Paul J Doherty - Academia.edu (original) (raw)
Papers by Paul J Doherty
Applied Geography, Feb 1, 2014
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders' ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity. This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
I would express my appreciation and gratitude to all involved with this research initiative and w... more I would express my appreciation and gratitude to all involved with this research initiative and will attempt to highlight some of those people here. First and foremost I would like to thank Yosemite Search and Rescue and all of the volunteers who risk their lives in service so that others may live.
International Journal of Geographical Information Science, Mar 25, 2014
ABSTRACT In order to address a spatio-temporal challenge such as incident prevention, we need inf... more ABSTRACT In order to address a spatio-temporal challenge such as incident prevention, we need information about the time and place where previous incidents have occurred in the past. Using geographic coordinates of incidents that occurred in the past in coincidence with spatial layers corresponding to environmental variables, we can produce probability maps in geographic and temporal space. Here we evaluate spatial statistic and machine learning approaches to answer an important space-time question: where and when are wildland search and rescue (WiSAR) incidents most likely to occur within Yosemite National Park in the future? We produced a probability map for the year 2011based on the presence and background learning algorithm (PBL) that successfully forecasts the most likely areas of future WiSAR incident occurrence based on environmental variables (distance to anthropogenic & natural features, vegetation, elevation, and slope) and the overlap with historic incidents from 2001-2010. This will allow decision-makers to spatially allocate resources where and when incidents are most likely to occur. In the process we not only answered questions related to a real-world problem, we also used novel space-time analyses that gives us insight into machine learning principles. The GIScience findings from this applied research have major implications for best-practices in future space-time research in the fields of epidemiology and ecological niche modelling.
ABSTRACTIn the absence of effective treatments, social distancing has been the only public health... more ABSTRACTIn the absence of effective treatments, social distancing has been the only public health measure available to combat the COVID-19 pandemic. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users’ location data. As part of the COVID-19 Data Mobility Network, we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials.All code is open source and freely available at https://github.com/ryanlayer/COvid19
Park Ranger & GIS Specialist, National Park Service PhD student, University of California, Merced... more Park Ranger & GIS Specialist, National Park Service PhD student, University of California, Merced http://www.esri.com/news/arcuser/0609/yosar.html Platinum Sponsors * KU Department of Geography * Coca-Cola Gold Sponsors * KU Institute for Policy & Social Research * State of Kansas Data Access and Support Center (DASC) * KU Libraries GIS and Data Services * Wilson & Company Engineers and Architects Silver Sponsors * ASPRS - Central Region * Bartlett & West * C-CHANGE Program (NSF IGERT) * Garmin * Kansas Applied Remote Sensing Program * KansasView * KU Transportation Research Institute * KU Biodiversity Institute Bronze Sponsors * KU Center for Remote Sensing of Ice Sheets (CReSIS) * KU Center for Global & International Studies * KU Environmental Studies Program
Applied Geography, 2014
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders' ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity. This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders’ ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical
geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity.
This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
Transactions in GIS, Dec 1, 2011
The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness pr... more The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness presents a unique and worthwhile spatiotemporal challenge to investigate. Once incidents are georeferenced they can be spatially queried and analyzed. However, one major challenge for evaluating SAR in a spatial context is the lack of explicitly spatial data (addresses or coordinates) for historic incidents; they must be georeferenced from textual descriptions. This study implemented two established approaches for georeferencing incidents, the 'Point-Radius' and 'Shape' methods. Incorporating uncertainty measurements into a spatial database allows for more appropriate analyses of spatial dependence and the spatial distribution of incidents. From 2005-2010, 1,271 of 1,356 Yosemite Search and Rescue YOSAR incidents (93.7%) could be georeferenced using the Point-Radius Method, with a mean uncertainty radius = 560 Ϯ 51 m and mean uncertainty area of 3.60 Ϯ 0.840 km 2 . However, when the Shape Method was applied to six case studies by considering the reference object shape, the uncertainty areas were reduced considerably (by up to 99.5% of the uncertain area generated by the Point-Radius Method). This is the first spatially-explicit study of SAR incidents and yields valuable insights into the role of georeferenced data in emergency preparedness.
