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      The fate of the Arctic seaweed Fucus distichus under climate change: an ecological niche modeling approach

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          Abstract

          Rising temperatures are predicted to melt all perennial ice cover in the Arctic by the end of this century, thus opening up suitable habitat for temperate and subarctic species. Canopy‐forming seaweeds provide an ideal system to predict the potential impact of climate‐change on rocky‐shore ecosystems, given their direct dependence on temperature and their key role in the ecological system. Our primary objective was to predict the climate‐change induced range‐shift of Fucus distichus, the dominant canopy‐forming macroalga in the Arctic and subarctic rocky intertidal. More specifically, we asked: which Arctic/subarctic and cold‐temperate shores of the northern hemisphere will display the greatest distributional change of Fdistichus and how will this affect niche overlap with seaweeds from temperate regions? We used the program MAXENT to develop correlative ecological niche models with dominant range‐limiting factors and 169 occurrence records. Using three climate‐change scenarios, we projected habitat suitability of Fdistichus – and its niche overlap with three dominant temperate macroalgae – until year 2200. Maximum sea surface temperature was identified as the most important factor in limiting the fundamental niche of Fdistichus. Rising temperatures were predicted to have low impact on the species' southern distribution limits, but to shift its northern distribution limits poleward into the high Arctic. In cold‐temperate to subarctic regions, new areas of niche overlap were predicted between Fdistichus and intertidal macroalgae immigrating from the south. While climate‐change threatens intertidal seaweeds in warm‐temperate regions, seaweed meadows will likely flourish in the Arctic intertidal. Although this enriches biodiversity and opens up new seaweed‐harvesting grounds, it will also trigger unpredictable changes in the structure and functioning of the Arctic intertidal ecosystem.

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          Niches, models, and climate change: assessing the assumptions and uncertainties.

          As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some "hotspots" of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option.
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            Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria.

            Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known "true" initial Maxent model, using several different metrics for model quality and transferability. We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.
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              Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

              Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.
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                Author and article information

                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                16 February 2016
                March 2016
                : 6
                : 6 ( doiID: 10.1002/ece3.2016.6.issue-6 )
                : 1712-1724
                Affiliations
                [ 1 ] Faculty of Biosciences and AquacultureNord University Universitetsalleen 11 8049 BodøNorway
                [ 2 ] Shoals Marine LaboratoryUniversity of New Hampshire Durham New Hampshire 03824USA
                Author notes
                [*] [* ] Correspondence

                Alexander Jueterbock, Faculty of Biosciences and Aquaculture, Nord University, Universitetsalleen 11, 8049 Bodø, Norway.

                Tel: +47 97464093;

                Fax: 03212‐2521383;

                E‐mail: Alexander-Jueterbock@ 123456web.de

                Article
                ECE32001
                10.1002/ece3.2001
                4801954
                27087933
                d1301997-fc4e-4e48-9214-6d4547114405
                © 2016 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 September 2015
                : 15 January 2016
                : 23 January 2016
                Page count
                Pages: 13
                Funding
                Funded by: Research Council of Norway
                Award ID: 196505
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece32001
                March 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.5 mode:remove_FC converted:21.03.2016

                Evolutionary Biology
                arctic ecosystem,cold‐temperate,competition,hybridization,intertidal macroalgae,key species,rocky intertidal

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