Kristina Enciso. Brian Leung. McGill University Quebec, Canada

Transcription

1 Embracing uncertainty to incorporate biotic interactions into species distribution modeling: creating community assemblages using interactive community distribution models Kristina Enciso Brian Leung McGill University Quebec, Canada

2 Species Distribution Models Relationship between environmental gradients and species distributions Predict distributions to unknown sites and/or future ecological sites Ecological Niche Theory (MacArthur, 1972)

3 Species Distribution Models How are they currently used? Conservation planning Climate change research Invasion ecology Policy/ Management Where are species interactions in SDMs? Are species interactions important?

4 Biotic Interactions & Invasive Species Why are biotic interactions not usually included in SDMs: Difficult to quantify Data is difficult to obtain Interactions limited to two or three interacting species Use of analytic shortcuts

5 Biotic Interactions & Invasive Species Biotic interactions are especially important when examining invasive species No a priori knowledge of interspecific interactions Induce changes to ecosystem and community functioning

6 Problems Species distributions models do not include biotic interactions Biotic interactions are difficult to incorporate Available data may be sparse

7 Purpose Can we create community assemblages using qualitative measures of biotic interactions despite limited community knowledge? Use interactive community distribution models (icdm)

8 Methods Artificial species abundances Generated using the Lotka-Volterra competition equation

9 Methods Artificial species abundances Generated using the Lotka-Volterra competition equation Growth

10 Methods Artificial species abundances Generated using the Lotka-Volterra competition equation Species Interaction

11 Methods Step 1: Species distribution model Separate species distribution models were created for each species where data is available. Sampled locations and environmental predictors do not need to be the same for each species.

12 Methods Step 2: Interactive community distribution model Step 1: Species distribution model Using the estimated species distribution of all other species found by the SDMs, as well as the previous environmental predictor new predicted species distributions are generated.

13 Methods Step 2: Interactive community distribution model Step 1: Species distribution model These new predicted abundances are re-inputted into the community distribution, taking the place of the estimated distributions from the SDM in an iterative process until regression coefficients come to an equilibrium.

14 Methods Step 2: Interactive community distribution model Step 1: Species distribution model These new predicted abundances are re-inputted into the community distribution, taking the place of the estimated distributions from the SDM in an iterative process until regression coefficients come to an equilibrium.

15 Preliminary Results Figure 1: Regression coefficient for interaction terms of four species for 100 iterations.

16 Preliminary Results Figure 2: The difference between R 2 values for SDM and R 2 values for icdm for 1000 simulations Negative values indicate more variation being explained by icdm

17 Discussion After every iteration predicted abundances become more and more accurate until they reach an equilibrium point where previous estimations can no longer be improved. R 2 values of icdm were better than R 2 values of SDMS.

18 Benefits of using icdms Use sparse data sets, smaller sampling effort Metric of biotic interactions not needed Better predictions than SDMs

19 Future Work Use more robust model algorithms (ANN, MaxEnt) Use real world data to validate method. Zooplankton and fish species data from Ontario lakes will be used to create community assemblage maps of the pelagic invasive species, Bythotrephes longimanus. Bythotrephes has been found to change phytoplankton, zooplankton, and fish taxonomic distributions (Strecker et al. 2008).

20 Acknowledgements Norman Yan (York University) Nick Mandrak (Fisheries and Oceans Canada) Corey Chivers Johanna Bradie Lidia Della Venezia Andrew Sellars Paul Edwards

21 References Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological theory and statistical modelling. Ecological Modelling, 157(2-3), Guisan, A., & Thuiller, W. (2005). Predicting species distribution: Offering more than simple habitat models. Ecology Letters, 8(9), Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3), Hof, A. R., Jansson, R., & Nilsson, C. (2012). How biotic interactions may alter future predictions of species distributions: Future threats to the persistence of the arctic fox in fennoscandia. Diversity and Distributions, 18(6), MacArthur, R.H. (1972) Geographical Ecology: Patterns in the Distribution of Species. Princeton University Press, New Jersey. Soberon, J., and A. T. Peterson Interpretation of models of fundamental ecological niches and species distributional areas. Biodiversity Informatics 2:1 10. Strecker, A. L., & Arnott, S. E. (2008). Invasive predator, bythotrephes, has varied effects on ecosystem function in freshwater lakes. Ecosystems, 11(3), Strecker, A. L., Beisner, B. E., Arnott, S. E., Paterson, A. M., Winter, J. G., Johannsson, O. E., & Yan, N. D. (2011). Direct and indirect effects of an invasive planktonic predator on pelagic food webs. Limnology and Oceanography, 56(1), Zimmermann, N. E., Edwards, T. C., Graham, C. H., Pearman, P. B., & Svenning, J. -. (2010). New trends in species distribution modelling. Ecography, 33(6),