Publications
2017 |
McAlister, Mark A; Moorman, Christopher E; Meentemeyer, Ross K; Fuller, Joseph C; Howell, Douglas L; DePerno, Christopher S Using Landscape Characteristics to Predict Distribution of Temperate-Breeding Canada Geese Journal Article Southeastern Naturalist, 16 (2), pp. 127-139, 2017. Abstract | Links | BibTeX | Tags: land cover, species distribution model @article{McAlister2017, title = {Using Landscape Characteristics to Predict Distribution of Temperate-Breeding Canada Geese}, author = {Mark A. McAlister and Christopher E. Moorman and Ross K. Meentemeyer and Joseph C. Fuller and Douglas L. Howell and Christopher S. DePerno}, url = {https://doi.org/10.1656/058.016.0201}, doi = {10.1656/058.016.0201}, year = {2017}, date = {2017-06-01}, journal = {Southeastern Naturalist}, volume = {16}, number = {2}, pages = {127-139}, abstract = {Accurate estimates of species' distributions are needed to ensure that conservation-planning efforts are directed at appropriate areas. Since the early 1980s, temperate-breeding populations of Branta canadensis (Canada Goose) have increased, yet reliable estimates of the species' distribution are lacking in many regions. Our objective was to identify the landcover features that best predicted Canada Goose distribution. In April 2015, we surveyed 300 one-km2 plots across North Carolina and observed 449 Canada Geese. We quantified percent coverage of 7 continuous landcover variables at 5 different spatial extents for each of the 300 plots. We fit logistic regression models using presence and absence at the 300 plots as the dependent variable and percent-cover covariates as independent variables. The best model for predicting Canada Goose presence included percent pasture within the 9 km2 surrounding the survey plot and percent open water within the 1-km2 survey plot. The probability of Canada Goose presence increased with increasing percent open water and percent pasture, albeit at different spatial extents, which provided important cover and food resources, respectively. Our approach using remote-sensing data to accurately predict Canada Goose presence across a large spatial extent can be employed to determine distributions for other easily surveyed, widely distributed species.}, keywords = {land cover, species distribution model}, pubstate = {published}, tppubtype = {article} } Accurate estimates of species' distributions are needed to ensure that conservation-planning efforts are directed at appropriate areas. Since the early 1980s, temperate-breeding populations of Branta canadensis (Canada Goose) have increased, yet reliable estimates of the species' distribution are lacking in many regions. Our objective was to identify the landcover features that best predicted Canada Goose distribution. In April 2015, we surveyed 300 one-km2 plots across North Carolina and observed 449 Canada Geese. We quantified percent coverage of 7 continuous landcover variables at 5 different spatial extents for each of the 300 plots. We fit logistic regression models using presence and absence at the 300 plots as the dependent variable and percent-cover covariates as independent variables. The best model for predicting Canada Goose presence included percent pasture within the 9 km2 surrounding the survey plot and percent open water within the 1-km2 survey plot. The probability of Canada Goose presence increased with increasing percent open water and percent pasture, albeit at different spatial extents, which provided important cover and food resources, respectively. Our approach using remote-sensing data to accurately predict Canada Goose presence across a large spatial extent can be employed to determine distributions for other easily surveyed, widely distributed species. |
2016 |
Davis, Amy J; Singh, Kunwar K; Thill, Jean-Claude; Meentemeyer, Ross K Accounting for residential propagule pressure improves prediction of urban plant invasion Journal Article Ecosphere, 7 (3), pp. e01232, 2016. Abstract | Links | BibTeX | Tags: Chinese privet, invasive species, propagule pressure, species distribution model, urban forests @article{Davis2016, title = {Accounting for residential propagule pressure improves prediction of urban plant invasion}, author = {Amy J. Davis and Kunwar K. Singh and Jean-Claude Thill and Ross K. Meentemeyer}, url = {http://onlinelibrary.wiley.com/doi/10.1002/ecs2.1232/full}, doi = {10.1002/ecs2.1232}, year = {2016}, date = {2016-03-08}, journal = {Ecosphere}, volume = {7}, number = {3}, pages = {e01232}, abstract = {Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts.}, keywords = {Chinese privet, invasive species, propagule pressure, species distribution model, urban forests}, pubstate = {published}, tppubtype = {article} } Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts. |
