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      Spatial models to account for variation in observer effort in bird atlases

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          Abstract

          To assess the importance of variation in observer effort between and within bird atlas projects and demonstrate the use of relatively simple conditional autoregressive ( CAR) models for analyzing grid‐based atlas data with varying effort. Pennsylvania and West Virginia, United States of America. We used varying proportions of randomly selected training data to assess whether variations in observer effort can be accounted for using CAR models and whether such models would still be useful for atlases with incomplete data. We then evaluated whether the application of these models influenced our assessment of distribution change between two atlas projects separated by twenty years (Pennsylvania), and tested our modeling methodology on a state bird atlas with incomplete coverage (West Virginia). Conditional Autoregressive models which included observer effort and landscape covariates were able to make robust predictions of species distributions in cases of sparse data coverage. Further, we found that CAR models without landscape covariates performed favorably. These models also account for variation in observer effort between atlas projects and can have a profound effect on the overall assessment of distribution change. Accounting for variation in observer effort in atlas projects is critically important. CAR models provide a useful modeling framework for accounting for variation in observer effort in bird atlas data because they are relatively simple to apply, and quick to run.

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          Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data

          Boakes et al. compile and analyze a historical dataset of 170,000 bird sightings over two centuries and show how changing trends in data gathering may confound a true picture of biodiversity change.
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            The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

            Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.
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              Tradeoffs of different types of species occurrence data for use in systematic conservation planning.

              Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.
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                Author and article information

                Contributors
                awilson@gettysburg.edu
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                18 July 2017
                August 2017
                : 7
                : 16 ( doiID: 10.1002/ece3.2017.7.issue-16 )
                : 6582-6594
                Affiliations
                [ 1 ] Environmental Studies Department Gettysburg College Gettysburg PA USA
                [ 2 ] Wildlife Management Bureau Pennsylvania Game Commission Harrisburg PA USA
                [ 3 ] Conservation Management Institute Virginia Tech Blacksburg VA USA
                [ 4 ] National Aviary Allegheny Commons West Pittsburgh PA USA
                Author notes
                [*] [* ] Correspondence

                Andrew M. Wilson, Environmental Studies Department, Gettysburg College, Gettysburg, PA, USA.

                Email: awilson@ 123456gettysburg.edu

                Author information
                http://orcid.org/0000-0001-8435-4516
                Article
                ECE33201
                10.1002/ece3.3201
                5574789
                7bca568c-e27b-4bf0-99f9-f253238c50dc
                © 2017 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
                : 02 December 2016
                : 25 May 2017
                : 30 May 2017
                Page count
                Figures: 9, Tables: 3, Pages: 13, Words: 8848
                Funding
                Funded by: U.S. Fish and Wildlife Service, Wildlife and Sport Fish Restoration Program
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece33201
                August 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.8 mode:remove_FC converted:29.08.2017

                Evolutionary Biology
                bird atlas,conditional autoregressive,observer effort,pennsylvania,spatial model,west virginia

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