Migration timing and tributary use of spawning flannelmouth sucker (Catostomus latipinnis).

Spawning phenology and associated migrations of fishes are often regulated by factors such as temperature and stream discharge, but flow regulation of mainstem rivers coupled with climate change might disrupt these cues and affect fitness. Flannelmouth sucker (Catostomus latipinnis) persisting in heavily modified river networks are known to spawn in tributaries that might provide better spawning habitat than neighboring mainstem rivers subject to habitat degradation (e.g., embedded sediments, altered thermal regimes, and disconnected floodplains). PIT tag data and radio telemetry were used to quantify the timing and duration of flannelmouth sucker tributary spawning migrations in relation to environmental cues in McElmo Creek, a tributary of the San Juan River in the American Southwest. We also tested the extent of the tributary migration and assessed mainstem movements prior to and after tributary migrations. Additionally, multiyear data sets of PIT detections from other tributaries in the Colorado River basin were used to quantify interannual and cross-site variation in the timing of flannelmouth sucker spawning migrations in relation to environmental cues. The arrival and residence times of fish spawning in McElmo Creek varied among years, with earlier migration and a 3-week increase in residence time in relatively wet years compared to drier years. Classification tree analysis suggested a combination of discharge- and temperature-determined arrival timing. Of fish PIT tagged in the fall, 56% tagged within 10 km of McElmo Creek spawned in the tributary the following spring, as did 60% of radio-tagged fish, with a decline in its use corresponding to increased distance of tagging location. A broader analysis of four tributaries in the Colorado River basin, including McElmo Creek, found photoperiod and temperature of tributary and mainstem rivers were the most important variables in determining migration timing, but tributary and mainstem discharge also aided in classification success. The largest tributary, the Little Colorado River, had more residential fish or fish that stayed for longer periods (median = 30 days), whereas McElmo Creek fish stayed an average of just 10 days in 2022. Our results generally suggest that higher discharge, across years or across sites, results in extended use of tributaries by flannelmouth suckers. Conservation actions that limit water extraction and maintain natural flow regimes in tributaries, while maintaining open connection with mainstem rivers, may benefit migratory species, including flannelmouth suckers.

A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states.

Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: (1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples and (2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2 . Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Last, we show that occupancy and site-abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates. Marginalized code was anywhere from five to >1,000 times faster than discrete code and differences in inferences were minimal. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance are minimally affected by marginalization. In the case of abundance, marginalized code is both quicker and has lower bias than an N-augmentation approach. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.

Inferring species interactions through joint mark-recapture analysis.

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark-recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark-recapture models that allow for joint modeling of interacting species. Here, we introduce a two-species mark-recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub (Gila cypha) and introduced rainbow trout (Oncorhynchus mykiss) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long-term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark-recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Using variance components to estimate power in a hierarchically nested sampling design.

We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007-2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components: (1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design.