An introduced predator affects the demography of an evolutionarily naïve species

An introduced predator affects the demography of an evolutionarily naïve species | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 October 2025 V1 Latest version Share on An introduced predator affects the demography of an evolutionarily naïve species Authors : Josh Rasmussen 0000-0002-9943-1892 [email protected] , Eric Billman , Jerry Johnson , Brenden Mikel Orocu 0009-0002-3277-5233 , Robert Richardson , Jaime Zuniga-Vega 0000-0002-9661-1521 , and Mark Belk 0000-0002-0576-0717 Authors Info & Affiliations https://doi.org/10.22541/au.175965316.66008925/v1 205 views 106 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Invasive predators often cause extirpation of evolutionarily naïve native species, but sometimes coexistence occurs. To understand the demography of an evolutionarily naïve fish species that sometimes coexists with an invasive predator, we collected mark-recapture data and size-frequency data of two populations of southern leatherside chub (Lepidomeda aliciae). One population has been invaded by nonnative brown trout (Salmo trutta). The other population has not been invaded by nonnative trout. In each population, we estimated vital rates from mark-recapture data to inform a stage-structured matrix transition model. We also used size-frequency distributions from these populations in an integral projection model. Southern leatherside chub from the predator-free environment exhibited higher survival (except age-0) and lower average realized fecundity than the population from the predator environment. Survival rates of age-0 in the predator environment were double the rates in the predator-free habitat. Growth transitions from the smallest size class and reproduction at medium sizes accounted for nearly 70% of the weight contributing to population growth in the predator environment, but only 43% in the predator-free population. Our results suggest that invasive brown trout directly reduce abundance and survival of larger southern leatherside chub which indirectly causes an increase in age-0 survival and the demographic value of reproduction at smaller sizes. Our findings highlight the utility of combining discrete and continuous modeling frameworks to assess complex demographic responses. Introduction Predators can act as strong selective agents in shaping the demography and life history dynamics of prey species (Johnson and Zúñiga-Vega 2009, Miller and Rudolf 2011, Gervasi et al. 2012, Walsh et al. 2015). Predation affects prey populations through direct mortality and indirectly by prompting costly defensive strategies, such as changes in behavior, physiology, and habitat use (Creel and Christianson 2008, Wallach et al. 2015, Johnson and Belk 2020, Moseby et al. 2023), or by altering ecosystem conditions, such as trophic cascades or competitive release (Pinto-Coelho et a. 2008, Fraser and Lamphere 2013, Sharpe and Chapman 2014, Richardson et al. 2016, Haberyan 2021). Differences in predation can also drive divergence in life history strategies among populations, if populations experience differential predation pressures (Johnson and Zúñiga-Vega 2009). Such disparities in life history strategies—e.g., reproductive timing, offspring size and number, and growth rates—can lead to variation in demographic rates, including survival and population growth, which can be key indicators of population resilience and long-term persistence (Metcalf and Pavard 2007). The introduction of nonnative predators can especially impact native populations when native species are evolutionarily naïve to the novel predation risk (Kovach et al. 2017, Wayne et al. 2017, Carthey and Blumstein 2018, Van der Weyde et al. 2023, McLean et al. 2025). Evolutionarily naïve species often lack effective antipredator responses (e.g. morphological, physiological, behavioral, or life history strategies) to counteract the effects of the novel predators (Sih et al. 2010, Medina et al. 2011, Brown et al. 2013, Martin 2014, Van der Weyde et al. 2023), and so experience disproportionate effects of predation, relative to non-naïve prey species. Additionally, the effects of predation are not likely to be uniformly experienced across life stages or sizes (Fraser and Lamphere 2013). It is well established that invasive predators can alter prey population dynamics (Mills et al. 2004, Carlsson et al. 2010, Wittmann et al. 2013, Brown et al. 2020), yet the demographic consequences can be complex due to context-dependent ecological interactions. Although extinction is a common outcome for evolutionarily naïve species when exposed to novel predation risk, coexistence can occur (Johnson and Zúñiga-Vega 2009, Sih et al. 2010, Billman et al. 2011, Wallach et al. 2015). According to the age-specific mortality hypothesis, populations that co-occur with predators exhibit higher adult mortality and lower juvenile mortality relative to populations in predator-free environments (Reznick et al. 1996, Johnson and Zúñiga-Vega 2009). Hence, we might expect that predator-naïve populations that coexist with a novel predator would exhibit demographic patterns similar to coevolved prey populations. However, few studies have reported on the demographic response of an evolutionarily