Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions

Marine managers routinely use spatial data to make decisions about their marine environment. Uncertainty associated with this spatial data can have profound impacts on these management decisions and their projected outcomes. Recent advances in modeling techniques, including species distribution models (SDMs), make it easier to generate continuous maps showing the uncertainty associated with spatial predictions and maps. However, SDM predictions and maps can be complex and nuanced. This complexity makes their use challenging for non-technical managers, preventing them from having the best available information to make decisions. To help bridge these communication and information gaps, we developed maps to illustrate how SDMs and associated uncertainty can be translated into readily usable products for managers. We also explicitly described the potential impacts of uncertainty on marine zoning decisions. This approach was applied to a case study in Saipan Lagoon, Commonwealth of the Northern Mariana Islands (CNMI). Managers in Saipan are interested in minimizing the potential impacts of personal watercraft (e.g., jet skis) on staghorn Acropora (i.e., Acropora aspera, A. formosa, and A. pulchra), which is an important coral assemblage in the lagoon. We used a recently completed SDM for staghorn Acropora to develop maps showing the sensitivity of zoning options to three different prediction and three different uncertainty thresholds (nine combinations total). Our analysis showed that the amount of area and geographic location of predicted staghorn Acropora presence changed based on these nine combinations. These dramatically different spatial patterns would have significant zoning implications when considering where to exclude and/or allow jet skis operations inside the lagoon. They also show that different uncertainty thresholds may lead managers to markedly different conclusions and courses of action. Defining acceptable levels of uncertainty upfront is critical for ensuring that managers can make more informed decisions, meet their marine resource goals and generate favorable outcomes for their stakeholders.

[1]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[2]  Jane Elith,et al.  Sensitivity of conservation planning to different approaches to using predicted species distribution data , 2005 .

[3]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[4]  C. Erbe Underwater noise of small personal watercraft (jet skis). , 2013, The Journal of the Acoustical Society of America.

[5]  A. Whitfield,et al.  Impacts of recreational motorboats on fishes: a review. , 2014, Marine pollution bulletin.

[6]  D. Brinkman,et al.  Acute ecotoxicology of natural oil and gas condensate to coral reef larvae , 2016, Scientific Reports.

[7]  Helen M. Regan,et al.  ROBUST DECISION‐MAKING UNDER SEVERE UNCERTAINTY FOR CONSERVATION MANAGEMENT , 2005 .

[8]  Mark W. Schwartz,et al.  Using niche models with climate projections to inform conservation management decisions , 2012 .

[9]  Brendan A. Wintle,et al.  Is my species distribution model fit for purpose? Matching data and models to applications , 2015 .

[10]  S. Sarkar,et al.  Systematic conservation planning , 2000, Nature.

[11]  U. Fish Endangered and threatened wildlife and plants , 1987 .

[12]  Hugh P. Possingham,et al.  Incorporating uncertainty associated with habitat data in marine reserve design , 2013 .

[13]  Dawn M. Kaufman,et al.  LATITUDINAL GRADIENTS OF BIODIVERSITY:Pattern,Process,Scale,and Synthesis , 2003 .

[14]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[15]  R. B. Aronson,et al.  Scale-dependent spatial variability of coral assemblages along the Florida Reef Tract , 1999, Coral Reefs.

[16]  W. Haider,et al.  The economic value of the coral reefs of Saipan, Commonwealth of the Northern Mariana Islands , 2006 .

[17]  Chris Field,et al.  A stochastic approach to marine reserve design: Incorporating data uncertainty , 2008, Ecol. Informatics.

[18]  L. Raymundo,et al.  Anomalous temperatures and extreme tides: Guam staghorn Acropora succumb to a double threat , 2017 .

[19]  Suzana Dragicevic,et al.  GIS-Based Multicriteria Evaluation and Fuzzy Sets to Identify Priority Sites for Marine Protection , 2006, Biodiversity and Conservation.

[20]  J. Davenport,et al.  The impact of tourism and personal leisure transport on coastal environments: A review , 2006 .

[21]  Hugh P Possingham,et al.  Making conservation decisions under uncertainty for the persistence of multiple species. , 2007, Ecological applications : a publication of the Ecological Society of America.

[22]  Craig J. Brown,et al.  Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques , 2011 .

[23]  B. Halpern,et al.  Guiding ecological principles for marine spatial planning , 2010 .

[24]  S. Sandin,et al.  Crude oil contamination interrupts settlement of coral larvae after direct exposure ends , 2015 .

[25]  Simon J. Pittman,et al.  Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals , 2009 .

[26]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[27]  Chris Roelfsema,et al.  Trade-offs between data resolution, accuracy, and cost when choosing information to plan reserves for coral reef ecosystems. , 2017, Journal of environmental management.

[28]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[29]  Robert van Woesik,et al.  Dynamics of shallow-water assemblages in the Saipan Lagoon , 2008 .

[30]  R. Grizzle,et al.  RECREATIONAL BOATING ACTIVITY AND ITS IMPACT ON THE RECRUITMENT AND SURVIVAL OF THE OYSTER CRASSOSTREA VIRGINICA ON INTERTIDAL REEFS IN MOSQUITO LAGOON, FLORIDA , 2009 .

[31]  Yakov Ben-Haim,et al.  Information-gap decision theory , 2001 .

[32]  M. Dokulil Environmental Impacts of Tourism on Lakes , 2014 .

[33]  Robin Gregory,et al.  Structured Decision Making: A Practical Guide to Environmental Management Choices , 2012 .

[34]  W. Phillips Tourism Threats to Coral Reef Resilience at Koh Sak, Pattaya Bay , 2015 .

[35]  Stacy D. Jupiter,et al.  Ecosystem-Based Management in Fiji: Successes and Challenges after Five Years of Implementation , 2011 .

[36]  Prue F. E. Addison,et al.  Practical solutions for making models indispensable in conservation decision‐making , 2013 .

[37]  A. Peterson Ecological niche conservatism: a time‐structured review of evidence , 2011 .

[38]  G. De’ath Boosted trees for ecological modeling and prediction. , 2007, Ecology.

[39]  Volker Grimm,et al.  Ecological models supporting environmental decision making: a strategy for the future. , 2010, Trends in ecology & evolution.

[40]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[41]  M. Lenzi,et al.  Assessment of resuspended matter and redistribution of macronutrient elements produced by boat disturbance in a eutrophic lagoon. , 2013, Journal of environmental management.

[42]  Gretchen G. Moisen,et al.  A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa , 2008 .

[43]  P. Ehlers Blue growth and ocean governance—how to balance the use and the protection of the seas , 2016 .

[44]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[45]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[46]  Eve McDonald-Madden,et al.  Predicting species distributions for conservation decisions , 2013, Ecology letters.

[47]  Jane Elith,et al.  Error and uncertainty in habitat models , 2006 .

[48]  T. Dawson,et al.  Selecting thresholds of occurrence in the prediction of species distributions , 2005 .

[49]  J. Friedman Stochastic gradient boosting , 2002 .

[50]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[51]  Brendan P. Brooke,et al.  On the use of abiotic surrogates to describe marine benthic biodiversity , 2010 .

[52]  J. Rodríguez,et al.  The application of predictive modelling of species distribution to biodiversity conservation , 2007 .

[53]  Mark A. Burgman,et al.  Treatment of uncertainty in conservation under climate change , 2013 .

[54]  Richard M Cowling,et al.  Designing Systematic Conservation Assessments that Promote Effective Implementation: Best Practice from South Africa , 2006, Conservation biology : the journal of the Society for Conservation Biology.