2015 |
Wine, Stuart; Gagne, Sara A; Meentemeyer, Ross K Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us? Journal Article Environmental Management, 55 (1), pp. 159-170, 2015, ISSN: 1432-1009. Abstract | Links | BibTeX | Tags: autologistic regression, citizen science, crowdsourcing, human-wildlife conflict, invasion, species distribution model, urban wildlife @article{Wine2015, title = {Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us?}, author = {Stuart Wine and Sara A. Gagne and Ross K. Meentemeyer}, url = {https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0CCcQFjAB&url=http%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%252Fs00267-014-0373-0.pdf&ei=JhGLVfqPIYyXgwT12IPABQ&usg=AFQjCNG8H3daXCGncVE8kIdFIGrMCUoEkw&sig2=psG_d-L74KjJ3i00HYqkrg&bvm=bv.96339352,d.eXY}, doi = {10.1007/s00267-014-0373-0}, issn = {1432-1009}, year = {2015}, date = {2015-01-01}, journal = {Environmental Management}, volume = {55}, number = {1}, pages = {159-170}, abstract = {The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human–coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans.}, keywords = {autologistic regression, citizen science, crowdsourcing, human-wildlife conflict, invasion, species distribution model, urban wildlife}, pubstate = {published}, tppubtype = {article} } The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human–coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans. |
2013 |
Cord, Anna F; Meentemeyer, Ross K; Leitao, Pedro J; Vaclavik, Tomas Modelling species distributions with remote sensing data: bridging disciplinary perspectives Journal Article Journal of Biogeography, 40 (12), pp. 2226-2227, 2013. Abstract | Links | BibTeX | Tags: remote sensing, species distribution model @article{Cord2013, title = {Modelling species distributions with remote sensing data: bridging disciplinary perspectives}, author = {Anna F. Cord and Ross K. Meentemeyer and Pedro J. Leitao and Tomas Vaclavik}, url = {http://onlinelibrary.wiley.com/doi/10.1111/jbi.12199/full}, doi = {10.1111/jbi.12199}, year = {2013}, date = {2013-12-01}, journal = {Journal of Biogeography}, volume = {40}, number = {12}, pages = {2226-2227}, abstract = {Over the last few decades, correlative species distribution models (SDMs) have been adopted as the most widely used approach for describing and predicting spatial patterns of relationships between species occurrence and environmental conditions (Elith & Leathwick, 2009). Over this same period, the discipline of remote sensing (RS) has produced a breadth of novel geospatial datasets and analytical algorithms for mapping biogeographical heterogeneity. It is not surprising that RS data are now commonly used in SDMs: space- and airborne RS data provide a low cost means to map environmental changes across multiple spatio-temporal scales and are attractive for their ability to measure spatial factors often impossible to quantify otherwise (e.g. landscape connectivity). Looking into this trend further, our literature search – with keywords ‘remote sensing’ and ‘species distribution’ or ‘habitat suitability’ – returned 210 articles where remote sensing was integrally used in SDMs, 60% of which were published just in the past 5 years (ISI Web of Science, 2 May 2013). This development should be a good thing, right? However, several scientists have critically pointed out that the content and spatial scale of RS predictors do not often match species' life-history strategies (e.g. Bradley et al., 2012; Lechner et al., 2012). Our objective here is not to disagree with their recently proposed methodological guidelines, but rather to reflect on the various facets of using RS in SDMs in an effort to bridge disciplinary perspectives.}, keywords = {remote sensing, species distribution model}, pubstate = {published}, tppubtype = {article} } Over the last few decades, correlative species distribution models (SDMs) have been adopted as the most widely used approach for describing and predicting spatial patterns of relationships between species occurrence and environmental conditions (Elith & Leathwick, 2009). Over this same period, the discipline of remote sensing (RS) has produced a breadth of novel geospatial datasets and analytical algorithms for mapping biogeographical heterogeneity. It is not surprising that RS data are now commonly used in SDMs: space- and airborne RS data provide a low cost means to map environmental changes across multiple spatio-temporal scales and are attractive for their ability