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Landscape Prediction and Mapping of Game Fish Biomass, an Ecosystem Service of Michigan Rivers
dc.contributor.authorEsselman, Peter C.
dc.contributor.authorStevenson, R. Jan
dc.contributor.authorLupi, Frank
dc.contributor.authorRiseng, Catherine M.
dc.contributor.authorWiley, Michael J.
dc.date.accessioned2018-02-05T16:40:47Z
dc.date.available2018-02-05T16:40:47Z
dc.date.issued2015-04
dc.identifier.citationEsselman, Peter C.; Stevenson, R. Jan; Lupi, Frank; Riseng, Catherine M.; Wiley, Michael J. (2015). "Landscape Prediction and Mapping of Game Fish Biomass, an Ecosystem Service of Michigan Rivers." North American Journal of Fisheries Management 35(2): 302-320.
dc.identifier.issn0275-5947
dc.identifier.issn1548-8675
dc.identifier.urihttps://hdl.handle.net/2027.42/141803
dc.description.abstractThe increased integration of ecosystem service concepts into natural resource management places renewed emphasis on prediction and mapping of fish biomass as a major provisioning service of rivers. The goals of this study were to predict and map patterns of fish biomass as a proxy for the availability of catchable fish for anglers in rivers and to identify the strongest landscape constraints on fish productivity. We examined hypotheses about fish responses to total phosphorus (TP), as TP is a growth‐limiting nutrient known to cause increases (subsidy response) and/or decreases (stress response) in fish biomass depending on its concentration and the species being considered. Boosted regression trees were used to define nonlinear functions that predicted the standing crops of Brook Trout Salvelinus fontinalis, Brown Trout Salmo trutta, Smallmouth Bass Micropterus dolomieu, panfishes (seven centrarchid species), and Walleye Sander vitreus by using landscape and modeled local‐scale predictors. Fitted models were highly significant and explained 22–56% of the variation in validation data sets. Nonlinear and threshold responses were apparent for numerous predictors, including TP concentration, which had significant effects on all except the Walleye fishery. Brook Trout and Smallmouth Bass exhibited both subsidy and stress responses, panfish biomass exhibited a subsidy response only, and Brown Trout exhibited a stress response. Maps of reach‐specific standing crop predictions showed patterns of predicted fish biomass that corresponded to spatial patterns in catchment area, water temperature, land cover, and nutrient availability. Maps illustrated predictions of higher trout biomass in coldwater streams draining glacial till in northern Michigan, higher Smallmouth Bass and panfish biomasses in warmwater systems of southern Michigan, and high Walleye biomass in large main‐stem rivers throughout the state. Our results allow fisheries managers to examine the biomass potential of streams, describe geographic patterns of fisheries, explore possible nutrient management targets, and identify habitats that are candidates for species management.Received May 20, 2014; accepted November 6, 2014
dc.publisherTaylor & Francis
dc.publisherWiley Periodicals, Inc.
dc.titleLandscape Prediction and Mapping of Game Fish Biomass, an Ecosystem Service of Michigan Rivers
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelNatural Resources and Environment
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/141803/1/nafm0302-sup-0001.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/141803/2/nafm0302.pdf
dc.identifier.doi10.1080/02755947.2014.987887
dc.identifier.sourceNorth American Journal of Fisheries Management
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