The Experts below are selected from a list of 1285419 Experts worldwide ranked by ideXlab platform
Kylie L Scales - One of the best experts on this subject based on the ideXlab platform.
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
Niklaus E Zimmermann - One of the best experts on this subject based on the ideXlab platform.
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measuring ecological niche overlap from occurrence and spatial Environmental Data
Global Ecology and Biogeography, 2012Co-Authors: Olivier Broennimann, Matthew C Fitzpatrick, Peter B Pearman, Blaise Petitpierre, Loic Pellissier, Nigel G Yoccoz, Wilfried Thuiller, Mariejosee Fortin, Christophe F Randin, Niklaus E ZimmermannAbstract:Aim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare Environmental niches from occurrence and spatial Environmental Data. Location Europe, North America and South America. Methods The framework applies kernel smoothers to densities of species occurrence in gridded Environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre-defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with Data on two well-studied invasive species. Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of Environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate Environmental space independent of both sampling effort and arbitrary choice of resolution in Environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results. Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra-specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the Environmental niche of a species or intra-specific lineage has changed over time.
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measuring ecological niche overlap from occurrence and spatial Environmental Data
Global Ecology and Biogeography, 2012Co-Authors: Olivier Broennimann, Matthew C Fitzpatrick, Peter B Pearman, Blaise Petitpierre, Loic Pellissier, Nigel G Yoccoz, Wilfried Thuiller, Mariejosee Fortin, Christophe F Randin, Niklaus E ZimmermannAbstract:Aim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare Environmental niches from occurrence and spatial Environmental Data. Location Europe, North America and South America. Methods The framework applies kernel smoothers to densities of species occurrence in gridded Environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre-defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with Data on two well-studied invasive species. Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of Environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate Environmental space independent of both sampling effort and arbitrary choice of resolution in Environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results. Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra-specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the Environmental niche of a species or intra-specific lineage has changed over time.
Steven J Bograd - One of the best experts on this subject based on the ideXlab platform.
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
Michael G Jacox - One of the best experts on this subject based on the ideXlab platform.
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
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scale of inference on the sensitivity of habitat models for wide ranging marine predators to the resolution of Environmental Data
Ecography, 2017Co-Authors: Kylie L Scales, Elliott L Hazen, Michael G Jacox, Christopher A Edwards, Andre M Boustany, Matthew J Oliver, Steven J BogradAbstract:Understanding and predicting the responses of wide-ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat-based models that can sufficiently capture their Environmental preferences. Marine ecosystems are inherently dynamic, and animal-environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of Environmental Data layers are therefore key considerations in modelling the Environmental determinants of habitat selection. The utility of Environmental Data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade-offs between near real-time, high resolution and composite (i.e. synoptic, cloud-free) Data fields. Using movement simulations with built-in Environmental preferences in combination with both modelled and remotely-sensed (ROMS, MODIS-Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3-111 km, daily-climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological Data fields can introduce bias in presence-availability designs based upon animal movement Datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely-sensed Data fields. We found that model accuracy decreased substantially above 70% Data loss. In cloudy regions, weekly or monthly Environmental Data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate-mediated ecosystem changes. Ecography © 2016 Nordic Society Oikos.; ;
Yoshinori Nakazawa - One of the best experts on this subject based on the ideXlab platform.
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Environmental Data sets matter in ecological niche modelling an example with solenopsis invicta and solenopsis richteri
Global Ecology and Biogeography, 2007Co-Authors: A. T. Peterson, Yoshinori NakazawaAbstract:Aim In response to a recent paper suggesting the failure of ecological niche models to predict between native and introduced distributional areas of fire ants (Solenopsis invicta), we sought to assess methodological causes of this failure. Location Ecological niche models were developed on the species’ native distributional area in South America, and projected globally. Methods We developed ecological niche models based on six different Environmental Data sets, and compared their respective abilities to anticipate the North American invasive distributional area of the species. Results We show that models based on the ‘bioclimatic variables’ of the WorldClim Data set indeed fail to predict the full invasive potential of the species, but that models based on four other Data sets could predict this potential correctly. Main conclusions The difference in predictive abilities appears to centre on the complexity of the Environmental variables involved. These results emphasize important influences of Environmental Data sets on the generality and ability of ecological niche models to anticipate novel phenomena, and offer a simpler explanation for the lack of predictive ability among native and invaded distributional areas than that of niche shifts.
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Environmental Data sets matter in ecological niche modelling an example with solenopsis invicta and solenopsis richteri
Global Ecology and Biogeography, 2007Co-Authors: A. T. Peterson, Yoshinori NakazawaAbstract:Aim In response to a recent paper suggesting the failure of ecological niche models to predict between native and introduced distributional areas of fire ants (Solenopsis invicta), we sought to assess methodological causes of this failure. Location Ecological niche models were developed on the species’ native distributional area in South America, and projected globally. Methods We developed ecological niche models based on six different Environmental Data sets, and compared their respective abilities to anticipate the North American invasive distributional area of the species. Results We show that models based on the ‘bioclimatic variables’ of the WorldClim Data set indeed fail to predict the full invasive potential of the species, but that models based on four other Data sets could predict this potential correctly. Main conclusions The difference in predictive abilities appears to centre on the complexity of the Environmental variables involved. These results emphasize important influences of Environmental Data sets on the generality and ability of ecological niche models to anticipate novel phenomena, and offer a simpler explanation for the lack of predictive ability among native and invaded distributional areas than that of niche shifts.