Using sclerochronology to estimate the growth rate in Pinna nobilis: a case study of Les Alfaques Bay population (Ebro Delta, Spain).

Pinna nobilis is the biggest Mediterranean bivalve, endemic and semi-infaunal. Provide hard substrates to colonize, increasing the spatial heterogeneity of the softbottom communities. P. nobilis suffer a drastic decline due to the anthropogenic pressures. It’s included in the Habitats Directive, in the Barcelona Convention, and in the red lists of many Mediterranean countries. Estimates the growth rate allows to understand the population dynamics of species and yield knowledge to improve protection efforts. In this study a new methodology based on sclerochronology was used to estimate the age and the growth rate of a P. nobilis population located in Les Alfaques bay. The shells of 35 specimens were cataloged. A subsample of 20 individuals was selected, and one valve of each specimens was cut into radial sections along PAMS (Posterior Adductor Muscle Scar) to study the inner register. Thus, the positions of PAMS obscured by nacre were identified, and the number of missing records was estimated by the width of the calcitic layer in the anterior part of the shell. The first growth curve for the Les Alfaques bay population was calculated from the length/age data. To simulate the growth rate of this population, the growth model based on the modified Von Bertalanffy equation was used. Shallow water usually hosts small sized populations of P. nobilis, while in deeper waters specimens reaches larger size. In Les Alfaques bay the population is composed by large size individuals though it’s located in shallows waters. This unusual size pattern is probably due to a sand bar that offers protection from hydrodynamic stress, allowing individuals to elongate more. This study contributes to the knowledge on P. nobilis biology and, with the aim to monitor this species, the growth curve could be used as baseline for future studies on habitat characteristics that may affect the population structure and dynamics in Les Alfaques Bay.

Flower cover and bee diversity estimate by drone RGB images

Worldwide, biodiversity is decreasing due to climate change, habitat fragmentation and agricultural intensification. Bees are essential crops pollinator, but their abundance and diversity are decreasing as well. For their conservation, it is necessary to assess the status of bee population. Field data collection methods are expensive and time consuming thus, recently, new methods based on remote sensing are used. In this study we tested the possibility of using flower cover diversity estimated by UAV images (FCD-UAV) to assess bee diversity and abundance in 10 agricultural meadows in the Netherlands. In order to do so, field data of flower and bee diversity and abundance were collected during a campaign in May 2021. Furthermore, RGB images of the areas have been collected using Unmanned Aerial Vehicle (UAV) and post-processed into orthomosaics. Lastly, Random Forest machine learning algorithm was applied to estimate FCD of the species detected in each field. Resulting FCD was expressed with Shannon and Simpson diversity indices, which were successively correlated to bee Shannon and Simpson diversity indices, abundance and species richness. The results showed a positive relationship between FCD-UAV and in-situ collected data about bee diversity, evaluated with Shannon index, abundance and species richness. The strongest relationship was found between FCD (Shannon Index) and bee abundance with R2=0.52. Following, good correlations were found with bee species richness (R2=0.39) and bee diversity (R2=0.37). R2 values of the relationship between FCD (Simpson Index) and bee abundance, species richness and diversity were slightly inferior (0.45, 0.37 and 0.35, respectively). Our results suggest that the proposed method based on the coupling of UAV imagery and machine learning for the assessment of flower species diversity could be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and of the habitat quality for bees.