Abstract
Predictive biodiversity distribution models (BDM) are useful for understanding the structure and functioning of ecological communities and managing them in the face of anthropogenic disturbances. In cases where their predictive performance is good, such models can help fill knowledge gaps that could only otherwise be addressed using direct observation, an often logistically and financially onerous prospect. The cornerstones of such models are environmental and spatial predictors. Typically, however, these predictors vary on different spatial and temporal scales than the biodiversity they are used to predict and are interpolated over space and time. We explore the consequences of these scale mismatches between predictors and predictions by comparing the results of BDMs built to predict fish species richness on Australia’s Great Barrier Reef. Specifically, we compared a series of annual models with uninformed priors with models built using the same predictors and observations, but which accumulated information through time via the inclusion of informed priors calculated from previous observation years. Advantages of using informed priors in these models included (1) down-weighting the importance of a large disturbance, (2) more certain species richness predictions, (3) more consistent predictions of species richness and (4) increased certainty in parameter coefficients. Despite such advantages, further research will be required to find additional ways to improve model performance.
Ana M. M. Sequeira and M. Julian Caley are co-first authors.
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H. Assareh, I. Smith, K.L. Mengersen, Bayesian change point detection in monitoring clinical outcomes, in Case Studies in Bayesian Statistical Modelling and Analysis, ed. by C. L. Alston, K. L. Mengersen, A. N. Pettitt, (Wiley, Chichester, 2012)
R. Beeden, J. Maynard, M. Puotinen, P. Marshall, J. Dryden, J. Goldberg, G. Williams, Impacts and recovery from severe tropical cyclone Yasi on the Great Barrier Reef. PLoS One 10, e0121272 (2015)
L. Broemeling, Bayesian Methods for Repeated Measures (Chapman and Hall/CRC, New York, 2016)
B.J. Cardinale, J.E. Duffy, A. Gonzalez, D.U. Hooper, C. Perrings, P. Venail, A. Narwani, G.M. Mace, D. Tilman, D.A. Wardle, A.P. Kinzig, G.C. Daily, M. Loreau, J.B. Grace, A. Larigauderie, D.S. Srivastava, S. Naeem, Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012)
J.H. Connell, Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978)
P.K. Dayton, Competition, disturbance, and community organization: the provision and subsequent utilization of space in a rocky intertidal community. Ecol. Monogr. 41, 351–389 (1971)
R. Fisher, B.T. Radford, N. Knowlton, R.E. Brainard, F.B. Michaelis, M.J. Caley, Global mismatch between research effort and conservation needs of tropical coral reefs. Conserv. Lett. 4, 64–72 (2011)
A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari, D. Rubin, Bayesian Data Analysis (Chapman and Hall, New York, 2015)
A.R. Halford, M.J. Caley, Towards an understanding of resilience in isolated coral reefs. Glob. Chang. Biol. 15, 3031–3045 (2009)
A.R. Halford, A.A. Thompson, Visual Census Surveys of Reef Fish. Long Term Monitoring of the Great Barrier Reef Standard Operational Procedure Number 3 (Australian Institute of Marine Science, Townsville, 1996)
A.R. Ives, S.R. Carpenter, Stability and diversity of ecosystems. Science 317, 58–62 (2007)
S.Y. Kang, J.M. McGree, C.C. Drovandi, M.J. Caley, K.L. Mengersen, Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef. Ecol. Appl. 26, 2635–2646 (2016)
G.M. Lovett, D.A. Burns, C.T. Driscoll, J.C. Jenkins, M.J. Mitchell, L. Rustad, J.B. Shanley, G.E. Likens, R. Haeuber, Who needs environmental monitoring? Front. Ecol. Environ. 5, 253–260 (2007)
S.A. Matthews, C. Mellin, A. MacNeil, S.F. Heron, W. Skirving, M. Puotinen, M.J. Devlin, M. Pratchett, High-resolution characterization of the abiotic environment and disturbance regimes on the Great Barrier Reef, 1985–2017. Ecology 100, e02574 (2019)
C. Mellin, C.J.A. Bradshaw, M.G. Meekan, M.J. Caley, Environmental and spatial predictors of species richness and abundance in coral reef fishes. Glob. Ecol. Biogeogr. 19, 212–222 (2010)
C. Mellin, M. Aaron MacNeil, A.J. Cheal, M.J. Emslie, M. Julian Caley, Marine protected areas increase resilience among coral reef communities. Ecol. Lett. 19, 629–637 (2016)
