Inferring the presence and abundance of rare waterbirds species from scarce data
Abstract
Abundance data are used in ecology for species monitoring and conservation. These count data often display several specific characteristics like numerous missing data, high variance, and a high proportion of zeros, particularly when monitoring rare species. We present a model that aims to impute missing data and estimate the effect of covariates on species presence and abundance. It is based on the log-normal Poisson model, which offers more flexibility in the variance of counts than a Poisson model. A latent variable is added for the overrepresentation of zeros in the data. The imputation of missing data is made possible by assuming that the latent variance matrix has low rank and the inclusion of covariates. We demonstrate the identifiability in the presence of missing data. Since maximum likelihood inference is intractable, we use a variational expectation-maximization algorithm to infer the parameters. We provide an estimate of the asymptotic variance of the estimators and derive prediction intervals for the imputations, an estimate of the temporal trend, and a procedure for detecting a potential change in this trend. We evaluate our imputations and associated prediction intervals using artificially degraded monitoring data set. We conclude with an illustration on a monitoring waterbirds data set.
Related
- Bayesian hypergraph inference from scarce and noisy dynamical observations
- What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
- Spending Scarce Confirmatory PET Measurements: Target-Aligned Validation in A4/LEARN
- Inferring geometric structure from eigenfunction triple products
- Inferring constitutive forces from noisy Allen-Cahn movies with moment equations
BibTeX
@misc{bricout2026inferring,
title = {{Inferring the presence and abundance of rare waterbirds species from scarce data}},
author = {Barbara Bricout and Laura Dami and Pierre Defos Du Rau and Sophie Donnet and Thomas Galewski and Stéphane Robin},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2602.10673},
eprint = {2602.10673},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05830337v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Inferring the presence and abundance of rare waterbirds species from scarce data
AU - Barbara Bricout
AU - Laura Dami
AU - Pierre Defos Du Rau
AU - Sophie Donnet
AU - Thomas Galewski
AU - Stéphane Robin
PY - 2026
DA - 2026/10/21
PB - HAL
DO - 10.48550/arxiv.2602.10673
UR - https://hal.science/hal-05830337v1
AB - Abundance data are used in ecology for species monitoring and conservation. These count data often display several specific characteristics like numerous missing data, high variance, and a high proportion of zeros, particularly when monitoring rare species. We present a model that aims to impute missing data and estimate the effect of covariates on species presence and abundance. It is based on the log-normal Poisson model, which offers more flexibility in the variance of counts than a Poisson model. A latent variable is added for the overrepresentation of zeros in the data. The imputation of missing data is made possible by assuming that the latent variance matrix has low rank and the inclusion of covariates. We demonstrate the identifiability in the presence of missing data. Since maximum likelihood inference is intractable, we use a variational expectation-maximization algorithm to infer the parameters. We provide an estimate of the asymptotic variance of the estimators and derive prediction intervals for the imputations, an estimate of the temporal trend, and a procedure for detecting a potential change in this trend. We evaluate our imputations and associated prediction intervals using artificially degraded monitoring data set. We conclude with an illustration on a monitoring waterbirds data set.
ER -