Effects of Temperature and Spatial Scale on Rio Grande Cutthroat Trout Growth and Abundance

Abstract

Diversity in habitat and life-history strategies promote a species' long-term persistence. However, life-history strategies are most commonly studied at broad spatial and temporal scales. We applied longevity growth models and closed N-mixture models to examine within- versus between stream variability in life-history characteristics of Rio Grande Cutthroat Trout in northern New Mexico streams. We developed a von Bertalanffy growth model and a closed N-mixture model in a hierarchical Bayesian framework to examine the importance of fine-scale variability in temperature and density-dependence on growth and abundance. The model indicated that accumulation of degree days likely positively influenced instantaneous growth rates and, to a lesser extent, negatively affected asymptotic body length. A nonlinear response of abundance to temperature was also observed, suggesting that Cutthroat Trout productivity along the temperature continuum was affected by physiological limitations ( optimal growth temperatures). Parameter variability was greatest at the segment level for asymptotic size and abundance, but greatest at the stream level for the rate at which asymptotic size is reached. In total, the results suggest that fine-scale habitat heterogeneity ( temperature) may play important roles in the continued persistence of Rio Grande Cutthroat Trout. Management actions should, therefore, consider the role of fine-scale processes for improving the likelihood of future population persistence.

Intrinsic and extrinsic drivers of life‐history variability for a south‐western cutthroat trout

“…We fit the Lester model to RGCT otolith data published in Huntsman et al. (2018), where otoliths were extracted from 247 RGCT in 2010 from one warm site (site 2; n = 98) and two cool sites (site 1; n = 88 and site 3; n = 61). Aged otoliths and RGCT lengths were then incorporated into a biphasic growth model with the following form (Wilson et al., 2018):where l t is length at time t (year), h is immature growth rate (mm length per year), t 1 is the Lester model hypothetical age‐at‐length 0, T is age‐at‐maturity, is the asymptotic size parameter, k is the rate at which is reached and t 0 is the von Bertalanffy hypothetical age‐at‐length 0.…”

“…We used the mean temperature and stream discharge between sampling occasions to model vital rates, similar to approaches taken for modelling seasonal vital rates of other trout populations (Letcher et al., 2015). Degree days were used instead of mean daily temperature to constrain our transition rate (Ψ) because this demographic rate is a proxy for instantaneous growth and development and degree days are commonly used to model temperature effects on growth in fishes (Huntsman et al., 2018; Ward et al., 2017). Degree days were calculated based on the following equation:where Temp is mean daily temperature for day x of n total days and is the minimum temperature at which growth typically occurs in cutthroat trout (5°C, Coleman & Fausch, 2007; USFWS, 1998).…”

“…Most evidence suggests that RGCT population dynamics are shaped by extrinsic environmental conditions (Bakevich et al., 2019; Huntsman, Martin, & Patten, 2018). For example, air temperature has increased and precipitation has decreased throughout the intermountain West in recent decades (Isaak et al, 2012; Zeigler, Todd, & Caldwell, 2012), resulting in streams once supporting healthy RGCT populations going intermittent (Zeigler et al., 2012).…”

“…Furthermore, cutthroat trout recruitment is limited by cool temperatures in high‐elevation streams (Coleman & Fausch, 2007), which may partially explain the quadratic relationship between RGCT abundance and temperature found by Huntsman et al. (2018). Regulation around an internal carrying capacity set by competition for limiting resources ( e.g .…”

Predicting ecosystem components in the Gulf of Mexico and their responses to climate variability with a dynamic Bayesian network model

“…Similarly, we were not able to detect significant impact on the red snapper, following increases in temperature. According to [54], changes in coastal wetland habitats due to sea level rise and changes in rainfall and freshwater flow patterns may be among the most important drivers of climate change impact on species like the red snapper. Generally, red snapper are widely distributed although they have a high affinity for certain habitat types at various stages in their development [55]; thus, we would not necessarily expect their population dynamics to be specifically sensitive to temperature.…”