scholarly journals Habitat Suitability Modeling and Mapping to Assess the Influence of Freshwater Withdrawals on Spatial Distributions and Population Numbers of Estuarine Species in the Lower Peace River and Charlotte Harbor, Florida

2021 ◽  
Vol 13 (1) ◽  
pp. 31-58
Author(s):  
Peter J. Rubec ◽  
Christi Santi ◽  
XinJian Chen ◽  
Yonas Ghile
Keyword(s):  
Habitat Suitability ◽  
Spatial Distributions ◽  
Charlotte Harbor ◽  
Habitat Suitability Modeling ◽  
Estuarine Species ◽  
Peace River ◽  
Population Numbers

scholarly journals Modeling and Mapping to Assess Spatial Distributions and Population Numbers of Fish and Invertebrate Species in the Lower Peace River and Charlotte Harbor, Florida

2019 ◽  
Vol 11 (4) ◽  
pp. 328-350 ◽  
Author(s):  
Peter J. Rubec ◽  
Christi Santi ◽  
Yonas Ghile ◽  
Xinjian Chen
Keyword(s):  
Spatial Distributions ◽  
Charlotte Harbor ◽  
Invertebrate Species ◽  
Peace River ◽  
Population Numbers

scholarly journals Prediction of leopard habitat suitability in Taman Negara main forest complex, Malaysia

2021 ◽  
Vol 42 (3(SI)) ◽  
pp. 806-811
Author(s):  
N.F. Khodri ◽  
◽  
T. Lihan ◽  
M.A. Mustapha ◽  
T.M. Taher ◽  
...  
Keyword(s):  
Land Use ◽  
Habitat Suitability ◽  
Wildlife Conservation ◽  
Environmental Parameters ◽  
Geographical Information ◽  
Urban Habitat ◽  
Suitable Habitat ◽  
Habitat Suitability Modeling ◽  
Presence Data ◽  
Gis Data

Aim: This research assessed the distribution of leopard to predict the habitat suitability in Taman Negara National Park and adjacent forest area. Methodology: Environmental factors for habitat suitability were derived from geographical information system (GIS) data such as elevation, slope, land-use, distance from urban and distance from river. Leopard presence data from 1993 to 2008 were integrated with the environmental parameters using maximum entropy (MaxEnt) modeling to assess habitat suitability across the study area. Results: The results showed that distance from river contributed the most (39.3%) in the habitat suitability modeling followed by distance from urban (31.4%), elevation (12.3%), land use types (10.1%), and slope (6.9%). Distance from river and urban showed highest contribution that influenced leopard distribution in which most suitable habitat occurred in proximity with river and further from urban. Habitat suitability of leopard were distributed among 48% over 2,218,389 ha of the study area. Interpretation: The findings of this study provides knowledge on how the species move and exploit different habitat niches for more effective conservation management. It provide models for future wildlife conservation and urban planning.

scholarly journals Habitat suitability modeling for the endangered Hawaiian petrel on Kauai and analysis of predicted habitat overlap with the Newell’s shearwater

2017 ◽  
Vol 12 ◽  
pp. 131-143 ◽  
Author(s):  
Jeff R. Troy ◽  
Nick D. Holmes ◽  
Joseph A. Veech ◽  
André F. Raine ◽  
M. Clay Green
Keyword(s):  
Habitat Suitability ◽  
Habitat Suitability Modeling ◽  
Habitat Overlap

Fish Habitat: Essential Fish Habitat and Rehabilitation

1999 ◽  
Keyword(s):  
Habitat Suitability ◽  
Polynomial Regression ◽  
Environmental Gradients ◽  
Fish Habitat ◽  
Tampa Bay ◽  
Spotted Seatrout ◽  
Charlotte Harbor ◽  
Biologically Relevant ◽  
Suitability Index ◽  
Catch Rates

