Ecological Modelling 303 (2015) 55–69 Contents lists available at ScienceDirect Ecological Modelling journa l h om epa ge: www.elsevier.com/locate/ecolmodel Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM) Wei Wu a,∗ , Kevin M. Yeager b , Mark S. Peterson a , Richard S. Fulford c a Department of Coastal Sciences, Gulf Coast Research Laboratory, The University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS 39564, USA b Department of Earth & Environmental Sciences, University of Kentucky, Lexington, KY 40506, USA c US EPA Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA a r t i c l e i n f o Article history: Received 5 November 2014 Received in revised form 11 February 2015 Accepted 12 February 2015 Keywords: SLAMM Neutral models Land persistence Coastal wetlands Sea-level rise a b s t r a c t A commonly used landscape model to simulate wetland change – the Sea Level Affecting Marshes Model (SLAMM) – has rarely been explicitly assessed for its prediction accuracy. Here, we evaluated this model using recently proposed neutral models – including the random constraint match model (RCM) and grow- ing cluster model (GrC), which consider the initial landscape conditions instead of starting with a blank or randomized initial map as traditional neutral models do. Thus, the SLAMM’s performance, due to pro- cesses accounted for in the model, could be more accurately assessed. RCM allocates change randomly in space, while in the GrC, change allocation is prioritized at the locations with pairs of to-be-increased land type and to-be-reduced land type adjacent to each other. The metrics we applied to evaluate the SLAMM vs. the neutral models accounted for five main components in map comparison: (1) reference change simulated correctly as change (hits), (2) reference persistence simulated correctly as persistence (correct rejections), (3) reference change simulated incorrectly as change to the wrong category (wrong hits), (4) reference change simulated incorrectly as persistence (misses), and (5) reference persistence simulated incorrectly as change (false alarms). These methods improved the way that we currently evaluate land change models, where we either do not compare to a neutral model, or the neutral model does not have the same boundary conditions and constraints as the assessed dynamics models. The results showed that the SLAMM could simulate wetland change more accurately compared to the GrC and RCM at a 10-year time step for the lower Pascagoula River basin, Mississippi, with higher hits and correct rejections, and lower misses and false alarms. The magnitude of simulated changes using the SLAMM was 46% of refer- ence changes. The number of wrong hits for the SLAMM was also lower than those for the neutral models after combining some land or water types into broader categories. After the aggregation, the SLAMM per- formance improved substantially. How the errors of this relatively short-term simulation propagate into longer-term predictions requires further investigation. This study also showed the importance of imple- menting elevation data with high vertical accuracy, and conducting local calibration when we apply the SLAMM. © 2015 Published by Elsevier B.V. 1. Introduction Coastal wetlands are dynamic landforms that are subject to changes due to factors from the upland and the ocean. One of the most important of these factors is accelerated relative sea-level rise (RSLR). RSLR is driven by a myriad of geological and climatological processes, including the melting of ice sheets, the thermal expansion of water as a result of global warming, and the ∗ Corresponding author. Tel.: +1 228 818 8855. E-mail address: wei.wu@usm.edu (W. Wu). subsidence of the marsh surface due to sediment compaction or tectonic processes. Coastal wetlands can become submerged and will disappear if marsh surface vertical accretion rates do not keep up with the rising rates of relative sea level; thus, are potentially vulnerable to accelerated RSLR. Wetland losses can detrimentally impact coastal ecosystems by increasing their vulnerability to storm surge and flooding (e.g., Lee et al., 1992; Nicholls et al., 1999; Zhang et al., 2012; Barbier et al., 2013), coastline retreat, changes in nutrient cycling (e.g., Bruland, 2008; Perez et al., 2011; Ardón et al., 2013), declines in net primary and secondary productivity (Day et al., 1997; Martin et al., 2000), salt water intrusion (Warne and Stanley, 1993; Martin et al., 2000; Day, 2005), fluctuations http://dx.doi.org/10.1016/j.ecolmodel.2015.02.008 0304-3800/© 2015 Published by Elsevier B.V.