The Long-Term Effect of Bleeding for Limulus Amebocyte Lysate on Annual Survival and Recapture of Tagged Horseshoe Crabs - David R. Smith; Joshua J. Newhard; Conor P. McGowan; C. Alyssa Butler (Frontiers in Marine Science, 2020)
In the U.S., 525,000 horseshoe crabs ( Limulus polyphemus ) per year have been captured during 2013–2017, brought to biomedical facilities, and bled to produce Limulus amebocyte lysate (LAL), then mostly released to the area of capture. The Atlantic States Marine Fisheries Commission estimates short-term bleeding-induced mortality to be 15% (4% to 30%), resulting in mortality of approximately 78,750 horseshoe crabs annually in recent years comprising a minor portion (<13%) of the up to one million annual coastwide landings dominated by harvest for bait. However, the long-term effect of bleeding for LAL on annual survival and spawning behavior is unknown; thus, results from short-term studies alone might underestimate bleeding effects at the population level. To address this knowledge gap, we analyzed data from the U.S. Fish and Wildlife horseshoe crab tagging database to estimate the differences in survival and recapture rates of bled and not bled horseshoe crabs tagged in the same years and geographic area. Contrary to expectation, survival was not lower for bled crabs compared to unbled crabs. Differences varied, but survival estimates tended to be higher for bled crabs than for unbled crabs. However, biomedical culling and selection for younger or healthier animals could have resulted in biomedically tagged individuals representing a healthier subset of the overall population with subsequent higher survival. Furthermore, the tagging analysis revealed a post-bleeding reduction in capture probability, which could indicate decreased spawning activity, evident in males more than females. Continued tagging of bled and unbled crabs in the same geographic area while recording age class and sex will contribute to the further resolution of LAL production’s effect on horseshoe crab populations.

Cross‐scale integration of knowledge for predicting species ranges: a metamodelling framework - Lauren Talluto; Isabelle Boulangeat; Aitor Ameztegui; Isabelle Aubin; Dominique Berteaux; Alyssa Butler; Frédérik Doyon; C. Ronnie Drever; Marie‐Josée Fortin; Tony Franceschini; Jean Liénard; Dan McKenney; et al. (Global Ecology and Biogeography, 2016)
Aim Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models ( SDMs ). However, most approaches include only a small subset of the available information, and many ignore smaller‐scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. Location E astern N orth A merica (as an example). Methods Our framework builds a metamodel that is constrained by the results of multiple sub‐models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence–absence data for sugar maple ( A cer saccharum ), an abundant tree native to eastern North America. Results For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. Main conclusions We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off‐the‐shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi‐source and multi‐scale data into ecological decision‐making.