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

How to efficiently determine the size at maturity of small-sized tropical fishes : a case study based on 144 species identified via DNA barcoding from southwestern Madagascar

In order to provide biological evidence of the real impact of mosquito seine nets in southwestern Madagascar, an efficient procedure for determining the size at maturity of small-sized tropical fishes was developed. The fishes caught by two small-scale fishermen were studied between October 2017 and April 2018. One catch per day was analyzed three days per month during the full-moon period. In the laboratory, fishes were all sorted by morphospecies, photographed and measured. One individual per morphospecies was selected for being identified using CO1 DNA barcoding. A total of 34,051 individual fishes belonging to 144 DNA bacoded species from 48 families was obtained from 42 samples, 467 individuals from 22 morphospecies that had not been successfully barcoded were excluded from the analyses. The macroscopic observations of 8,143 individuals between 0.7 and 10 cm SL indicated the proportion of individuals with clearly observable gonads was 15% only.Among the 144 species identified via DNA barcoding, 83 consisted of individuals that were all without clearly observable gonads, seven of individuals that were all with clearly observable gonads and 54 included of individuals with and without clearly observable gonads. As the determination of L-50 using logistic general linear models failed for most species, the minimum size at maturity was retained to determine the proportion of juveniles and adults for these 54 species. Compared to the data available in FishBase, the minimum size at maturity appears more adequate to discrimine juvenile from adult fish of small-sized tropical species

Spatial and interannual variability of presettlement tropical fish assemblages explained by remote sensing oceanic conditions

Understanding the interannual effect of various environmental factors on biodiversity distribution is fundamental for developing biological monitoring tools. The interannual variability of environmental factors on presettlement fish assemblages (PFAs) has been so far under investigated, especially in Madagascar. Numerous explanatory variables including local hydro-dynamic conditions recorded during the sampling night, characteristics of the benthic substrate and remotely sensed oceanic conditions (RSOC) were used to explain the spatio-temporal variability of PFAs in southwestern Madagascar. Gradient forest analyses were used to hierarchically classify the effect of these explanatory variables on the PFAs for two sites and during two different recruitment seasons. RSOC variables appeared to better explain the PFAs than the local variable and the characteristics of the benthic substrate. The PFAs caught in water masses with coastal characteristics were better explained than those with open water characteristics. This spatial variability is hypothesised to be linked to differences in feeding conditions among water masses. The gradient forest analyses also highlighted the complexity of predicting PFAs as the species for which abundances were better explained by RSOC variables varied between years. This interannual variability was mainly explained by the interannual variation of chlorophylla(Chl a) concentration, wind and surface current, with better prediction obtained during the year with high Chl a values associated with high averaged sea surface temperature. These findings suggest the importance of forecasting Chl a concentrations, taking into account the impact of tropical storms and climate variability in order to predict PFAs in the future