to measure spatial factors often impossible to quantify otherwise (e.g. landscape connectivity). Looking into this trend further, our literature search – with keywords ‘remote sensing’ and ‘species distribution’ or ‘habitat suitability’ – returned 210 articles where remote sensing was integrally used in SDMs, 60% of which were published just in the past 5 years (ISI Web of Science, 2 May 2013). This development should be a good thing, right? However, several scientists have critically pointed out that the content and spatial scale of RS predictors do not often match species' life-history strategies (e.g. Bradley et al., 2012; Lechner et al., 2012). Our objective here is not to disagree with their recently proposed methodological guidelines, but rather to reflect on the various facets of using RS in SDMs in an effort to bridge disciplinary perspectives. |
2012 |
Meentemeyer, Ross K; Haas, Sarah E; Vaclavik, Tomas Landscape Epidemiology of Emerging Infectious Diseases in Natural and Human-Altered Ecosystems Journal Article Annual Review of Phytopathology, 50 , pp. 379-402, 2012. Abstract | Links | BibTeX | Tags: connectivity, disease control, dynamic model, invasive species, multiscale, species distribution model @article{Meentemeyer2012, title = {Landscape Epidemiology of Emerging Infectious Diseases in Natural and Human-Altered Ecosystems}, author = {Ross K. Meentemeyer and Sarah E. Haas and Tomas Vaclavik}, url = {http://www.annualreviews.org/doi/pdf/10.1146/annurev-phyto-081211-172938}, doi = {10.1146/annurev-phyto-081211-172938}, year = {2012}, date = {2012-06-06}, journal = {Annual Review of Phytopathology}, volume = {50}, pages = {379-402}, abstract = {A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system.}, keywords = {connectivity, disease control, dynamic model, invasive species, multiscale, species distribution model}, pubstate = {published}, tppubtype = {article} } A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system. |
2011 |
Meentemeyer, Ross K; Cunniffe, Nik J; Cook, Alex R; Filipe, Joao A; Hunter, Richard D; Rizzo, David M; Gilligan, Christopher A Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030) Journal Article Ecosphere, 2 (art17), 2011. Abstract | Links | BibTeX | Tags: computational biology, emerging infectious disease, GIS, landscape epidemiology, Phytophthora ramorum, spatial heterogeneity, species distribution model @article{Meentemeyer2011, title = {Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)}, author = {Ross K. Meentemeyer and Nik J. Cunniffe and Alex R. Cook and Joao A. Filipe and Richard D. Hunter and David M. Rizzo and Christopher A. Gilligan }, url = {http://dx.doi.org/10.1890/ES10-00192.1}, year = {2011}, date = {2011-02-16}, journal = {Ecosphere}, volume = {2}, number = {art17}, abstract = {The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions.}, keywords = {computational biology, emerging infectious disease, GIS, landscape epidemiology, Phytophthora ramorum, spatial heterogeneity, species distribution model}, pubstate = {published}, tppubtype = {article} } The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions. |
| 1. | McAlister, Mark A; Moorman, Christopher E; Meentemeyer, Ross K; Fuller, Joseph C; Howell, Douglas L; DePerno, Christopher S: Using Landscape Characteristics to Predict Distribution of Temperate-Breeding Canada Geese. In: Southeastern Naturalist, 16 (2), pp. 127-139, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @article{McAlister2017, title = {Using Landscape Characteristics to Predict Distribution of Temperate-Breeding Canada Geese}, author = {Mark A. McAlister and Christopher E. Moorman and Ross K. Meentemeyer and Joseph C. Fuller and Douglas L. Howell and Christopher S. DePerno}, url = {https://doi.org/10.1656/058.016.0201}, doi = {10.1656/058.016.0201}, year = {2017}, date = {2017-06-01}, journal = {Southeastern Naturalist}, volume = {16}, number = {2}, pages = {127-139}, abstract = {Accurate estimates of species' distributions are needed to ensure that conservation-planning efforts are directed at appropriate areas. Since the early 1980s, temperate-breeding populations of Branta canadensis (Canada Goose) have increased, yet reliable estimates of the species' distribution are lacking in many regions. Our objective was to identify the landcover features that best predicted Canada Goose distribution. In April 2015, we surveyed 300 one-km2 plots across North Carolina and observed 449 Canada Geese. We quantified percent coverage of 7 continuous landcover variables at 5 different spatial extents for each of the 300 plots. We fit logistic regression models using presence and absence at the 300 plots as the dependent variable and percent-cover covariates as independent variables. The best model for predicting Canada Goose presence included percent pasture within the 9 km2 surrounding the survey plot and percent open water within the 1-km2 survey plot. The probability of Canada Goose presence increased with increasing percent open water and percent pasture, albeit at different spatial extents, which provided important cover and food resources, respectively. Our approach using remote-sensing data to accurately predict Canada Goose presence across a large spatial extent can be employed to determine distributions for other easily surveyed, widely distributed species.