Estimating abundance of endangered fish by eliminating bias from non-constant detectability

• Main Authors: Stewart, DR, Butler, MJ, Harris, G, Johnson, LA, Radke, WR
• Format: Article
• Language: English
• Published: Inter-Research 2017-02-01
• Series: Endangered Species Research
• Online Access: https://www.int-res.com/abstracts/esr/v32/p187-201/

Worldwide, approximately half of all freshwater fish are threatened with extinction or lack sufficient data to classify their conservation status. We focused on 3 such species endemic to southeastern Arizona, USA, and Sonora, Mexico: Yaqui topminnow Poeciliopsis occidentalis sonoriensis, Yaqui chub Gila purpurae, and beautiful shiner Cyprinella formosa. These species, like many others, require accurate estimates and trends of abundance to characterize their conservation status. Hence, sampling must be designed appropriately. We used historical data to determine precision and minimum number of traps necessary to estimate abundance with relative precision ≤25. Next, we examined alternative trap soak times to improve sampling efficiency. We then incorporated variables influencing detectability to produce unbiased abundance estimates. Finally, we simulated how ignoring biases from heterogeneous detectability affects abundance indices and the ability to detect changes in abundance. We found catch estimated from the historical design, which assumed constant detectability, had much variability, thereby requiring ~40 traps pond-1. A 4 h soak duration increased catch for 2 species and detection probability for all. Detectability increased while abundance decreased with water temperature (all 3 species). Detection of beautiful shiner declined as abundance increased with depth. Our simulations indicated that if detectability varies but is assumed constant (i.e. ignoring detection probability), the probability of finding population change when none occurred (i.e. Type I error) is 50%, and the probability of detecting true population change (i.e. power) declines to ~0.75. Ignoring variable detectability in count data can misrepresent species status, habitat relationships, and ultimately mislead conservation and stewardship of endangered species.