naïve species that has achieved coexistence with an invasive predator (Rasmussen et al. 2011). Comparing the demography of allopatric predator-naïve populations to those coexisting with novel predators could clarify aspects of the ecological and evolutionary effects of invasive predation. In this study, we utilized and compared population projection models of two populations of fish experiencing differential predation pressure to assess the demographic consequences of an invasive predator (brown trout; Salmo trutta ) on an evolutionarily naïve species (southern leatherside chub; Lepidomeda aliciae ). Southern leatherside chub are a small-stream fish with populations in adjacent creeks in central Utah, USA. In Lost Creek, nonnative, predatory brown trout have become established, while in Salina Creek, brown trout do not occur (Wilson and Belk 1996, Billman et al. 2013). This system allows us to investigate the demographic patterns that allow the coexistence of an evolutionarily naïve species with an invasive predator. Based on life history theory, we predict that southern leatherside chub in the predator environment will exhibit higher juvenile survival and lower adult survival compared to chub found in the predator-free stream. To test this, we employed two modeling approaches—a matrix transition model based on mark-recapture data and an integral projection model based on size frequency distributions. From these, we derived survival, reproduction, and population growth rates from both sets of data for both populations to determine how an invasive predator shaped the demographics of evolutionarily naïve species. Study System Southern leatherside chub are a rare fish species of the family Leucisidae found in small streams of the eastern Great Basin in central Utah in the western United States (Wilson and Belk 2001, Johnson et al. 2004, Belk and Johnson 2007). They live up to 8 years and reach standard lengths (SL) of 140 mm (Johnson et al. 1995, Rasmussen and Belk 2012). They have one breeding season annually— from mid to late summer (Munro et al. 1990, Johnson et al. 1995). Salina Creek (upstream from the town of Salina, Sevier County, Utah; 38.9044 N 111.69 W, elev. 1807 m) and Lost Creek (just southwest of Salina, Sevier County, Utah; 38.8414 N 111.8625 W, elev. 1746 m) are two adjacent stream systems where populations of southern leatherside chub occur (Figure 1). Both river systems have similar habitat quality such as range of depths (Salina Creek depth range was 0.25–1.5 m and Lost Creek depth range was 0.30–2.0 m during the study period) and the presence of backwaters and side channels (Walser et al. 1999, Wilson and Belk 2001, Billman et al. 2011). Habitats in these streams consist of heterogeneous instream structure, such as deep pools (> 1 m), riffles, including cover from riparian vegetation and undercut banks. Brown trout —a nonnative, piscivorous salmonid— were introduced into Utah in the early 1900s and stocked in streams across Utah where native cutthroat trout ( Oncorhynchus clarkii utah ) were once present (Billman et al. 2011). Southern leatherside chub co-exist with nonnative brown trout in Lost Creek which prey upon southern leatherside chub (Belk and Johnson 2007). Large aquatic predators capable of consuming adult southern leatherside chub are absent in Salina Creek (Billman et al. 2011). Other species present at both sites include speckled dace ( Rhinichthys osculus ), mottled sculpin ( Cottus bairdii ), and mountain sucker ( Catostomus platyrhynchus ; Olsen and Belk 2005, Rasmussen and Belk 2012), none of which prey upon adult southern leatherside chub. Here we refer to the population in Lost Creek with brown trout present as a predator environment and the population in Salina Creek absent of brown trout as a predator-free environment , though it is likely that early life stages of our native species of interest experience predation from other native species, including adult southern leatherside chub. Mark-recapture Design To obtain vital rates for southern leatherside chubs, we conducted a multi-year mark-recapture study in Lost Creek and Salina Creek from 2003 to 2006. In each stream, we established four contiguous 50-m segments and blocked each at the downstream end with nets. Fish were removed from each segment using three successive electrofishing passes with a Smith-Root LR-24 backpack electrofisher (Vancouver, Washington). Segments were sampled sequentially in an upstream direction. Captured fish were sorted into aerated tubs by species; non-target species were released immediately, while southern leatherside chub were retained for measurement and marking. Southern leatherside chub were measured (standard length, SL) and assigned to one of three size classes based on size-at-maturity and known growth patterns (Johnson et al. 1995, Billman et al. 2011). Individuals measuring 40–64 mm SL were marked with Visual Implant Elastomer (VIE; Northwest Marine Technology, Inc., Anacortes, Washington) at the dorsal insertion of the caudal fin. Fish between 65–84 mm SL were marked at the ventral insertion of the caudal fin, and those greater than 84 mm SL were marked at the base of the anal fin. Each year of capture was denoted by a unique elastomer color. Individuals less than 40 mm SL, presumed