Environmental impact assessment for large carnivores: a methodological review of the wolf (Canis lupus) monitoring in Portugal

The continuous growth of the global human population results in increased
use and change of landscapes, with infrastructures like transportation or
energy facilities, being a particular risk to large carnivores.
Environmental Impact Assessments were established to identify the probable
environmental consequences of any new proposed project, find ways to
reduce impacts, and provide evidence to inform decision making and
mitigation. Portugal has a wolf population of around 300 individuals,
designated as an endangered species with full legal protection. They
occupy the northern mountainous areas of the country which has also been
the focus of new human infrastructures over the last 20 years.
Consequently, dozens of wolf monitoring programs have been established to
evaluate wolf population status, to identify impacts, and to inform
appropriate mitigation or compensation measures. We reviewed Portuguese
wolf monitoring programs to answer four key questions: do wolf programs
examine adequate biological parameters to meet monitoring objectives? is
the study design suitable for measuring impacts? are data collection
methods and effort sufficient for the stated inference objectives? and do
statistical analyses of the data lead to robust conclusions? Overall, we
found a mismatch between the stated aims of wolf monitoring and the
results reported, and often neither aligns with the existing national wolf
monitoring guidelines. Despite the vast effort expended and the diversity
of methods used, data analysis makes almost exclusive use of relative
indices or summary statistics, with little consideration of the potential
biases that arise through the (imperfect) observational process. This
makes comparisons of impacts across space and time difficult and is
therefore unlikely to contribute to a general understanding of wolf
responses to infrastructure-related disturbance. We recommend the
development of standardized monitoring protocols and advocate for the use
of statistical methods that account for imperfect detection to guarantee
accuracy, reproducibility, and efficacy of the programs.