Creating a distribution model of three crayfish species of the genus Faxonius (Decapoda, Cambaridae) in Michigan streams using publicly accessible data

IF 0.6 4区 生物学 Q4 MARINE & FRESHWATER BIOLOGY Crustaceana Pub Date : 2023-08-15 DOI:10.1163/15685403-bja10311
Robert C. Homan, K. Baker, B. Roth, Kelley R. Smith
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Abstract

This research seeks to create a predictive model of habitat suitability for use in determining waterbodies vulnerable to introduced species within the state of Michigan. Three members of the genus Faxonius (Decapoda, Cambaridae) were selected as test taxa for the model due to several species’ propensity for significantly altering the ecosystems they inhabit in Michigan and elsewhere. Michigan State University and the Michigan Department of Natural Resources (MDNR) conducted extensive field surveys of crayfish species assemblages across 461 stream sites from 2014-2016. This project compares these field data to data from publicly available national datasets with the purpose of revealing ecosystems that are vulnerable to population expansion. We identify patterns in Faxonius habitat at local (100 acres) and landscape (1000 acres) scales by associating crayfish occurrences throughout Michigan with variables characterizing landscape conditions thought to be important factors affecting their spread, growth, and survival. An Artificial Neural Network (ANN) model using variables from Soil Survey Geographic Database (SSURGO) and National Land Cover Database (NLCD) successfully identified stream sites and watersheds in Michigan vulnerable to range expansion by Faxonius rusticus (Girard, 1852), Faxonius propinquus (Girard, 1852), and/or Faxonius virilis (Hagen, 1870). We found several habitat variables that influence our predictions. The most important variable describing F. rusticus presence was local (100-acre) scale Open Water land cover class, whereas for F. propinquus, the high-intensity developed land cover class at the local scale was the most important, while it was the shrubland land cover class at the local scale for F. virilis. This research demonstrates a powerful method to identify locations using remote sensing data that can be prioritized for conservation efforts that are threatened by invasive crayfish species.
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使用公开的数据创建密歇根溪流中三种小龙虾属(十足目,小龙虾科)的分布模型
这项研究旨在创建一个栖息地适宜性的预测模型,用于确定密歇根州易受引入物种影响的水体。由于几个物种倾向于显著改变它们在密歇根州和其他地方栖息的生态系统,Faxonius属的三个成员(十足目,Cambaridae)被选为该模型的测试分类群。2014-2016年,密歇根州立大学和密歇根自然资源部对461个溪流地点的小龙虾物种组合进行了广泛的实地调查。该项目将这些实地数据与公开的国家数据集的数据进行比较,目的是揭示易受人口扩张影响的生态系统。我们通过将密歇根州各地小龙虾的出现与表征景观条件的变量联系起来,确定了当地(100英亩)和景观(1000英亩)范围内小龙虾栖息地的模式,景观条件被认为是影响其传播、生长和生存的重要因素。使用土壤调查地理数据库(SSURGO)和国家土地覆盖数据库(NLCD)中的变量建立的人工神经网络(ANN)模型成功地确定了密歇根州易受沙氏乳杆菌(Girard,1852)、拟尖乳杆菌(吉拉德,1852。我们发现了几个影响我们预测的栖息地变量。描述F.rusticus存在的最重要变量是当地(100英亩)规模的开放水域土地覆盖类别,而对于F.propincus来说,当地规模的高强度发达土地覆盖类别是最重要的,而对于F.virilis来说,这是当地规模的灌木土地覆盖类别。这项研究展示了一种利用遥感数据识别位置的强大方法,可以优先考虑受到入侵小龙虾物种威胁的保护工作。
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来源期刊
Crustaceana
Crustaceana 生物-海洋与淡水生物学
CiteScore
1.30
自引率
33.30%
发文量
52
审稿时长
3 months
期刊介绍: Crustaceana is a leading journal in the world on crustacean research, including the latest papers from all branches of zoology. It provides up-to-date information on aspects such as taxonomy, zoogeography, ecology, physiology, anatomy, genetics, palaeontology, and biometry, and covers all groups of Crustacea. Boasting a large international circulation, Crustaceana provides its readers with an abstract for each article.
期刊最新文献
Pinnotheres onychodactylus Tesch, 1918: a junior synonym of Magnotheres Globosus (Hombron & Jacquinot, 1846) (Decapoda, Brachyura, Pinnotheridae) Survival and moulting rates of larvae of Callinectes sapidus Rathbun, 1896 under different environmental circumstances (Brachyura, Portunidae) The effects of using different stocking volumes on the development of broodstocks of the blue crab, Callinectes sapidus Rathbun, 1896 (Brachyura, Portunidae) First record of Monstrilla Leucopis G.O. Sars, 1921 (Copepoda, Monstrilloida, Monstrillidae) from the eastern Pacific On the identity of Speocarcinus celebensis Tesch, 1918 (Decapoda, Brachyura, Pilumnidae) from Sulawesi
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