Effects of climate change on the potential habitat distribution of swimming crab Portunus trituberculatus under the species distribution model

Journal of Oceanology and Limnology, Год журнала: 2022, Номер 40(4), С. 1556 - 1565

Опубликована: Апрель 28, 2022

Язык: Английский

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Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA

Frontiers in Marine Science, Год журнала: 2022, Номер 9

Опубликована: Май 16, 2022

Whale populations recovering from historical whaling are particularly vulnerable to incidental mortality and disturbance caused by growing ocean industrialization. Several distinct of rorqual whales (including humpback, blue, fin whales) migrate feed off the coast Oregon, USA where spatial overlap with human activities on rise. Effective mitigation conflicts requires better foundational understanding temporal habitat use patterns inform conservation management. Based a year-round, multi-platform distance sampling dataset (2016-2021, 177 survey days, 754 groups observed), this study generated density models describe predict seasonal distribution in Oregon. Phenology analysis sightings revealed peak humpback whale blue over Oregon continental shelf August September respectively, higher winter (December). Additionally, we compared sighting rates across three decades effort (since 1989) demonstrate that strikingly more prevalent current dataset, including increases whales. Finally, surface relating densities static dynamic environmental variables acquired data-assimilative summer spring were influenced oceanographic features indicative active upwelling frontal zones (respectively 27% 40% deviance explained). On shelf, predicted occur closer shore than southern waters Summer models, showed predictive performance suitable for management purposes, assessed through internal cross-validation comparison an external (388 observed). Indeed, monthly hotspots high multiple years validated independent (80% model). These lay robust basis fine-scale reduce impacts endangered

Язык: Английский

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A machine learning approach for protected species bycatch estimation

Frontiers in Marine Science, Год журнала: 2024, Номер 11

Опубликована: Апрель 15, 2024

Introduction Monitoring bycatch of protected species is a fisheries management priority. In practice, difficult to precisely or accurately estimate with commonly used ratio estimators parametric, linear model-based methods. Machine-learning algorithms have been proposed as means overcoming some the analytical hurdles in estimating bycatch. Methods Using 17 years set-specific data derived from 100% observer coverage Hawaii shallow-set longline fishery and 25 aligned environmental predictors, we evaluated new approach for estimation using Ensemble Random Forests (ERFs). We tested ability ERFs predict interactions five varying levels methods correcting these predictions Type I II error rates training data. also assessed amount needed inform ERF by mimicking sequential addition each subsequent fishing year. Results showed that was most effective greater than 2% interaction correction improved estimates all but introduced tendency regress towards mean Training needs differed among those above required 7-12 Discussion Our machine learning can improve rare comparisons are other approaches assess which perform best hyperrare species.

Язык: Английский

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Will climate change cause Sargassum beds in temperate waters to expand or contract? Evidence from the range shift pattern of Sargassum

Marine Environmental Research, Год журнала: 2024, Номер 200, С. 106659 - 106659

Опубликована: Июль 28, 2024

Язык: Английский

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Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation

Ecological Modelling, Год журнала: 2022, Номер 470, С. 110011 - 110011

Опубликована: Май 5, 2022

Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale distribution predictions harbour porpoise (Phocoena phocoena) using empirical aerial-video-survey data collected along east coast Scotland in August and September 2010 2014. Incorporating environmental covariates that cover habitat preferences prey proxies, a traditional (and commonly implemented) Generalized Additive Model (GAM), two Hierarchical Bayesian Modelling (HBM) approaches Integrated Nested Laplace Approximation (INLA) model-fitting methodology. One HBM-INLA modelled gridded space (similar GAM), other dealt more explicitly continuous Log-Gaussian Cox Process (LGCP). Overall, predicted distributions three models were similar; however, HBMs had twice level certainty, showed much finer-scale patterns distribution, identified some areas high relative density not apparent GAM. Spatial differences due how accounted for autocorrelation, clustering animals, between discrete vs. space; consequently, analyses likely depend on scale at which results, needed. For large-scale analysis (>5–10 km resolution, e.g. initial impact assessment), there was little difference results; insights into (<1 km) from HBM model LGCP, while computationally costly, offered potential benefits refining conservation or mitigation measures within offshore developments protected areas.

Язык: Английский

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