to be young-of-the-year, were excluded from the study due to the potential for increased marking-related mortality and limited detectability during electrofishing. Visual Implant Elastomer marks have been shown to remain visible in southern leatherside chub for multiple years (Rasmussen and Belk 2012). Because there is no evidence of sexual dimorphism in the species, individuals were not sexed. In 2004, we expanded our sampling efforts beyond the original four 50-m segments. In this year, we sampled additional 50-m segments downstream beginning at distances of 50, 100, 250, and 500 meters from the lower boundary of the original sampling area. Upstream, sampling included segments located at 0, 100, and 200 meters from the uppermost boundary of the original reach. We marked all southern leatherside chub recaptured from previous years captured in these extended segments using the same VIE protocol to indicate size class and year of capture. New captures in the extended segments were not marked. In 2005, upstream and downstream sampling was standardized to 150 meters in each direction from the original four segments, sampled in 50-m increments. Unmarked individuals captured within the original segments were marked according to the same protocol used in previous years. In 2006, our sampling was restricted to Salina Creek, and no new marks were administered that year. Population Projection Models To model the population dynamics of southern leatherside chub, we employed two widely used population projection approaches: a stage-structured matrix model (Caswell 2001); and an integral projection model (IPM; Easterling et al. 2000). Both models are common in ecological research; however, they are rarely applied side-by-side to the same populations using related datasets (Doak et al. 2021). The stage-structured matrix model relies on discrete size or age classes and incorporates vital rates—specifically, survival and fecundity—to project population dynamics over time. These rates are typically derived from capture-recapture data, making this approach informative for identifying the influence of specific life stages on population growth. In contrast, integral projection models are usually built from individual-level capture-recapture data to estimate survival, growth, and reproduction as continuous functions of body size. However, in this study, we use size frequency distributions, rather than mark-recapture data, to parameterize the IPM. Our study system allowed for a direct comparison of the two analytical approaches to assess how an invasive predator influences the demography of an evolutionary naïve species. This comparison provides a framework for evaluating the utility of each model under different datasets. These approaches differ in their data requirements and modeling frameworks, but offer complementary perspectives on population dynamics. Matrix Transition Model To determine the survival rates of the southern leatherside chub, we used Program MARK (White 1996) to estimate survival ( φ ) and recapture ( p ) probabilities for the populations from the two streams. We analyzed the data with the “Multi-strata Recaptures Only” function within Program MARK, to evaluate survival and recapture probabilities similar to a Cormack-Jolly-Seber framework (CJS; Cormack 1964, Jolly 1965, Seber 1965, Lebreton et al. 1992). The CJS model uses log likelihood ( log e L ) to estimate φ and p given the probabilities of the observed encounter histories: \(\log_{\text{e\ }}L\ (\varphi,\ p\ |\ EH)=\left(Х_{j}\right)*log\ \left(\text{Probability\ }\left(\text{EH}_{j}|\ first\ release\right)\right),\ \) where EH are the observed capture histories with n as the number of unique histories observed in the dataset, and X j is the number of individuals with the capture history EH j . The probability of each capture is the product of φ and p across the various capture events and intervening periods. Our Salina Creek data included four capture events, with three intervening periods: 2003-2006. For example, there were eleven individuals that were originally captured in 2003, not captured in 2004, but recaptured in both 2005 and 2006. This gives the capture history of 1011. The probability of this capture history is\(\varphi_{1\ }\left(1-p_{2}\right)\ \varphi_{2\ \ }p_{3}\ \varphi_{3}\ p_{4}\), which incorporates the fact that though the individuals were not captured during the second event they were known to be alive because they were subsequently captured alive. The assumptions of this approach include homogeneity of survival and capture probabilities among individuals within a population or group, but fates of individuals are independent of each other (Lebreton et al. 1992). These are often referred to as the iii assumptions, i.e., independence of fates and identity of rates of individuals. Additional assumptions are that marks are not lost or missed, sampling duration is negligible relative to intervening survival periods, and handling effects are random among individuals (Buckland 1980). See Lebreton et al. (1992) for a more comprehensive description of this model and the iii assumptions. We utilized the multi-strata approach to incorporate an additional estimation of the probability of transition among strata or groups ( ψ ; Arnason 1973, Brownie