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Accurate estimates of species' distributions are needed to ensure that conservation-planning efforts are directed at appropriate areas. Since the early 1980s, temperate-breeding populations of Branta canadensis (Canada Goose) have increased, yet reliable estimates of the species' distribution are lacking in many regions. Our objective was to identify the landcover features that best predicted Canada Goose distribution. In April 2015, we surveyed 300 one-km2 plots across North Carolina and observed 449 Canada Geese. We quantified percent coverage of 7 continuous landcover variables at 5 different spatial extents for each of the 300 plots. We fit logistic regression models using presence and absence at the 300 plots as the dependent variable and percent-cover covariates as independent variables. The best model for predicting Canada Goose presence included percent pasture within the 9 km2 surrounding the survey plot and percent open water within the 1-km2 survey plot. The probability of Canada Goose presence increased with increasing percent open water and percent pasture, albeit at different spatial extents, which provided important cover and food resources, respectively. Our approach using remote-sensing data to accurately predict Canada Goose presence across a large spatial extent can be employed to determine distributions for other easily surveyed, widely distributed species. |
| 2. | Davis, Amy J; Singh, Kunwar K; Thill, Jean-Claude; Meentemeyer, Ross K: Accounting for residential propagule pressure improves prediction of urban plant invasion. In: Ecosphere, 7 (3), pp. e01232, 2016. (Type: Journal Article | Abstract | Links | BibTeX) @article{Davis2016, title = {Accounting for residential propagule pressure improves prediction of urban plant invasion}, author = {Amy J. Davis and Kunwar K. Singh and Jean-Claude Thill and Ross K. Meentemeyer}, url = {http://onlinelibrary.wiley.com/doi/10.1002/ecs2.1232/full}, doi = {10.1002/ecs2.1232}, year = {2016}, date = {2016-03-08}, journal = {Ecosphere}, volume = {7}, number = {3}, pages = {e01232}, abstract = {Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts. |
| 3. | Wine, Stuart; Gagne, Sara A; Meentemeyer, Ross K: Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us?. In: Environmental Management, 55 (1), pp. 159-170, 2015, ISSN: 1432-1009. (Type: Journal Article | Abstract | Links | BibTeX) @article{Wine2015, title = {Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us?}, author = {Stuart Wine and Sara A. Gagne and Ross K. Meentemeyer}, url = {https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0CCcQFjAB&url=http%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%252Fs00267-014-0373-0.pdf&ei=JhGLVfqPIYyXgwT12IPABQ&usg=AFQjCNG8H3daXCGncVE8kIdFIGrMCUoEkw&sig2=psG_d-L74KjJ3i00HYqkrg&bvm=bv.96339352,d.eXY}, doi = {10.1007/s00267-014-0373-0}, issn = {1432-1009}, year = {2015}, date = {2015-01-01}, journal = {Environmental Management}, volume = {55}, number = {1}, pages = {159-170}, abstract = {The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human–coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human–coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans. |
| 4. | Cord, Anna F; Meentemeyer, Ross K; Leitao, Pedro J; Vaclavik, Tomas: Modelling species distributions with remote sensing data: bridging disciplinary perspectives. In: Journal of Biogeography, 40 (12), pp. 2226-2227, 2013. (Type: Journal Article | Abstract | Links | BibTeX) @article{Cord2013, title = {Modelling species distributions with remote sensing data: bridging disciplinary perspectives}, author = {Anna F. Cord and Ross K. Meentemeyer and Pedro J. Leitao and Tomas Vaclavik}, url = {http://onlinelibrary.wiley.com/doi/10.1111/jbi.12199/full}, doi = {10.1111/jbi.12199}, year = {2013}, date = {2013-12-01}, journal = {Journal of Biogeography}, volume = {40}, number = {12}, pages = {2226-2227}, abstract = {Over