Comparing generalized and customized spread models for nonnative forest pests.
exotic
macroecology
multispecies
nonindigenous
risk assessment
simulation
spatially explicit
Journal
Ecological applications : a publication of the Ecological Society of America
ISSN: 1051-0761
Titre abrégé: Ecol Appl
Pays: United States
ID NLM: 9889808
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
13
03
2019
revised:
09
07
2019
accepted:
18
07
2019
pubmed:
31
7
2019
medline:
25
9
2020
entrez:
31
7
2019
Statut:
ppublish
Résumé
While generality is often desirable in ecology, customized models for individual species are thought to be more predictive by accounting for context specificity. However, fully customized models require more information for focal species. We focus on pest spread and ask: How much does predictive power differ between generalized and customized models? Further, we examine whether an intermediate "semi-generalized" model, combining elements of a general model with species-specific modifications, could yield predictive advantages. We compared predictive power of a generalized model applied to all forest pest species (the generalized dispersal kernel or GDK) to customized spread models for three invasive forest pests (beech bark disease [Cryptococcus fagisuga], gypsy moth [Lymantria dispar], and hemlock woolly adelgid [Adelges tsugae]), for which time-series data exist. We generated semi-generalized dispersal kernel models (SDK) through GDK correction factors based on additional species-specific information. We found that customized models were more predictive than the GDK by an average of 17% for the three species examined, although the GDK still had strong predictive ability (57% spatial variation explained). However, by combining the GDK with simple corrections into the SDK model, we attained a mean of 91% of the spatial variation explained, compared to 74% for the customized models. This is, to our knowledge, the first comparison of general and species-specific ecological spread models' predictive abilities. Our strong predictive results suggest that general models can be effectively synthesized with context-specific information for single species to respond quickly to invasions. We provided SDK forecasts to 2030 for all 63 United States pests in our data set.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e01988Informations de copyright
© 2019 by the Ecological Society of America.
Références
Aslan, B., and G. Zech. 2005. New test for the multivariate two-sample problem based on the concept of minimum energy. Journal of Statistical Computation and Simulation 75:109-119.
Aukema, J. E., et al. 2011. Economic impacts of non-native forest insects in the continental United States. PLoS ONE 6:e24587.
Aylor, D. E. 1990. The role of intermittent wind in the dispersal of fungal pathogens. Annual Review of Phytopathology 28:73-92.
Bigsby, K. M., P. C. Tobin, and E. O. Sills. 2011. Anthropogenic drivers of gypsy moth spread. Biological Invasions 13:2077-2090.
Bradie, J., and B. Leung. 2015. Pathway-level models to predict non-indigenous species establishment using propagule pressure, environmental tolerance and trait data. Journal of Applied Ecology 52:100-109.
Colunga-Garcia, M., R. A. Haack, R. A. Magarey, and M. L. Margosian. 2010. Modeling spatial establishment patterns of exotic forest insects in urban areas in relation to tree cover and propagule pressure. Journal of Economic Entomology 103:108-118.
Evans, M. R., et al. 2013. Do simple models lead to generality in ecology? Trends in Ecology & Evolution 28:578-583.
Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302-4315.
Fithian, W., J. Elith, T. Hastie, and D. A. Keith. 2015. Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods in Ecology and Evolution 6:424-438.
Gaertner, M., J. R. Wilson, M. W. Cadotte, J. S. MacIvor, R. D. Zenni, and D. M. Richardson. 2017. Non-native species in urban environments: patterns, processes, impacts and challenges. Biological Invasions 19:3461-3469.
Gilbert, M., J. C. Grégoire, J. F. Freise, and W. Heitland. 2004. Long-distance dispersal and human population density allow the prediction of invasive patterns in the horse chestnut leafminer Cameraria ohridella. Journal of Animal Ecology 73:459-468.
Grayson, K. L., and D. M. Johnson. 2018. Novel insights on population and range edge dynamics using an unparalleled spatiotemporal record of species invasion. Journal of Animal Ecology 87:581-593.
Haack, R. A. 2006. Exotic bark-and wood-boring Coleoptera in the United States: recent establishments and interceptions. Canadian Journal of Forest Research 36:269-288.
Hansen, B. B. 2007. Flexible, optimal matching for observational studies. R News 7:18-24.
Hauer, M. E. 2019. Population projections for US counties by age, sex, and race controlled to shared socioeconomic pathway. Scientific data 6:190005.
Hellmann, J. J., J. E. Byers, B. G. Bierwagen, and J. S. Dukes. 2008. Five potential consequences of climate change for invasive species. Conservation Biology 22:534-543.
Houston, D. R. 1994. Major new tree disease epidemics: beech bark disease. Annual Review of Phytopathology 32:75-87.