et al. 1993). Our strata were the body length bins of small, medium, and large individuals. We interpret the ψ parameter to be the proportion of surviving individuals that transition to a larger size class. We fit a single model for each location that included a single mean φ estimate for each size class, a constant p rate across all size groups and years, and single mean ψ from small to medium, small to large, and medium to large. We used a sine link function. The probability of transitioning from a larger stratum to a smaller one (e.g., large to small) was fixed at 0. The model estimated seven parameters. We estimated realized fecundity ( F ) from our 2004 electrofishing samples using the relationship between fish size and number of oocytes reported by Billman et al. (2011) for each stream (Supplementary Tables A & B). Reproductive females are only in the medium and large classes. Using sex ratios in Billman et al. (2011), we estimated the number and mean size of females in the medium and large classes. We then estimated the number of oocytes produced by the average female for each class given the mean size of females in each class and estimated the total number of oocytes produced by all females in that size class by multiplying the mean number of oocytes by the number of females captured in 2004. To estimate the probability of survival of age-0 fish (from egg to the lower size limit of the smallest size class), we divided the total number of individuals observed in the small size class by the combined total number of eggs produced by medium and large females. We calculated realized fecundity for each size class by multiplying the probability of survival of age-0 fish by the total number of eggs produced in that class. Finally, we divided the number of surviving offspring (i.e., small individuals produced) by the number of reproductive females in each size class to estimate size-specific realized fecundity for medium and large females in each stream. We used the estimated probabilities of survival and the realized fecundity estimates in a size-structured matrix population model to estimate the population growth rate ( λ ) of each location. Given the timing of spawning and our censuses, we utilized a pre-reproduction census model. This model assumes that individuals must survive for one year (i.e., age-0) to be counted for the first time in the census as newly observed stage-1 individuals (stage-1 represents juveniles or age-1 individuals; Caswell 2000). Back-transformed estimates from the mark-recapture model were entered into the diagonal as φ (1- ψ ), i.e., the probability of surviving and remaining in the same size class. The probability of surviving and transitioning to a larger size class, calculated as φ ψ , was entered on the appropriate sub-diagonal. Realized fecundity rates for medium and large individuals were also entered in the appropriate column of the first row. The λ for each population during the period of study was calculated by finding the dominant eigenvalue of each matrix, using the eigen function within Program R (R Core Team 2022). We used standard errors from the Program MARK models to create upper and lower limit matrices from which 95% confidence intervals for λ were generated. We estimated the stable age distribution using the normalized eigenvector associated with the dominant eigenvalue for the population matrix, i.e., dividing each value in the eigenvector by the sum of all values in the eigenvector. We also calculated elasticity values for each matrix entry of both populations. Elasticities are measures of the relative contribution of each demographic process (i.e., of each matrix entry) to the population growth rate (de Kroon et al. 1986; de Kroon et al. 2000). Integral Projection Model To determine the effect of an introduced predator on an evolutionary naïve species, we used size distribution data from three successive years from both sites for an IPM (Easterling et al. 2000, Merow et al. 2014) represented as the form\(\mu_{t}\left(x\right)=\ k(y,x,\theta)\mu_{t-1}(y)dy\) where\(\mu_{t}(x)\) represents the abundance of size- x individuals at time t . \(\mu_{t-1}(y)\) represents the abundance of size- y individuals at time t-1 . The kernel k depends on the parameter set θ and is split up into 4 pieces:\(k\left(y,x,\theta\right)=\ s\left(y,\theta\right)g(x|y,\theta)+\ b(x|y,\theta)f(y,\theta)\), where s is the survival function for an individual of size y , and g is a function for the distribution of the potential size of an individual at time t given they were at size y at time t-1 . The function b represents the abundance of new recruits from an individual of size y at time t-1 and f is the probability of an individual of size y reproducing. Our southern leatherside chub IPM model uses a logistic function for s and f (which is scaled by a parameter scalar) and a normal distribution for g and b . The parameters included are shown in the following equations: \(s\left(\theta\right)=\ 1/(1+exp\ \left(\theta_{1}+\theta_{2}y\right))\) \(g(x|y,\theta)=\ N(\theta_{3}+\ \theta_{4}\ y,\sigma_{g}^{2})\) \(b\left(x|y,\theta\right)=\ N(\theta_{5}+\ \theta_{6}\ y,\sigma_{b}^{2})\) \(f\left(y,\theta\right)=\ \theta_{7}/(1+exp\ \left(\theta_{8}+\theta_{9}y\right))\) This includes eleven