is an emerging and promising paradigm. One major challenge is the identification of highly specific biomarkers that selectively report exposure to a target pollutant, while remaining quiescent under a diverse set of other, often unknown, environmental conditions. This study hypothesized that a microarray data mining approach can identify highly specific biomarkers, and, that the robustness property can generalize to unforeseen environmental conditions. Starting with Arabidopsis thaliana microarray data measuring responses to a variety of treatments, the study used the top scoring pair (TSP) algorithm to identify mRNA transcripts that respond uniquely to phenanthrene, a model polycyclic aromatic hydrocarbon. Subsequent in silico analysis with a larger set of microarray data indicated that the biomarkers remained robust under new conditions. Finally, in vivo experiments were performed with unforeseen conditions that mimic phenanthrene stress, and the biomarkers were assayed using qRT-PCR. In these experiments, the biomarkers always responded positively to phenanthrene, and never responded to the unforeseen conditions, thereby supporting the hypotheses. This data mining approach requires only microarray or next-generation RNA-seq data, and, in principle, can be applied to arbitrary biomonitoring organisms and chemical exposures.