Predicting species richness and abundance of tropical post-larval fish using machine learning

International audiencePost-larval prediction is important, as post-larval supply allows us to understand juvenile fish populations. No previous studies have predicted post-larval fish species richness and abundance combining molecular tools, machine learning, and past-days remotely sensed oceanic conditions (RSOCs) obtained in the days just prior to sampling at different scales. Previous studies aimed at modeling species richness and abundance of marine fishes have mainly used environmental variables recorded locally during sampling and have merely focused on juvenile and adult fishes due to the difficulty of obtaining accurate species richness estimates for post-larvae. The present work predicted post-larval species richness (identified using DNA barcoding) and abundance at 2 coastal sites in SW Madagascar using random forest (RF) models. RFs were fitted using combinations of local variables and RSOCs at a small-scale (8 d prior to fish sampling in a 50 × 120 km 2 area), meso-scale (16 d prior; 100 × 200 km 2 ), and large-scale (24 d prior; 200 × 300 km 2 ). RF models combining local and small-scale RSOC variables predicted species richness and abundance best, with accuracy around 70 and 60%, respectively. We observed a small variation of RF model performance in predicting species richness and abundance among all sites, highlighting the consistency of the predictive RF model. Moreover, partial dependence plots showed that high species richness and abundance were predicted for sea surface temperatures <27.0°C and chlorophyll a concentrations <0.22 mg m -3 . With respect to temporal changes, these thresholds were solely observed from November to December. Our results suggest that, in SW Madagascar, species richness and abundance of post-larval fish may only be predicted prior to the ecological impacts of tropical storms on larval settlement success

Dynamics and fate of blue carbon in a mangrove-seagrass seascape: influence of landscape configuration and land-use change

Context Seagrass meadows act as efficient natural carbon sinks by sequestering atmospheric CO2 and through trapping of allochthonous organic material, thereby preserving organic carbon (C-org) in their sediments. Less understood is the influence of landscape configuration and transformation (land-use change) on carbon sequestration dynamics in coastal seascapes across the land-sea interface. Objectives We explored the influence of landscape configuration and degradation of adjacent mangroves on the dynamics and fate of C-org in seagrass habitats. Methods Through predictive modelling, we assessed sedimentary C-org content, stocks and source composition in multiple seascapes (km-wide buffer zones) dominated by different seagrass communities in northwest Madagascar. The study area encompassed seagrass meadows adjacent to intact and deforested mangroves. Results The sedimentary C-org content was influenced by a combination of landscape metrics and inherent habitat plant- and sediment-properties. We found a strong land-to-sea gradient, likely driven by hydrodynamic forces, generating distinct patterns in sedimentary C-org levels in seagrass seascapes. There was higher C-org content and a mangrove signal in seagrass surface sediments closer to the deforested mangrove area, possibly due to an escalated export of C-org from deforested mangrove soils. Seascapes comprising large continuous seagrass meadows had higher sedimentary C-org levels in comparison to more diverse and patchy seascapes. Conclusion Our results emphasize the benefit to consider the influence of seascape configuration and connectivity to accurately assess C-org content in coastal habitats. Understanding spatial patterns of variability and what is driving the observed patterns is useful for identifying carbon sink hotspots and develop management prioritizations

HILOI, a research program on seagrass ecosystems in islands from the Western Indian Ocean

PosterInternational audiencePoster about HILOI, a research program on seagrass ecosystems in islands from the Western Indian Ocea