estimated parameters, though we fix \(\theta_{7}\)to be 0.75. Biologically, \(\theta_{7}\) may represent a constraint related to growth or survival processes. By fixing this parameter, we assume consistency in the corresponding biological process across different environmental conditions. While this assumption simplifies computation, it may limit the model’s flexibility in capturing environmental variability, which should be considered when interpreting results. Nonetheless, this helps with estimating the other parameters and with identifiability. Additionally, 𝜎 2 g and 𝜎 2 b represent the variance for the normal distributions of g and b . To account for the variability that occurs from the size frequency data, we use a hierarchical structure to filter the true IPM structure. This approach enhances the robustness of parameter estimates by modeling both individual-level variation and population-level trends. The abundance of\(\mu_{t}(x)\) is used as the mean function in a Poisson process, which is then used as the likelihood for the data (Ghosh et al. 2012, Gelfand et al. 2013). For example, if \(n_{t}(x)\) represents observed counts at size x at time t , we would say that\(n_{t}\left(x\right)\sim Poisson\ (\mu_{t}\left(x\right))\). To evaluate the integral step at each time point, the mean function\(\mu_{t}\left(x\right)\) and the kernel \(k(y,x,\theta)\) are decomposed into Fourier basis functions. This allows the mean function to be treated as continuous, as opposed to previous approaches that discretize this process. The mean function is decomposed into\(\mu_{t}\left(x\right)=a_{\text{i\ }}\phi_{i}(x)\ \)here\(\phi_{i}\left(x\right)\) are the Fourier basis functions and\(a_{\text{i\ }}\)are random coefficients which need to be estimated. We utilized Fourier basis functions to capture continuous variation in size structure, which is appropriate for species like the southern leatherside chub that exhibit continuous growth. The kernel is decomposed as\(k\left(y,x,\theta\right)=c_{\text{j\ }}(\theta,x)\phi_{j}(y)\). In this case, the basis coefficients are deterministic given the parameter set, so they do not need to be estimated. The IPM integral then becomes\(k\left(y,x,\theta\right)\mu_{t-1}\left(y\right)dy=\ a_{\text{i\ }}\phi_{i}(x)c_{\text{j\ }}(\theta,x)\phi_{j}(y)dy,\)which simplifies to\(a_{\text{i\ }}c_{\text{j\ }}(\theta,x)\) because of the properties of orthonormal basis functions. The resultant infinite sum is truncated to have a number of components that balances computational costs and accuracy. This basis function approach has been used in integral models in spatial-temporal statistics (Xu et al. 2005, Cressie and Wikle 2011); however, the likelihood in these cases is typically linear and normally distributed. In the case of our IPM, the likelihood is a Poisson process, so the parameters of the kernel and the random basis coefficients controlling the density of the population were estimated using Markov chain Monte Carlo (MCMC) methods (Gelman et al. 2013). Another advantage of this approach is that credible intervals can be found for all vital rates and a number of probability statements can be made about them. Population growth rate (λ) is computed as the eigenvalue corresponding to the first eigenfunction of the kernel ( k ). λ is computed numerically by discretizing the space in a fine grid. We follow the approach of Doak et al. (2021) and discretize the domain where the size profiles are distinguished. This creates a large matrix from which we found the eigenvalue and function that gives us the long-term growth rate and stable size distribution. Results Matrix Transition Model Southern leatherside chub in the predator-free environment survived at a higher rate in all size classes than those in the predator environment (Fig. 2). Individuals from the small size class had the lowest difference in survival between the predator environment and the predator-free environment. This contrasts with the individuals from the large size class, which had the greatest difference in survival between the two environments. Southern leatherside chub transitioned at the highest rate in both environments between the small and medium size classes (Fig. 3). The transition between medium to large size classes was the next highest but was almost half of the small to medium transition rate. There were no statistical differences in transition rates between the predator and predator-free environments. Across both systems, reproductive females in the same size class produced a similar number of oocytes (Table 1). Large females consistently produced more eggs than medium females. Total egg production followed a similar pattern, with higher totals for large females in both environments. Survival of age-0 was estimated to be over twice as high in the predator environment (0.0033) than in the predator-free environment (0.0014). Thus, realized fecundity was also greater in the predator environment for both reproductive size classes (Table 1). The population growth rate (λ) for the predator-free environment was > 1, indicating positive population growth (Table 3). Conversely, λ was less than one in the predator environment; however, the confidence interval included one, suggesting stable population growth during the study