2008
DOI: 10.1525/cond.2008.110.1.177
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A Classification-Tree Analysis of Nesting Habitat in an Island Population of Northern Harriers

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Cited by 5 publications
(3 citation statements)
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“…The results of the study relate to other studies where landscape structure, represented by various metrics, had an effect on species' selection of the breeding habitat (Barbaro & Van Halder, ; Berry, Bock, & Haire, ; Bomhard, ; Jokimäki & Solonen, ; Jones, ; Massey, Bowen, Griffin, & McGarigal, ; Wiens, Chr, Horne, & Ims, ). Differences in the total area of the presumed home ranges of all magpie nests were likely caused by the arbitrarily chosen borders of the study area.…”
Section: Discussion and Outlooksupporting
confidence: 73%
“…The results of the study relate to other studies where landscape structure, represented by various metrics, had an effect on species' selection of the breeding habitat (Barbaro & Van Halder, ; Berry, Bock, & Haire, ; Bomhard, ; Jokimäki & Solonen, ; Jones, ; Massey, Bowen, Griffin, & McGarigal, ; Wiens, Chr, Horne, & Ims, ). Differences in the total area of the presumed home ranges of all magpie nests were likely caused by the arbitrarily chosen borders of the study area.…”
Section: Discussion and Outlooksupporting
confidence: 73%
“…Different modeling approaches exhibit distinct advantages and limitations (Li & Wang, 2013; Thuiller et al., 2009), whereas ensemble species distribution models (ESDMs) amalgamate the strengths of diverse models and thereby enhance the robustness of predictive outcomes. We used an ESDM that included a generalized linear model (GLM) (Brito et al., 1999), generalized additive model (GAM) (Li et al., 2017b), generalized boosted regressions model (Friedman et al., 2000), classification tree analysis (CTA) (Massey et al., 2008), random forest (RF) (Breiman, 2001), and maximum entropy model (MaxEnt) (Ancillotto et al., 2019).…”
Section: Methodsmentioning
confidence: 99%
“…Different modeling approaches exhibit distinct advantages and limitations [27][28][29], while ensemble species distribution models (ESDMs) offer the capability to amalgamate the strengths and drawbacks of diverse models, thereby enhancing the robustness of predictive outcomes. Within our study, our ensemble model encompasses a spectrum of models, namely generalized linear model (GLM) [30], generalized additive model (GAM) [31], generalized boosted regressions model (GBM) [32], classification tree analysis (CTA) [33], random forest (RF) [34] and maximum entropy model (MaxEnt) [35].…”
Section: Data Analysis 231 Ensemble Species Distribution Modelsmentioning
confidence: 99%