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| 03716nam 2200397zi 4500 |
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| 001 | 9.919330 |
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| 003 | CaOODSP |
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| 005 | 20230127134309 |
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| 006 | m o d f |
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| 007 | cr mn||||||||| |
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| 008 | 230120t20232023oncab ob f000 0 eng d |
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| 020 | |a9780660471990 |
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| 040 | |aCaOODSP|beng|erda|cCaOODSP |
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| 041 | 0 |aeng|beng|bfre |
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| 043 | |an-cn-on |
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| 086 | 1 |aFs97-4/3259E-PDF |
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| 100 | 1 |aReid, Scott, |d1971- |eauthor. |
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| 245 | 10|aPredicting mussel species at risk distributions in southwestern Ontario rivers using spatial distribution models and the Aquatic Ecosystem Classification method / |cby Scott M. Reid, Allan H.M. Bell, Anita LeBaron, Bastian J. Schmidt, and Nicholas E. Jones. |
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| 264 | 1|aBurlington, ON : |bOntario and Prairie Region, Fisheries and Oceans Canada, |c2023. |
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| 264 | 4|c©2023 |
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| 300 | |a1 online resource (vii, 26 pages) : |billustrations (chiefly colour), maps (chiefly colour). |
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| 336 | |atext|btxt|2rdacontent |
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| 337 | |acomputer|bc|2rdamedia |
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| 338 | |aonline resource|bcr|2rdacarrier |
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| 490 | 1 |aCanadian manuscript report of fisheries and aquatic sciences, |x1488-5387 ; |v3259 |
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| 504 | |aIncludes bibliographical references (pages 7-10). |
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| 520 | 3 |a"By identifying relationships with abiotic and biotic factors, output from species distribution models can help to identify the boundaries of aquatic species at risk critical habitat, direct inventories, and define the spatial units for long-term population monitoring. In this study, we tested whether SDMs can be developed from existing southern Ontario occurrence data for five mussel species at risk using MaxEnt software, a program for modelling species distributions with presence-only species records. Models were built using species presence and abiotic attribute data for the Ausable, Bayfield, Grand, Thames, and Sydenham rivers. Abiotic attributes included: channel slope, riparian and catchment forest cover, summer water temperature, surficial geology, and upstream catchment area. Attributes were based on the provincial Aquatic Ecosystem Classification (AEC) scheme. Strongly supported distribution models were developed for all five mussel species, with 2 to 4 influential predictor variables being identified for each species. Predictors identified consistently across species as influencing habitat suitability were summer water temperature and upstream contributing area. Other informative variables (i.e., geology and tree cover) were only identified for more widespread species (e.g., Wavy-rayed Lampmussel). The number of informative predictor variables for rarer species (e.g., Fawnsfoot) may be limited by the small number of species records, which could be addressed through future inventories. Incorporating the influence of anthropogenic stressors and host fish availability would also improve MaxEnt models but does require the compilation of additional databases"--Abstract, page vi. |
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| 546 | |aIncludes abstracts in English and French. |
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| 650 | 0|aUnionidae|zOntario, Southwestern|xGeographical distribution. |
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| 650 | 0|aRare mollusks|zOntario, Southwestern|xGeographical distribution. |
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| 650 | 6|aUnionidés|zOntario (Sud-Ouest)|xDistribution géographique. |
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| 650 | 6|aMollusques rares|zOntario (Sud-Ouest)|xDistribution géographique. |
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| 710 | 1 |aCanada. |bDepartment of Fisheries and Oceans, |eissuing body. |
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| 710 | 1 |aCanada. |bDepartment of Fisheries and Oceans. |bOntario and Prairie Region, |eissuing body. |
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| 830 | #0|aCanadian manuscript report of fisheries and aquatic sciences ;|v3259.|w(CaOODSP)9.505211 |
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| 856 | 40|qPDF|s2.10 MB|uhttps://publications.gc.ca/collections/collection_2023/mpo-dfo/Fs97-4-3259-eng.pdf |
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