period. Across the two populations, the stable size distribution showed that the smallest size stage had the greatest proportion of individuals, but there was a greater proportion of individuals in the small size stage from the predator environment compared to the predator-free environment (Table 3). Reproductive values were 1.00, 2.79, and 5.74 for the small, medium, and large size stages, respectively in the predator-free environment. In contrast, reproductive values were 1.00, 3.58, and 10.16 for the same size stages in the predator environment. Elasticity analysis of the transition matrices showed each size class had a similar influence on λ in the predator-free environment (Table 4). This was not the case for the predator environment. Small and medium-sized individuals from the predator environment had a greater impact on λ than that of large individuals (Table 4). In the predator-free environment, the fecundity of both medium and large females had similar elasticities, whereas in the predator environment, the fecundity of medium-sized females had a substantially higher elasticity value than that of large-sized females. The survival (stasis) of large adults had a greater elasticity in the predator-free population compared to a remarkably low elasticity in the predator population (Table 4). When the elasticities were summed according to the three demographic processes (fecundity, growth, and stasis), growth was the most influential demographic process for both populations. The predator-free population was relatively more influenced by stasis and the predator population was more influenced by fecundity (Figure 4). Integral Projection Model While the values here from the integral projection model cannot be compared directly with the matrix vital rates, we observed similar patterns of the effect the introduced predator has on our evolutionarily naïve species. The IPM shows that the presence of brown trout—the invasive predator—is associated with increased mortality rates, greater fecundity, and a greater proportion of smaller individuals in the southern leatherside chub, relative to the population absent this predator. The slope of the survival rate curve represents how much the probability of survival changes with size. The survival rate curve showed a higher slope (in the logit scale) for survival in the predator-free environment (0.12, 95% CI: 0.10 – 0.14) than in the predator environment (0.03, CI: 0.02 – 0.05), indicating higher survival rates for southern leatherside chub when brown trout are absent (Figure 5). We found no differences in annual growth rates between fish from the predator environment and fish from the predator-free environment with slopes of 13.9 (CI: 9.5 - 19.8) and 12.4 (CI: 8.9 – 17.0). The IPM measures realized fecundity as the number of individuals recruited to 40 mm SL. Similar to the matrix transition model, the realized fecundity is higher in individuals from the predator environment compared to those in the predator-free environment, particularly among smaller size classes. The difference between the realized fecundity of predator and non-predator environments narrowed as size increased (Figure 6). Population growth rates estimated by the IPM were both > 1, but the confidence intervals broadly overlapped unity (Table 3); therefore, we cannot rule out a stable, or even a declining population during our study period. Overall, the stable size distribution showed that in each population, small individuals (< 60 mm SL) were more abundant than other size classes (Figure 7). Additionally, smaller individuals comprised a higher proportion of the population in the predator environment than the predator-free environment until about 60 mm in length. Individuals larger than 60 mm in standard length from the predator-free environment had a greater contribution to the population structure than in the predator environment. Recruitment density describes the relative frequency of newly recruited individuals across the range of body sizes. In the predator-free environment, recruitment density peaked at approximately 42 mm SL, whereas in the predator environment, the peak shifted rightward to around 45 mm SL (Figure 8). This shift resulted in greater recruitment density of larger-sized individuals in the predator environment compared to the predator-free environment. The proportional contribution of size to recruitment revealed that the smallest individuals contribute minimally - if at all - to recruitment of age-0 individuals (Figure 9). In the predator environment, medium-sized individuals contributed more to recruitment than their counterparts in the predator-free environment. However, this pattern reversed among the largest individuals, which contributed more to recruitment in the predator-free environment than in the predator environment. Discussion Although we have termed our systems of southern leatherside chub as predator and non-predator , with the latter “naïve to predation”, these populations are not entirely naïve to all forms of predation. Larger individuals in these populations likely experienced little to no predation pressure from other fish prior to the introduction of brown trout. Early life stages are probably consumed by a variety of native predators, including conspecifics, but