Jointly estimating spatial sampling effort and habitat suitability for multiple species from opportunistic presence-only data

A Hierarchical Model to Estimate Fish Abundance in Alpine Streams by using Removal Sampling Data from Multiple Locations...

Abstract: We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout (Salmo trutta fario) by using removal sampling and biometric data

the obtained segmentation maps is combined with the pixel- wise classification map using the majority voting principle: For every region in the segmentation map, all the pixels

By combining these two datasets, our approach estimates the relative abundance of a set of species in a set of sites, while accounting for the different detectability of the species

PEM = 0.80 OBTY + 1.10 LECO – 1.20 AREP – 0.30 QTUM – 0.70 QTUA + 1.80 KLDS – 0.90 KEXT + 1.86 TOOL – 3.30 (1) where PEM is the effort estimated by the proposed method

Our main results are formal Eqs (2) and (5) which express the target of the density estimate in the environmental space as a function of the true focal species density f,

We found that our method to jointly estimate densities of multiple species, with a spatial function representing a common sampling effort, provides unbiased estimation

Title: Evaluating and benchmarking biodiversity monitoring: metadata-based indicators for sampling design, sampling effort and data analysis5. Authors: Szabolcs LENGYEL a *,