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Ecological Niche Modeling: Concepts and Applications A. Townsend Peterson University of Kansas Natural History Museum Niche Concepts Many definitions exist for ‘niche’ … I avoid those that are process-oriented (e.g., “the role of species X in its community”) Focus on a more workable definition: “the set of environmental conditions within which a species can maintain populations without immigrational subsidy” (J. Grinnell) Grinnellian concept has the advantage of a single focus (environmental conditions) Interactive (Eltonian) processes probably act chiefly on finer spatial scales and are not commonly discernable on coarse scales The BAM Diagram Abiotic niche Area presenting appropriate combinations of abiotic and biotic conditions (= potential distribution) Actual geographic distribution (abiotic and biotic conditions fulfilled, accessible to dispersers) Accessibility Biotic interactions Species Interactions Important Abiotic niche Accessibility Biotic interactions Species Interactions Unimportant Abiotic niche Biotic interactions Accessibility Niche Modeling Physiological constraints Ecological processes Geographic phenomenon As such, geographic phenomena of distributions should be reconstructed in ecological spaces Linked spaces, in which there is a one-toone mapping between elements in G and elements in E Modeling best carried out in E Easy-to-measure variables (scenopoetic) Proximate variables (scenopoetic and/or bionomic) S1 S2 P1 S3 P2 S4 P3 S5 P4 S6 Presence or absence of suitable conditions for the species in question “Presence” Absence Data Abiotic niche Accessibility Biotic interactions Niche Modeling Considerations Input Data Presence data should be unbiased in sampling environments OR interpretation limited to environments sampled Absence data are of dubious utility Environmental data must be proximate … i.e., as close to causal as can be Occurrence data and environmental data must be of comparable resolution spatially and temporally Modeling Considerations Enough complexity to capture details of niches Not too much complexity to avoid overfitting Model response types must be appropriate to the complexity of ecological niches Appropriate regions for training and testing models must be chosen based on M (accessibility) Model validation is not simple Major Findings ENM offers a means of identifying nonrandom associations between species’ occurrences and ecological features (= niche) Niches are conserved over ecologicalevolutionary time periods Niches are conserved even in novel community contexts ENM offers excellent predictivity of geographic phenomena related to biodiversity … Niche Conservatism I: Invasions are Predictable Aedes albopictus Aedes albopictus Known as the “Asian Tiger Mosquito” Invader; fastest spreading mosquito in the world Aggressive daytime biter and pest Known to transmit Dengue, La Crosse, St. Louis, Eastern Equine, Ross River, Rift Valley, and West Nile Viruses Abiotic niche Area presenting appropriate combinations of abiotic and biotic conditions (= potential distribution) Actual geographic distribution (abiotic and biotic conditions fulfilled, accessible to dispersers) Accessibility Biotic interactions Abiotic niche Area presenting appropriate combinations of abiotic and biotic conditions (= potential distribution) Actual geographic distribution (abiotic and biotic conditions fulfilled, accessible to dispersers) Accessibility Accessibility Biotic interactions Aedes albopictus Present predicted distribution, native range in Asia Aedes albopictus: USA invasion Projected Asian niche into USA present to create invasion risk-map. How well did GARP perform... Aedes albopictus: USA invasion Aedes albopictus: world risk-map Conservatism II Predictivity Across the End of the Pleistocene Pollen-based Analyses Picea sp. Brasenia schreberi Acer saccharum Niche Conservatism Evidence Short term – invasions are predictable in terms of potential geography Middle term – longitudinal studies Pleistocene to Present are successful Middle-to-Long term – sister species tend to be very similar Long term – tracing over phylogeny often (but not always) shows conservative evolutionary patterns Applications II: Marine Intrusion and Climate Change Effects on Biodiversity Coastline Topography and Marine Intrusion New Zealand - Coromandel Coast South Coast New Guinea The Americas Global Projected Extinctions from Marine Intrusion Global Species Losses: 181 species under the 1 m scenario 337 species under the 6 m scenario out of 18,628 current species considered Mexican Sheartail Doricha eliza Joint Effects Double whammy More species affected Joint Effects Percent loss owing to inundation 60 40 20 0 0 20 40 60 Percent loss owing to climate change 80 APPLICATIONS III: BIODIVERSITY LOSS IN MEXICO Biodiversity Loss in Mexico Loss of Evergreen Tropical Forest Black – area lost; lightest gray – area remaining Jay Species – Distributional Loss Map of Distributional Loss - Corvids APPLICATIONS IV: MALARIA IN AFRICA Anopheles gambiae Forecasting Dengue Vector Activity Using Time-specific Ecological Niche Modeling A. T. Peterson, Y. Nakazawa, and E. Martinez-Meyer Instituto de Biologia and Facultad de Ciencias, Universidad Nacional Autonoma de Mexico Satellite Imagery Advanced Very High Resolution Radiometer (AVHRR) passes over all points on Earth’s surface each day Raw data include reflectance in different parts of the electromagnetic spectrum Normalized Difference Vegetation Index (NDVI) is a composite of two bands of raw data that summarizes ‘greenness’, and (more precisely) volume of photosynthetic vegetation AVHRR NDVI data are available as biweekly or monthly composites Potential for ENM and Dengue ENM is presently developed based on static environmental data that are not temporally precise Using occurrence information and ecological data that are precise both in time and space, ENM can be made time-specific Would provide detailed predictivity on a weekly or monthly basis for vector distributions and disease transmission Potentially applicable to any ephemeral species of epidemiological interest Distributional Data for Aedes aegypti, 1995, in Mexico Time-ignorant Prediction Closer View Note broad areas predicted Time-specific Occurrence Data August 1995 August 1995 Predicts August 1995 (easy) Can We Predict August from Previous Months? Example: June 1995 predicts August 1995 Composite Prediction for August 1995: Two Previous Months Averaged Time-specific Model Compared with Time-ignorant model Closer View Summary of Prototype Tests Percent correctly predicted Month N Any >50% >80% June 22 100 72.7 54.5 July 28 100 82.1 67.9 August 40 100 75 35 September 25 100 80 16 October 19 94.7 78.9 21.1 November 25 100 76 52 December 22 100 95.5 81.8 APPLICATIONS: EMERGENCE OF LEISHMANIASIS IN SOUTHERN BRAZIL Diferencia 1975 – 2055 - migonei Red areas improve for the species, blue areas get worse for the species Diferencia 1975 – 2055 - intermedia Red areas improve for the species, blue areas get worse for the species Diferencia 1975 – 2055 - whitmani Conclusion: Lutzomyia whitmani is the sandfly species most likely to be responding to climate change and causing the observed re-emergence Red areas improve for the species, blue areas get worse for the species Niche Modeling Summary Niches evolve slowly Conserved niches allow many fascinating insights Characterize ecology and distributions Predict unknown and undescribed populations Predict geography of species’ invasions Predict shifts in species’ distributions in response to climate change and land use change Detect effects of interactions with other species Improve conservation planning based on species’ distributions PLEASE, now assemble, organize, and prepare your good and bad vibrations Thanks [email protected]