Transactions in …, 2011
The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness pr... more The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness presents a unique and worthwhile spatiotemporal challenge to investigate. Once incidents are georeferenced they can be spatially queried and analyzed. However, one major challenge for evaluating SAR in a spatial context is the lack of explicitly spatial data (addresses or coordinates) for historic incidents; they must be georeferenced from textual descriptions. This study implemented two established approaches for georeferencing incidents, the 'Point-Radius' and 'Shape' methods. Incorporating uncertainty measurements into a spatial database allows for more appropriate analyses of spatial dependence and the spatial distribution of incidents. From 2005-2010, 1,271 of 1,356 Yosemite Search and Rescue YOSAR incidents (93.7%) could be georeferenced using the Point-Radius Method, with a mean uncertainty radius = 560 Ϯ 51 m and mean uncertainty area of 3.60 Ϯ 0.840 km 2 . However, when the Shape Method was applied to six case studies by considering the reference object shape, the uncertainty areas were reduced considerably (by up to 99.5% of the uncertain area generated by the Point-Radius Method). This is the first spatially-explicit study of SAR incidents and yields valuable insights into the role of georeferenced data in emergency preparedness.
Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires... more Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires suitable areas that are flat, devoid of tree canopy, and not within close proximity to other hazards. The objective of this study was to identify helicopter landing areas that are most likely to exist based on available geographic data using two GIScience methods. The first approach produced an expert model that was derived from predefined feature constraints based on existing knowledge of helicopter landing area requirements (weighted overlay algorithm). The second model is derived using a machine learning technique (maximum entropy algorithm, Maxent) that derives feature constraints from existing presence-only points; that is, geographic one-class data. Both models yielded similar output and successfully classified test coordinates, but Maxent was more efficient and required no user-defined weighting that is typically subject to human bias or disagreement. The pros and cons of each approach are discussed and the comparison reveals important considerations for a variety of future land suitability studies, including ecological niche modeling. The conclusion is that the two approaches complement each other. Overall, we produced an effective geographic information system product to support the identification of suitable landing areas in emergent rescue situations. To our knowledge, this is the first GIScience study focused on estimating the location of landing zones for a search-and-rescue application.
Low hatching success may limit progress towards reaching productivity goals for Atlantic Coast pi... more Low hatching success may limit progress towards reaching productivity goals for Atlantic Coast piping plover (Charadrius melodus) recovery, despite management strategies to protect eggs from predators and decrease human disturbance of birds on nests. We measured piping plover hatching success on Eastern Long Island beaches and identified the major causes of egg failure to better understand why eggs that were otherwise intact (not depredated or destroyed by tidal flooding) failed to hatch. We documented egg and nest fates, dissected contents of unhatched eggs to determine viability, and recorded human and predator activity near a subset of plover nests on Suffolk County Parks properties. The low hatching success we recorded (0.60) in 2006 and 2007 would require higher chick survival rates than are typically observed for piping plovers to meet recovery targets for productivity. Few eggs showed signs of poor viability and overall egg hatchability was comparable to other ground nesting birds. Most egg failure was due to either depredation at unexclosed nests or nest abandonment by adults. The best predictor of nest abandonment was the maximum number of red fox tracks (Vulpes vulpes) counted on nearby transects (β = −1.16, 95% CI: −2.0 to −0.3) and we found evidence that plovers abandoned eggs in response to predation risk (e.g., a fox circling a nest exclosure). Adults from abandoned nests may have deserted eggs or been depredated. In either case, intact and viable eggs were abandoned. Nest abandonment was not related to human activity near nests, which were buffered from human disturbance by symbolic string fencing. Our results suggest that depredation and nest abandonment (e.g., desertion or death of adults) due to predator disturbance, not human disturbance or poor egg viability, contributed to the low hatching success we recorded. Active predator removal in addition to modification of predator exclosure use and design may be necessary to prevent direct (egg depredation) and indirect (nest abandonment) negative effects of predators on hatching success.
Climate models may be limited in their inferential use if they cannot be locally validated or do ... more Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.
Talks by Paul J Doherty
Here is a remote lecture I gave for a diverse audience of K-12 students, University students, GIS... more Here is a remote lecture I gave for a diverse audience of K-12 students, University students, GIS practitioners, and the general public on GIS Day 2014.
You can watch my Dissertation Defense on YouTube (See link).
Applied Geography, Feb 1, 2014
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders' ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity. This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
I would express my appreciation and gratitude to all involved with this research initiative and w... more I would express my appreciation and gratitude to all involved with this research initiative and will attempt to highlight some of those people here. First and foremost I would like to thank Yosemite Search and Rescue and all of the volunteers who risk their lives in service so that others may live.
