2017
DOI: 10.1007/s10530-017-1492-3
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Geostatistical distribution modelling of two invasive crayfish across dendritic stream networks

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Cited by 26 publications
(56 citation statements)
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“…) or next‐generation species distribution models (Filipe et al. ; Ver Hoef et al. ) to further reduce remaining uncertainties and provide decision makers with ever more precise information.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…) or next‐generation species distribution models (Filipe et al. ; Ver Hoef et al. ) to further reduce remaining uncertainties and provide decision makers with ever more precise information.…”
Section: Discussionmentioning
confidence: 99%
“…Scenarios developed from the database provide spatially comprehensive thermal information that has contributed to an improved understanding of salmon migrations (Westley et al 2015;Palmer 2017), locations susceptible to nonnative species invasions (Al-Chokhachy et al 2016;Isaak et al 2016aIsaak et al , 2016bHowell, in press), and parameterization of distribution models that predict locations of climate refuge streams (Isaak et al 2015;Young et al 2016). As the NorWeST database and scenarios are periodically updated and improved, they could be used in development of spatially explicit, high-resolution behavioral and bioenergetic models that encompass the full suite of freshwater salmon habitats and life stages (Honea et al 2016;Crozier et al 2017) or next-generation species distribution models (Filipe et al 2017;Ver Hoef et al 2018) to further reduce remaining uncertainties and provide decision makers with ever more precise information.…”
Section: Adaptive Responsesmentioning
confidence: 99%
“…Moreover, SSNs accommodate covariates to describe relationships with response variables and can be implemented with classical geostatistical kriging techniques (Cressie, ) to make predictions throughout river networks with spatially explicit uncertainty estimates (Isaak et al, ; Ver Hoef et al, ). Like other spatial statistical models (Beale et al, ; Temesgen & Ver Hoef, ; Ver Hoef, ), SSNs also consistently improve predictive performance relative to nonspatial models when applied to correlated data sets (Brennan et al, ; Filipe et al, ; Isaak et al, ; Turschwell et al, ).…”
Section: Introductionmentioning
confidence: 92%
“…In the context of the early stages of the invasion process, species distribution models (SDMs) can identify areas that provide suitable environmental conditions for the establishment of invasive crayfish. This approach has been used at scales ranging from local assessments (e.g., river basins) to national [34][35][36][37], transnational [38][39][40][41] or even worldwide [24,42]. Additionally, model projections can be produced under future scenarios of climate change, analysing possible shifts in areas suitable for the invaders or for the species that these can affect [38,40].…”
Section: Modelsmentioning
confidence: 99%