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

Abstract

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.

Estimating the Effects of Environmental Variables and Gear Type on Panfish Catch and Bycatch in Reservoirs

“…These results were dependent on the coefficient of variation of the sample. However, we believe that it is imperative for fisheries managers to (1) consider sample size requirements and precision goals prior to initiating a survey separately for each impoundment and (2) allocate samples with Thompson 2012;Stewart et al 2017). We found the standard North American-design fyke net to be more effective and efficient at capturing panfishes than the two hoop-net designs used in this study.…”

“…We restricted the table of frequency categories for analysis to those categories containing more than five individuals, and we used Cochran-Mantel-Haenszel (CMH) test statistics (χ 2 ) to draw inferences while controlling for lake, with the Bonferroni correction used to reduce the chance of a type I error (Agresti 2002). We used a multispecies Bayesian mixture model to link the individuals collected from the temporally replicated samples to a latent abundance state at a site within each lake to quantify detection and true local abundance (Stewart et al 2017(Stewart et al , 2019. Therefore, we consider the experimental design where the observed catch histories represent a sequence of replicated counts of unmarked fish (y) for panfish species j at each site i = 1, 2,. .…”

“…We evaluated the minimum number of nets needed to achieve RSE25 for each target species and gear type from each lake (Stewart et al 2017). The target level of RSE25 was based on recommendations by Robson and Regier (1964) and Hardin and Conner (1992).…”

“…The target level of RSE25 was based on recommendations by Robson and Regier (1964) and Hardin and Conner (1992). We used a stochastic resampling technique in program R that randomly sampled two nets from the empirical data for each lake and species 3,000 times with replacement (Stewart et al 2017). Next, the proportion of the 3,000 samples that attained an RSE of 25 or less was calculated (Stewart and Long 2012).…”

Accounting for detectability and abundance in survey design for a declining species

“…This minimum‐abundance survey effort is the appropriate metric if we are to be confident that the species is not present, but has rarely been met when surveying for our species, and is substantially higher than the effort required to detect the species when it is at its historical peak mean per‐site abundance. Failing to account for how detectability decreases as populations decline or as survey conditions change can massively underestimate the survey effort required to achieve confidence in a species' absence or lead to poor estimation of species' abundance (Stewart, Butler, Harris, Johnson, & Radke, ). This bias may cause us to falsely affirm the species' absence at a site and so underestimate the species' occupancy at a regional scale (Cubaynes et al, ; Mazerolle et al, ; Thompson, ).…”