The USGS is offering a funding opportunity to a CESU partner for developing research to assess the vulnerability of species to environmental change is an important management challenge, particularly for poorly studied species for which species status assessments are required.

Agencies such as

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the Fish and Wildlife Service are tasked with assessing species sensitivities to environmental conditions, their exposure to, and ability to adapt to changing conditions.

Yet, defensible assessments currently require detailed knowledge of species-specific traits and ecologies and this information is hard to come by.

Vulnerability assessments for lesser-studied species can be extremely challenging.

Most vulnerability assessment methods and frameworks are developed using well-studied species and their applicability to species with poorly understood traits and ecologies is questionable.

Advances in machine learning and statistical clustering can provide new ways of simply and defensibly assessing sensitivity and adaptive capacity for priority species.

A predictive model and associated classification tree can provide an accessible, transparent, and repeatable means of the vulnerability for lesser studied species, lessening the research burden of agencies and staff.