International Journal of Geographical Information Science, Mar 25, 2014
ABSTRACT In order to address a spatio-temporal challenge such as incident prevention, we need inf... more ABSTRACT In order to address a spatio-temporal challenge such as incident prevention, we need information about the time and place where previous incidents have occurred in the past. Using geographic coordinates of incidents that occurred in the past in coincidence with spatial layers corresponding to environmental variables, we can produce probability maps in geographic and temporal space. Here we evaluate spatial statistic and machine learning approaches to answer an important space-time question: where and when are wildland search and rescue (WiSAR) incidents most likely to occur within Yosemite National Park in the future? We produced a probability map for the year 2011based on the presence and background learning algorithm (PBL) that successfully forecasts the most likely areas of future WiSAR incident occurrence based on environmental variables (distance to anthropogenic & natural features, vegetation, elevation, and slope) and the overlap with historic incidents from 2001-2010. This will allow decision-makers to spatially allocate resources where and when incidents are most likely to occur. In the process we not only answered questions related to a real-world problem, we also used novel space-time analyses that gives us insight into machine learning principles. The GIScience findings from this applied research have major implications for best-practices in future space-time research in the fields of epidemiology and ecological niche modelling.
ABSTRACTIn the absence of effective treatments, social distancing has been the only public health... more ABSTRACTIn the absence of effective treatments, social distancing has been the only public health measure available to combat the COVID-19 pandemic. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users’ location data. As part of the COVID-19 Data Mobility Network, we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials.All code is open source and freely available at https://github.com/ryanlayer/COvid19
Park Ranger & GIS Specialist, National Park Service PhD student, University of California, Merced... more Park Ranger & GIS Specialist, National Park Service PhD student, University of California, Merced http://www.esri.com/news/arcuser/0609/yosar.html Platinum Sponsors * KU Department of Geography * Coca-Cola Gold Sponsors * KU Institute for Policy & Social Research * State of Kansas Data Access and Support Center (DASC) * KU Libraries GIS and Data Services * Wilson & Company Engineers and Architects Silver Sponsors * ASPRS - Central Region * Bartlett & West * C-CHANGE Program (NSF IGERT) * Garmin * Kansas Applied Remote Sensing Program * KansasView * KU Transportation Research Institute * KU Biodiversity Institute Bronze Sponsors * KU Center for Remote Sensing of Ice Sheets (CReSIS) * KU Center for Global & International Studies * KU Environmental Studies Program
Applied Geography, 2014
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders' ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity. This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as ... more Study of wilderness search and rescue (WiSAR) incidents suggests a dependency on demographics as well as physical geography in relation to decisions made before/after becoming lost and subsequent locations in which subjects are found. Thus an understanding of the complex relationship between demographics and physical geography could enhance the responders’ ability to locate the subject in a timely manner. Various global datasets have been organized to provide general distance and feature based geostatistical methods for describing this relationship. However, there is some question as to the applicability of these generalized datasets to local incidents that are dominated by a specific physical
geography. This study consists of two primary objectives related to the allocation of geographic probability intended to manage the overall size of the search area. The first objective considers the applicability of a global dataset of lost person incidents to a localized environment with limited geographic diversity.
This is followed by a comparison between a commonly used Euclidean distance statistic and an alternative travel-cost model that accounts for the influence of anthropogenic and landscape features on subject mobility and travel time. In both instances, lost person incident data from years 2000 to 2010 for Yosemite National Park is used and compared to a large pool of internationally compiled cases consisting of similar subject profiles.
Transactions in GIS, Dec 1, 2011
The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness pr... more The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness presents a unique and worthwhile spatiotemporal challenge to investigate. Once incidents are georeferenced they can be spatially queried and analyzed. However, one major challenge for evaluating SAR in a spatial context is the lack of explicitly spatial data (addresses or coordinates) for historic incidents; they must be georeferenced from textual descriptions. This study implemented two established approaches for georeferencing incidents, the 'Point-Radius' and 'Shape' methods. Incorporating uncertainty measurements into a spatial database allows for more appropriate analyses of spatial dependence and the spatial distribution of incidents. From 2005-2010, 1,271 of 1,356 Yosemite Search and Rescue YOSAR incidents (93.7%) could be georeferenced using the Point-Radius Method, with a mean uncertainty radius = 560 Ϯ 51 m and mean uncertainty area of 3.60 Ϯ 0.840 km 2 . However, when the Shape Method was applied to six case studies by considering the reference object shape, the uncertainty areas were reduced considerably (by up to 99.5% of the uncertain area generated by the Point-Radius Method). This is the first spatially-explicit study of SAR incidents and yields valuable insights into the role of georeferenced data in emergency preparedness.
