Fish community diversity in a coastal zone at southeastern Gulf of California,Mexico

Although the southeast region of the Gulf of California has a high fish diversity, due to the high biological productivity, the coastal area of Nayarit has few studies in this regard. The main objective of this work is to describe the variability of the structure of the ichthyofauna in the coastal zone of Nayarit during an annual cycle. Biological samples were collected at 10 stations during February, May, July, and December 2014. The temperature, depth, salinity, and organic material and carbonates in sediments were also recorded. The analysis of diversity includes three facets: ecological, taxonomic, and functional. A total of 82 species belonging to 56 genera, 31 families, 11 orders, and two classes were identified. The most abundant species included Selene peruviana, Stellifer wintersteenorum, Cathorops sp., and Larimus argenteus. Of the total of identified species, 62% were considered as rare according to their abundance and frequency. Although the environmental variables analyzed were variable, all diversity indices did not reveal an evident spatio-seasonal pattern. Likewise, most values of average taxonomic distinctness presented the expected values. However, some values showed a low taxonomic diversity. The indices of functional diversity showed a stable functional richness and redundancy in the attributes of the species.

     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.