Abstract
Habitat restoration is an important tool for managing degraded ecosystems, yet the success of restoration projects depends in part on adequately identifying preferred sites for restoration. Species distribution modeling using a machine learning approach provides novel tools for mapping areas of interest for restoration projects. Here we use stacked-species distribution models (s-SDMs) to identify candidate locations for installment of manmade reefs, a useful management tool for restoring structural habitat complexity and the associated biota in marine ecosystems. We created species distribution models for 21 species of commercial, recreational, ecological, or conservation importance within the Southern California Bight based on observations from long-term reef surveys combined with high resolution (200 x 200m) geospatial environmental data layers. We then combined the individual species models to create a stacked-species habitat suitability map, identifying over 800 km2 of potential area for reef restoration within the Bight. When considering only the 21 focal species, s-SDM scores were positively associated with observed bootstrap species richness not only on natural reefs (linear model: slope = 0.27, 95% CI = 0.17 - 0.36, w = 1), but also this result was supported by two independent test datasets. The predicted richness from this linear model was associated with observed species richness when considering only the focal species on manmade reefs (linear model: slope = 0.52, 95% CI = 0.13 - 0.92, w = 1) and also when considering 204 other non-focal species on both natural and manmade reefs in southern California (slope = 3.65, 95% CI = 2.93 - 4.37, w = 1). Finally, our results demonstrate that the existing manmade reefs included in our study on average are located in regions with habitat suitability that is not only less suitable than natural reefs (t-value = -5.4; p < 0.05) only, but also only slightly significantly better than random (p < 0.05), demonstrating a need for more biologically informed placement of manmade reefs. The stacked-species distribution model provides insight for marine restoration projects in southern California specifically, but more generally this method can also be widely applied to other types of habitat restoration including both marine and terrestrial.
Highlights
Increasing habitat loss and degradation worldwide threatens many of the world’s species (Foley et al, 2005), resulting in population declines (Bender et al, 1998), loss of genetic diversity (Sih et al, 2000), and even species extinctions (Barnosky et al, 2011)
To assess the quality of the Species Distribution Models (SDMs) for identifying high quality habitat for restoration, we evaluated the extent to which the species distribution models (s-SDMs) is associated with species richness
Improving the success of habitat restoration projects is a necessity as ecosystems worldwide continue to face increasing anthropogenic pressures and habitat loss
Summary
Increasing habitat loss and degradation worldwide threatens many of the world’s species (Foley et al, 2005), resulting in population declines (Bender et al, 1998), loss of genetic diversity (Sih et al, 2000), and even species extinctions (Barnosky et al, 2011). Habitat restoration is an important tool for managing degraded ecosystems (Polak and Saltz, 2011), in an effort to restore and prevent species loss (Pavlik, 1996). One key factor that influences the success of habitat restoration projects is the quality of sites chosen for management (Bottin et al, 2007). Manmade habitat structures can fail when placed in areas with non-ideal environmental conditions (e.g., Frissell and Nawa, 1992). To prevent such failures, it is crucial that we develop and test methods for identifying candidate sites for habitat restoration
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