chub likely rapidly outgrow the gape-limited predation risk posed by most native fish species. Evolutionarily, some populations of the species likely co-occurred with Bonneville cutthroat trout; however, these native trout are generally small-bodied when occurring in stream environments and rarely reach sizes sufficient to allow for general piscivory (Nannini 2001). In contrast, introduced brown trout are routinely piscivorous at sizes above 30 cm and can consume fish up to 40% of their body length—encompassing nearly all size classes of southern leatherside chub (Billman et al. 2011). Moreover, brown trout exhibit higher rates of fish predation than native cutthroat trout (Belk and Johnson 2007, McHugh et al. 2008). Thus, while we recognize that larval and small juvenile chub face natural predation pressures, the populations we studied have likely never experienced significant predation on adults from other fish species until the introduction of brown trout. We predicted that southern leatherside chub, when exposed to broader levels of predation, would exhibit demographic patterns similar to populations coevolved with predators—particularly in systems where prey are unable to outgrow the predator’s gape limitation (Reznick et al. 1996, Johnson and Zúñiga-Vega 2009). Specifically, we predicted higher survival at smaller sizes and lower survival of larger individuals in the predator-exposed environment relative to the predator-free system. We also predicted a population structure skewed toward smaller individuals due to increased predation-related mortality of larger size classes. Our findings support these predictions. In the presence of predators, southern leatherside chub appear to recruit at slightly larger size, which is likely a compensatory response to increased adult mortality (Table 1; Figure 7). This life history shift is also evident in the elevated importance of growth transitions from small to medium size and reproduction by medium-sized individuals in the Lost Creek population (Table 4; Figure 8), which together account for nearly 70% of the weight contributing to population growth, i.e. elasticity. In contrast, these same processes comprise only 43% of the elasticity in the predator-free Salina Creek population, despite that system exhibiting nearly double the absolute survival rate of small individuals (Figure 2). In Lost Creek, large fish exert far less demographic influence through survival and fecundity than their counterparts in Salina Creek. Mortality rates of age-0 southern leatherside chub were less than half in the predator environment compared to the predator-free stream despite the habitats being very similar. Although our data do not directly identify the mechanism behind this pattern, the most plausible explanation is an increase in predation-driven mortality in the presence of introduced brown trout. Cannibalism, or the reduction thereof, could also contribute to the observed mortality rates. If brown trout are preying primarily on larger individuals, it could reduce mortality rates on smaller fish that would otherwise be vulnerable to predation by larger leatherside chub. This appears to explain the interaction between pickerel ( Esox niger ) and eastern mosquitofish ( Gambusia holbrooki ), which showed significantly higher neonate survival within the predator treatments, presumably resulting from decreased cannibalism (Winkelman and Aho 1993). As brown trout selectively remove larger chub, speckled dace and mottled sculpin, they may indirectly release age-0 (i.e., fish in their first year) and older juvenile chub from predation pressure. The extent to which reduced predation by each of these groups contributes to the observed increase in early survival is unknown, but all are likely components to some degree of the same emergent dynamic. Notably, large southern leatherside chub are the largest predator within the native fish assemblage (in our study the predator-free population from Salina Creek) and therefore we presume that cannibalism (and the subsequent reduction) could have a meaningful impact on the survival of age-0 and juvenile chub. The introduction of brown trout appears to impose size-specific predation pressure on southern leatherside chub, directly reducing adult survival while indirectly increasing early juvenile survival. This is similar to a trophic cascade (Ripple et al. 2016), though relatively short, and one that unfolds entirely within a single species across its size classes. Conversely, Reznick et al. (2002) noted that the indirect effects of predation, through its influence on population density, may generate similar life-history patterns. By reducing density, predation can increase per-capita resource availability, which in turn promotes faster growth and earlier maturity. Under such density-independent conditions, theory predicts that populations will reproduce at smaller sizes and younger ages, consistent with the life history patterns we observed here. Although, we cannot disentangle the proximate mechanisms underlying the altered patterns in the predator environment, it is clear that elevated predation is the ultimate driver. Brown trout have been implicated as the likely culprit for the decline of southern leatherside chub across its range and even