<em>Abstract.—</em> A need exists to scientifically determine optimal fish habitats to support decision making for management of essential fish habitat. Scientists have been collaborating to conduct habitat suitability index (HSI) modeling to spatially delineate fish habitats for estuarine fish and invertebrate species in Tampa Bay and Charlotte Harbor, Florida. Results from HSI modeling of juvenile spotted seatrout <em>Cynoscion nebulosus </em> in Charlotte Harbor are presented. Data obtained from 1989–1997 by fisheries-independent monitoring in the two estuaries were used along with environmental data from other sources. Standardized catch-per-unit-effort (catch rates) were calculated across gear types using fisheries-monitoring data from Charlotte Harbor and Tampa Bay. Suitability index functions were determined using three methods: (1) frequency of occurrence, (2) mean catch rates within ranges, and (3) smooth-mean catch rates determined by polynomial regression. Mean catch rates were estimated within biologically relevant ranges and, where sufficient data were available, for finer intervals across environmental gradients. Suitability index functions across environmental gradients were then derived by scaling catch rates. Gridded habitat layers for temperature, salinity, depth, and bottom type in Charlotte Harbor were also created using a geographic information system. Habitat suitability index modeling was conducted using the U.S. Fish and Wildlife Service geometric mean method linked to the ArcView Spatial Analyst module. The model integrated suitability indices associated with the habitat layers for Charlotte Harbor to create a map of the predicted distribution for juvenile spotted seatrout during the fall season. Suitability indices developed for Tampa Bay were used with Charlotte Harbor habitat layers to test transfer of the indices to another estuary. Predicted HSI maps depicted low to optimum habitat suitability zones in Charlotte Harbor. Model performance was evaluated by statistically comparing the relative ranking of mean catch rates with mean suitability indices for corresponding zones. Suitability indices obtained using polynomial regression methods yielded morereliable HSI maps for juvenile spotted seatrout than those derived using mean catch rates within biologically relevant ranges. The observed map, derived using smooth-mean suitability indices transferred from Tampa Bay, was not significantly different (Chi-square goodness-of-fit test) from the expected map derived using smooth-mean indices from Charlotte Harbor. Our modeling efforts using transferred indices indicate that it is possible to predict the geographic distributions of fish species by life stage in estuaries lacking fisheries monitoring.

Other Techniques Related to Habitat Analysis

2021 ◽  
pp. 193-208
Author(s):  
Joseph A. Veech
Keyword(s):  
Habitat Suitability ◽  
Environmental Variables ◽  
Resource Selection ◽  
Compositional Data ◽  
Initial Step ◽  
Compositional Data Analysis ◽  
Habitat Suitability Modeling ◽  
Species Response ◽  
Habitat Analysis ◽  
Active Choice

Habitat analysis is strictly defined as a statistical examination to identify the environmental variables that a species associates with, wherein association is taken as some form of correspondence between a species response variable (e.g., presence–absence or abundance) and the environmental variables. There are other statistical techniques and empirical goals that extend this basic framework. These techniques often rely on a habitat analysis having been conducted as an initial step. Resource selection functions quantify an individual’s and a species’ use of a resource based upon the properties of the resource. Resource is broadly defined and can include particular types of habitat. Selectivity and preference indices are used to assess an individual’s preference and active choice of different resource types. Compositional data analysis is a statistical method for examining the composition of an individual’s territory or home range with regard to different habitat types that may be represented. Habitat suitability modeling and species distribution modeling are closely related techniques designed to map the spatial distribution of a species’ habitat and sometimes the species itself based upon its habitat requirements and other factors.

scholarly journals Flow Management to Control Excessive Growth of Macrophytes – An Assessment Based on Habitat Suitability Modeling

2018 ◽  
Vol 9 ◽  
Author(s):  
Konstantin Ochs ◽  
Rui P. Rivaes ◽  
Teresa Ferreira ◽  
Gregory Egger
Keyword(s):  
Habitat Suitability ◽  
Flow Management ◽  
Habitat Suitability Modeling
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