Designing an optimized landscape restoration with spatially interdependent non-linear models.

Brazilian Atlantic Forest is a biodiversity hotspot drastically fragmented due to different land use practices. Our understanding on the impacts of fragmentation and restoration practices on ecosystem functionality significantly increased during the last decades. However, it is unknown to our knowledge how a precision restoration approach, integrated with landscape metrics, will affect the decision-making process of forest restoration. Here, we applied Landscape Shape Index and Contagion metrics in a genetic algorithm for planning forest restoration in watersheds at the pixel level. We evaluated how such integration may configure the precision of restoration with scenarios related to landscape ecology metrics. The genetic algorithm worked toward optimizing the site, shape, and size of forest patches across the landscape according to the results obtained in applying the metrics. Our results, obtained by simulations of scenarios, support aggregation of forest restoration zones as expected, with priority restoration areas indicated where most of the aggregation of forest patches occurs. Our optimized solutions for the study area (Santa Maria do Rio Doce Watershed) predicted an important improvement of landscape metrics (LSI = 44 %; Contagion/LSI = 73 %). Largest shifts are suggested based on LSI (i.e., three larger fragments) and Contagion/LSI (i.e., only one well-connected fragment) optimizations. Our findings indicate that restoration in an extremely fragmented landscape will promote a shift toward more connected patches and with reduction of the surface:volume ratio. Our work explores the use of genetic algorithms to propose forest restoration based on landscape ecology metrics in a spatially explicit innovative approach. Our results indicate that LSI and Contagion:LSI ratio may affect the choice concerning precise location of restoration sites based on forest fragments scattered in the landscape and reinforce the usefulness of genetic algorithms to yield an optimized-driven solution for restoration initiatives.