9
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Projecting regions of North Atlantic right whale, Eubalaena glacialis, habitat suitability in the Gulf of Maine for the year 2050

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          North Atlantic right whales (Eubalaena glacialis) are critically endangered, and recent changes in distribution patterns have been a major management challenge. Understanding the role that environmental conditions play in habitat suitability helps to determine the regions in need of monitoring or protection for conservation of the species, particularly as climate change shifts suitable habitat. This study used three species distribution modeling algorithms, together with historical whale abundance data (1993–2009) and environmental covariate data, to build monthly ensemble models of past E. glacialis habitat suitability in the Gulf of Maine. The model was projected onto the year 2050 for a range of climate scenarios. Specifically, the distribution of the species was modeled using generalized additive models, boosted regression trees, and artificial neural networks, with environmental covariates that included sea surface temperature, bottom water temperature, bathymetry, a modeled Calanus finmarchicus habitat index, and chlorophyll. Year-2050 projections used downscaled climate anomaly fields from Representative Concentration Pathway 4.5 and 8.5. The relative contribution of each covariate changed seasonally, with an increase in the importance of bottom temperature and C. finmarchicus in the summer, when model performance was highest. A negative correlation was observed between model performance and sea surface temperature contribution. The 2050 projections indicated decreased habitat suitability across the Gulf of Maine in the period from July through October, with the exception of narrow bands along the Scotian Shelf. The results suggest that regions outside of the current areas of conservation focus may become increasingly important habitats for E. glacialis under future climate scenarios.

          Related collections

          Most cited references71

          • Record: found
          • Abstract: found
          • Article: not found

          A working guide to boosted regression trees.

          1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Novel methods improve prediction of species’ distributions from occurrence data

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

                Bookmark

                Author and article information

                Journal
                Elementa: Science of the Anthropocene
                University of California Press
                2325-1026
                April 28 2021
                2021
                April 28 2021
                2021
                : 9
                : 1
                Affiliations
                [1 ]Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA
                [2 ]Colby College, Waterville, ME, USA
                [3 ]Anderson Cabot Center for Ocean Life, New England Aquarium, Boston, MA, USA
                [4 ]Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, NS, Canada
                [5 ]Center for Coastal Studies, Provincetown, MA, USA
                Article
                10.1525/elementa.2020.20.00058
                cd440219-40a1-44ac-98e0-343ee6dd8973
                © 2021

                http://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article