Quantifying the known unknowns: estimating maximum intrinsic rate of population increase in the face of uncertainty

Abstract

Sensitivity to overfishing is often estimated using simple models that depend upon life history parameters, especially for species lacking detailed biological information. Yet, there has been little exploration of how uncertainty in life history parameters can influence demographic parameter estimates and therefore fisheries management options. We estimate the maximum intrinsic rate of population increase (rmax) for ten coastal carcharhiniform shark populations using an unstructured life history model that explicitly accounts for uncertainty in life history parameters. We evaluate how the two directly estimated parameters, age at maturity αmat and annual reproductive output b, most influenced rmax estimates. Uncertainty in age at maturity values was low, but resulted in moderate uncertainty in rmax estimates. The model was sensitive to uncertainty in annual reproductive output for the least fecund species with fewer than 5 female offspring per year, which is not unusual for large elasmobranchs, marine mammals, and seabirds. Managers and policy makers should be careful to restrict mortality on species with very low annual reproductive output <2 females per year. We recommend elasmobranch biologists to measure frequency distributions of litter sizes (rather than just a range) as well as improving estimates of natural mortality of data-poor elasmobranchs.

Life History, Growth, and Reproductive Biology of Four Mobulid Species in the Bohol Sea, Philippines

“…Realistic scenarios for M. japanica's r preg are based on the mean size of pregnant females, while estimations based on sizeat-pregnancy resulted in implausible values described by Pardo et al (2018). Similarly, realistic scenarios for M. thurstoni in the present study are based on the weaker prior from Pardo et al (2016a), and prior from Cuevas-Zimbrón et al (2013), and estimation based on the strong prior were excluded due to implausible values.…”

“…2) Intrinsic population growth rate estimation. These age values were then used to estimate the intrinsic population growth rate r for mobulids based on the updated Euler-Lotka equation (Pardo et al, 2016a(Pardo et al, ,b, 2018)…”

Population productivity of shovelnose rays: Inferring the potential for recovery

“…The annual reproductive output and age at maturity were highly uncertain parameters, while the natural mortality was estimated indirectly, which can result in additional uncertainty [13]. Model parameters were drawn from their respective distributions iteratively 20,000 times [71]. To incorporate uncertainty into M , for each iteration the values for α mat , α max and k were drawn from their respective distributions, and used to estimate natural mortality for the four natural mortality estimators, which in turn is required to estimate r max [71].…”

“…One commonly used metric of productivity is the maximum intrinsic rate of population increase r max , which reflects the theoretical maximum growth rate of depleted populations in the absence of density dependent regulation [69]. This method can help to infer and understand a species ability to recover from population declines, and provide the demographic basis for evaluating the sustainability of fisheries [70] and international trade, particularly for poorly monitored species with limited available life-history information [71, 72]. The maximum intrinsic population rate of population increase has previously been estimated for Pseudobatos horkelii and Pseudobatos productus as a part of multi-species comparison [72, 73], however there has not been a comprehensive analysis on the population productivity for shovelnose rays.…”

First observations on captive hatching and incubation period of the yellow‐nose skateDipturus chilensis(Rajiformes: Rajidae), from the South‐Eastern Pacific Ocean

“…Life‐history traits are useful for informing fishery managers and conservation biologists about the capacity of a stock to withstand a given exploitation scenario (Hoenig & Gruber, ; Montealegre‐Quijano et al, ; Pardo et al, ). These data are especially relevant for elasmobranchs, which are known to be less resilient to increases in mortality rates and thus, more vulnerable to overfishing (Dulvy et al, ; Kindsvater et al, ).…”