R.B. Millar, Comparison of hierarchical Bayesian models for overdispersed count data using DIC and Bayes’ factors. Biometrics 65, 962–969 (2009)
N. Myers, R.A. Mittermeier, C.G. Mittermeier, G.A.B. da Fonseca, J. Kent, Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000)
J.D. Nichols, B.K. Williams, Monitoring for conservation. Trends Ecol. Evol. 21, 668–673 (2006)
R.T. Paine, S.A. Levin, Intertidal landscapes: disturbance and the dynamics of pattern. Ecol. Monogr. 51, 145–178 (1981)
S.T.A. Pickett, P.S.E. White, The Ecology of Natural Disturbance and Patch Dynamics (Academic, London, 1985)
S.L. Pimm, The complexity and stability of ecosystems. Nature 307, 321–326 (1984)
S. Sarkka, Bayesian Filtering and Smoothing (Cambridge University Press, Cambridge, 2013)
A.M.M. Sequeira, C. Mellin, H.M. Lozano-Montes, M.A. Vanderklift, R.C. Babcock, M.D.E. Haywood, J.J. Meeuwig, M.J. Caley, Transferability of predictive models of coral reef fish species richness. J. Appl. Ecol. 53, 64–72 (2016)
A.M.M. Sequeira, P.J. Bouchet, K.L. Yates, K. Mengersen, M.J. Caley, Transferring biodiversity models for conservation: opportunities and challenges. Methods Ecol. Evol. 9, 1250–1264 (2018)
A.M.M. Sequeira, C. Mellin, H.M. Lozano-Montes, J.J. Meeuwig, M.A. Vanderklift, M.D.E. Haywood, R.C. Babcock, M.J. Caley, Challenges of transferring models of fish abundance between coral reefs. PeerJ 6, e4566 (2018)
W.P. Sousa, The role of disturbance in natural communities. Annu. Rev. Ecol. Syst. 15, 353–391 (1984)
H. Sweatman, A. Cheal, G. Coleman, M. Emslie, K. Johns, M. Jonker, I. Miller, K. Osborne, Long-Term Monitoring of the Great Barrier Reef. Status Report No 8 (Australian Institute of Marine Science, Townsville, 2008)
D. Tilman, J.A. Downing, Biodiversity and stability in grasslands. Nature 367, 363 (1994)
J. Vercelloni, K. Mengersen, F. Ruggeri, M.J. Caley, Improved coral population estimation reveals trends at multiple scales on Australia’s Great Barrier Reef. Ecosystems 20, 1337–1350 (2017)
B. Worm, E.B. Barbier, N. Beaumont, J.E. Duffy, C. Folke, B.S. Halpern, J.B.C. Jackson, H.K. Lotze, F. Micheli, S.R. Palumbi, E. Sala, K.A. Selkoe, J.J. Stachowicz, R. Watson, Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006)
K.L. Yates, C. Mellin, M.J. Caley, B.T. Radford, J.J. Meeuwig, Models of marine fish biodiversity: assessing predictors from three habitat classification schemes. PLoS One 11, e0155634 (2016)
K.L. Yates, P.J. Bouchet, M.J. Caley, K. Mengersen, C.F. Randin, S. Parnell, A.H. Fielding, A.J. Bamford, S. Ban, A.M. Barbosa, C.F. Dormann, J. Elith, C.B. Embling, G.N. Ervin, R. Fisher, S. Gould, R.F. Graf, E.J. Gregr, P.N. Halpin, R.K. Heikkinen, S. Heinänen, A.R. Jones, P.K. Krishnakumar, V. Lauria, H. Lozano-Montes, L. Mannocci, C. Mellin, M.B. Mesgaran, E. Moreno-Amat, S. Mormede, E. Novaczek, S. Oppel, G. Ortuño Crespo, A.T. Peterson, G. Rapacciuolo, J.J. Roberts, R.E. Ross, K.L. Scales, D. Schoeman, P. Snelgrove, G. Sundblad, W. Thuiller, L.G. Torres, H. Verbruggen, L. Wang, S. Wenger, M.J. Whittingham, Y. Zharikov, D. Zurell, A.M.M. Sequeira, Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802 (2018)
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Table S1
Coefficient estimates and standard deviations for all models across all datasets when using informative priors. Estimates for which the 68% and 95% credible intervals cross zero are shown in light and dark grey respectively, indicating no substantive effect from the respective predictor. All models include a random intercept α j for the jth reef, with α j ~ N(μ α, σ α 2). Model descriptions are given in Table 15.1 (DOCX 60 kb)
Table S2
Coefficient estimates and standard deviations for all models across all datasets when using informative priors. Estimates for which the 68% and 95% credible intervals cross zero are shown in light and dark grey respectively, indicating no substantive effect from the respective predictor. Model descriptions are given in Table 15.1 (DOCX 60 kb)
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Sequeira, A.M.M., Caley, M.J., Mellin, C., Mengersen, K.L. (2020). Bayesian Learning of Biodiversity Models Using Repeated Observations. In: Mengersen, K., Pudlo, P., Robert, C. (eds) Case Studies in Applied Bayesian Data Science. Lecture Notes in Mathematics, vol 2259. Springer, Cham. https://doi.org/10.1007/978-3-030-42553-1_15
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