the last few decades, correlative species distribution models (SDMs) have been adopted as the most widely used approach for describing and predicting spatial patterns of relationships between species occurrence and environmental conditions (Elith & Leathwick, 2009). Over this same period, the discipline of remote sensing (RS) has produced a breadth of novel geospatial datasets and analytical algorithms for mapping biogeographical heterogeneity. It is not surprising that RS data are now commonly used in SDMs: space- and airborne RS data provide a low cost means to map environmental changes across multiple spatio-temporal scales and are attractive for their ability to measure spatial factors often impossible to quantify otherwise (e.g. landscape connectivity). Looking into this trend further, our literature search – with keywords ‘remote sensing’ and ‘species distribution’ or ‘habitat suitability’ – returned 210 articles where remote sensing was integrally used in SDMs, 60% of which were published just in the past 5 years (ISI Web of Science, 2 May 2013). This development should be a good thing, right? However, several scientists have critically pointed out that the content and spatial scale of RS predictors do not often match species' life-history strategies (e.g. Bradley et al., 2012; Lechner et al., 2012). Our objective here is not to disagree with their recently proposed methodological guidelines, but rather to reflect on the various facets of using RS in SDMs in an effort to bridge disciplinary perspectives.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Over the last few decades, correlative species distribution models (SDMs) have been adopted as the most widely used approach for describing and predicting spatial patterns of relationships between species occurrence and environmental conditions (Elith & Leathwick, 2009). Over this same period, the discipline of remote sensing (RS) has produced a breadth of novel geospatial datasets and analytical algorithms for mapping biogeographical heterogeneity. It is not surprising that RS data are now commonly used in SDMs: space- and airborne RS data provide a low cost means to map environmental changes across multiple spatio-temporal scales and are attractive for their ability to measure spatial factors often impossible to quantify otherwise (e.g. landscape connectivity). Looking into this trend further, our literature search – with keywords ‘remote sensing’ and ‘species distribution’ or ‘habitat suitability’ – returned 210 articles where remote sensing was integrally used in SDMs, 60% of which were published just in the past 5 years (ISI Web of Science, 2 May 2013). This development should be a good thing, right? However, several scientists have critically pointed out that the content and spatial scale of RS predictors do not often match species' life-history strategies (e.g. Bradley et al., 2012; Lechner et al., 2012). Our objective here is not to disagree with their recently proposed methodological guidelines, but rather to reflect on the various facets of using RS in SDMs in an effort to bridge disciplinary perspectives. |
| 5. | Meentemeyer, Ross K; Haas, Sarah E; Vaclavik, Tomas: Landscape Epidemiology of Emerging Infectious Diseases in Natural and Human-Altered Ecosystems. In: Annual Review of Phytopathology, 50 , pp. 379-402, 2012. (Type: Journal Article | Abstract | Links | BibTeX) @article{Meentemeyer2012, title = {Landscape Epidemiology of Emerging Infectious Diseases in Natural and Human-Altered Ecosystems}, author = {Ross K. Meentemeyer and Sarah E. Haas and Tomas Vaclavik}, url = {http://www.annualreviews.org/doi/pdf/10.1146/annurev-phyto-081211-172938}, doi = {10.1146/annurev-phyto-081211-172938}, year = {2012}, date = {2012-06-06}, journal = {Annual Review of Phytopathology}, volume = {50}, pages = {379-402}, abstract = {A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system. |
| 6. | Meentemeyer, Ross K; Cunniffe, Nik J; Cook, Alex R; Filipe, Joao A; Hunter, Richard D; Rizzo, David M; Gilligan, Christopher A: Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030). In: Ecosphere, 2 (art17), 2011. (Type: Journal Article | Abstract | Links | BibTeX) @article{Meentemeyer2011, title = {Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)}, author = {Ross K. Meentemeyer and Nik J. Cunniffe and Alex R. Cook and Joao A. Filipe and Richard D. Hunter and David M. Rizzo and Christopher A. Gilligan }, url = {http://dx.doi.org/10.1890/ES10-00192.1}, year = {2011}, date = {2011-02-16}, journal = {Ecosphere}, volume = {2}, number = {art17}, abstract = {The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions. |