Transactions in …, 2011
The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness pr... more The Search and Rescue (SAR) of individuals who become lost, injured, or stranded in wilderness presents a unique and worthwhile spatiotemporal challenge to investigate. Once incidents are georeferenced they can be spatially queried and analyzed. However, one major challenge for evaluating SAR in a spatial context is the lack of explicitly spatial data (addresses or coordinates) for historic incidents; they must be georeferenced from textual descriptions. This study implemented two established approaches for georeferencing incidents, the 'Point-Radius' and 'Shape' methods. Incorporating uncertainty measurements into a spatial database allows for more appropriate analyses of spatial dependence and the spatial distribution of incidents. From 2005-2010, 1,271 of 1,356 Yosemite Search and Rescue YOSAR incidents (93.7%) could be georeferenced using the Point-Radius Method, with a mean uncertainty radius = 560 Ϯ 51 m and mean uncertainty area of 3.60 Ϯ 0.840 km 2 . However, when the Shape Method was applied to six case studies by considering the reference object shape, the uncertainty areas were reduced considerably (by up to 99.5% of the uncertain area generated by the Point-Radius Method). This is the first spatially-explicit study of SAR incidents and yields valuable insights into the role of georeferenced data in emergency preparedness.
Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires... more Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires suitable areas that are flat, devoid of tree canopy, and not within close proximity to other hazards. The objective of this study was to identify helicopter landing areas that are most likely to exist based on available geographic data using two GIScience methods. The first approach produced an expert model that was derived from predefined feature constraints based on existing knowledge of helicopter landing area requirements (weighted overlay algorithm). The second model is derived using a machine learning technique (maximum entropy algorithm, Maxent) that derives feature constraints from existing presence-only points; that is, geographic one-class data. Both models yielded similar output and successfully classified test coordinates, but Maxent was more efficient and required no user-defined weighting that is typically subject to human bias or disagreement. The pros and cons of each approach are discussed and the comparison reveals important considerations for a variety of future land suitability studies, including ecological niche modeling. The conclusion is that the two approaches complement each other. Overall, we produced an effective geographic information system product to support the identification of suitable landing areas in emergent rescue situations. To our knowledge, this is the first GIScience study focused on estimating the location of landing zones for a search-and-rescue application.
Low hatching success may limit progress towards reaching productivity goals for Atlantic Coast pi... more Low hatching success may limit progress towards reaching productivity goals for Atlantic Coast piping plover (Charadrius melodus) recovery, despite management strategies to protect eggs from predators and decrease human disturbance of birds on nests. We measured piping plover hatching success on Eastern Long Island beaches and identified the major causes of egg failure to better understand why eggs that were otherwise intact (not depredated or destroyed by tidal flooding) failed to hatch. We documented egg and nest fates, dissected contents of unhatched eggs to determine viability, and recorded human and predator activity near a subset of plover nests on Suffolk County Parks properties. The low hatching success we recorded (0.60) in 2006 and 2007 would require higher chick survival rates than are typically observed for piping plovers to meet recovery targets for productivity. Few eggs showed signs of poor viability and overall egg hatchability was comparable to other ground nesting birds. Most egg failure was due to either depredation at unexclosed nests or nest abandonment by adults. The best predictor of nest abandonment was the maximum number of red fox tracks (Vulpes vulpes) counted on nearby transects (β = −1.16, 95% CI: −2.0 to −0.3) and we found evidence that plovers abandoned eggs in response to predation risk (e.g., a fox circling a nest exclosure). Adults from abandoned nests may have deserted eggs or been depredated. In either case, intact and viable eggs were abandoned. Nest abandonment was not related to human activity near nests, which were buffered from human disturbance by symbolic string fencing. Our results suggest that depredation and nest abandonment (e.g., desertion or death of adults) due to predator disturbance, not human disturbance or poor egg viability, contributed to the low hatching success we recorded. Active predator removal in addition to modification of predator exclosure use and design may be necessary to prevent direct (egg depredation) and indirect (nest abandonment) negative effects of predators on hatching success.
Climate models may be limited in their inferential use if they cannot be locally validated or do ... more Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.
Here is a remote lecture I gave for a diverse audience of K-12 students, University students, GIS... more Here is a remote lecture I gave for a diverse audience of K-12 students, University students, GIS practitioners, and the general public on GIS Day 2014.
You can watch my Dissertation Defense on YouTube (See link).