Hudgins, E. J., A. M. Liebhold, and B. Leung. 2017. Predicting the spread of all invasive forest pests in the United States. Ecology Letters 20:426-435.
Koch, F. H., and W. D. Smith. 2008. Spatio-temporal analysis of Xyleborus glabratus (Coleoptera: Circulionidae: Scolytinae) invasion in eastern US forests. Environmental Entomology 37:442-452.
Kot, M., M. A. Lewis, and P. Van Den Driessche. 1996. Dispersal data and the spread of invading organisms. Ecology 77:2027-2042.
Kovacs, K. F., R. J. Mercader, R. G. Haight, N. W. Siegert, D. G. McCullough, and A. M. Liebhold. 2011. The influence of satellite populations of emerald ash borer on projected economic costs in US communities, 2010-2020. Journal of Environmental Management 92:2170-2181.
Leung, B., E. J. Hudgins, A. Potapova, and M. Ruiz-Jaen. 2019. A new baseline for countrywide α-diversity and species distributions: illustration using > 6000 plant species in Panama. Ecological Applications 29(3):e01866.
Liebhold, A., V. Mastro, and P. W. Schaefer. 1989. Learning from the legacy of Leopold Trouvelot. Bulletin of the Entomological Society of America 35:20-21.
Liebhold, A. M., J. A. Halverson, and G. A. Elmes. 1992. Gypsy moth invasion in North America: a quantitative analysis. Journal of Biogeography 19:513-520.
Lodge, D. M., et al. 2006. Biological invasions: recommendations for US policy and management. Ecological Applications 16:2035-2054.
Lovett, G. M., M. Weiss, A. M. Liebhold, T. P. Holmes, B. Leung, K. F. Lambert, et al. 2016. Nonnative forest insects and pathogens in the United States: Impacts and policy options. Ecological Applications 26:1437-1455.
McCullough, D. G., and R. J. Mercader. 2012. Evaluation of potential strategies to SLow Ash Mortality (SLAM) caused by emerald ash borer (Agrilus planipennis): SLAM in an urban forest. International Journal of Pest Management 58:9-23.
Morin, R. S., A. M. Liebhold, P. C. Tobin, K. W. Gottschalk, and E. Luzader. 2007. Spread of beech bark disease in the eastern United States and its relationship to regional forest composition. Canadian Journal of Forest Research 37:726-736.
Morin, R. S., A. M. Liebhold, and K. W. Gottschalk. 2009. Anisotropic spread of hemlock woolly adelgid in the eastern United States. Biological Invasions 11:2341-2350.
Paradis, A., J. Elkinton, K. Hayhoe, and J. Buonaccorsi. 2008. Role of winter temperature and climate change on the survival and future range expansion of the hemlock woolly adelgid (Adelges tsugae) in eastern North America. Mitigation and Adaptation Strategies for Global Change 13:541-554.
Seebens, H., et al. 2015. Global trade will accelerate plant invasions in emerging economies under climate change. Global Change Biology 21:4128-4140.
Sharov, A. A., D. Leonard, A. M. Liebhold, E. A. Roberts, and W. Dickerson. 2002. “Slow the spread”: a national program to contain the gypsy moth. Journal of Forestry Research 100:30-36.
Shigesada, N., K. Kawasaki, and Y. Takeda. 1995. Modeling stratified diffusion in biological invasions. American Naturalist 146:229-251.
Skellam, J. G. 1951. Random dispersal in theoretical populations. Biometrika 38:196-218.
Skuhravá, M., V. Skuhravý, and G. Csóka. 2007. The invasive spread of the gall midge Obolodiplosis robiniae in Europe. Cecidology 22:84-90.
Taylor, R. A. J., L. S. Bauer, T. M. Poland, and K. N. Windell. 2010. Flight performance of Agrilus planipennis (Coleoptera: Buprestidae) on a flight mill and in free flight. Journal of Insect Behavior 23:128-148.
Tobin, P. C., D. R. Gray, and A. M. Liebhold. 2014. Supraoptimal temperatures influence the range dynamics of a non-native insect. Diversity and Distributions 20:813-823.
Vilà, M., and P. E. Hulme, editors. 2017. Impact of biological invasions on ecosystem services. Volume 12. Springer, Cham, Switzerland.
Ward, S. F., S. Fei, and A. M. Liebhold. 2019. Spatial patterns of discovery points and invasion hotspots of non-native forest pests. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12988.
Ward, J. S., M. E. Montgomery, C. A. S.-J. Cheah, B. P. Onken, and R. S. Cowles. 2004. Eastern hemlock forests: guidelines to minimize the impacts of hemlock woolly adelgid. NA-TP-03-04. USDA Forest Service, Northeastern Area State and Private Forestry, Morgantown, West Virginia, USA.