extirpation of some populations (Belk and Johnson 2007). Our study is only a brief snapshot of time and suggests that coexistence between brown trout and southern leatherside chub in Lost Creek may be a possibility; however, at this time, we don’t know if the southern leatherside chub and introduced brown trout have reached a stable coexistence or if the process of extirpation is just prolonged in this instance. Certainly, a perturbed and weakened population of southern leatherside chub in Lost Creek will be more susceptible to stochastic events, including factors such as prolonged drought. We need longer term population monitoring to understand which of the two possible scenarios will prevail: coexistence through changes in life history or extirpation. Although our study is based on a limited number of populations, this constraint reflects the current conservation reality of southern leatherside chub, with many populations already extirpated and few viable systems remaining for comparison. Despite this limitation, our approach provides valuable insight because the two study streams are located in close proximity and share highly similar environmental conditions. This environmental similarity strengthens inference by reducing the likelihood that differences in demography are driven by habitat variability rather than predation pressure. Despite utilizing different types of data from the same populations, the size-structured matrix transition model and the IPM provided consistent and complementary results for demography in response to invasive predators. Both methods generate basic demographic information (such as population growth rates) that permits comparison across differing predation regimes (Table 5). There are, however, some differences in the two approaches. For example, the matrix transition model provides elasticities, which the IPM does not; and the IPM provides assessment of continuous variation across size of the functions rather than across discrete bins, which the matrix approach does not. We recommend that, when possible, both methodologies be utilized to analyze population demography, as that can provide two sources of information for comparison to provide a more comprehensive analysis. Finally, to our knowledge, few IPMs have employed a Bayesian approach using size frequency data alone (Gonzalez et al. 2016; Erguler et al. 2022). Most rely on mark-recapture data to build vital rate functions (e.g., survival, growth, fecundity, recruitment) individually (Doak et al. 2021). Our study demonstrates that a Bayesian IPM based on size frequency data can yield demographic insights similar to those from matrix models built on mark-recapture data. 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Tables Table 1 Summary of site- and size-specific reproduction and realized fecundity (F) for southern leatherside chub in predator-free (Salina Creek) and predator (Lost Creek) environments during 2004, including mean standard length (SL, mm), estimated number of oocytes per reproductive female (# Oocytes - given mean SL and based on relationships described by Billman et al. 2011), estimated number of eggs produced by all reproductive females in that size class (Eggs), estimated number of small fish produced (based on estimated survival from age-0 stage class to small class) and realized fecundity (F) - calculated as the number of surviving small fish per reproductive female. Estimates are provided only for medium and large size classes, as small individuals are not reproductively mature. Salina Creek Medium 79 1,653 147,739 213.87 1.27 Large 92 2,550 129,962 188.13 3.58 Lost Creek Medium 80 1,178 59,141 193.60 2.08 Large 93 2,348 62,747 205.40 7.51 Table 2 Transition matrices of southern leatherside chub in Salina (predator absent) and Lost (predator present) Creeks generated from estimates of survival and transition produced using Program MARK multi-strata function and estimates of realized fecundity as described in the text. Salina Creek Small 0.02 1.27 3.58 Medium 0.46 0.25 0.00 Large 0.00 0.26 0.66 Lost Creek Small 0.05 2.08 7.51 Medium 0.25 0.13 0.00 Large 0.00 0.06 0.19 Table 3 Summary of population demographics in both Salina Creek (non-predator population) and Lost Creek (predator population). Population growth includes 95% confidence intervals in parentheses. For the matrix transition model approach, these were calculated using standard errors of survival and transition rates from Program MARK. Stable age distribution in order of small, medium, and large lengths derived from the matrix transition model. Salina Creek Matrix Transition Model 1.28 (1.08,1.40) 0.61 0.27 0.12 Integral Projection Model 1.19 (0.81, 1.44) (Fig. 6) Lost Creek Matrix Transition Model 0.92 (0.62,1.31) 0.74 0.24 0.02 Integral Projection Model 1.09 (0.72, 1.30) (Fig. 6) Table 4 Elasticity of entries of the transition matrices for southern leatherside chub populations in Salina Creek (non-predator population) and Lost Creek (predator population). Salina Creek Small 0.004 0.134 0.161 Medium 0.295 0.072 0.000 Large 0.000 0.161 0.174 Sum 0.299 0.367 0.335 Lost Creek Small 0.022 0.301 0.096 Medium 0.397 0.067 0.000 Large 0.000 0.095 0.026 Sum 0.419 0.462 0.122 Table 5 Comparison of the demographic parameters obtained from mark-recapture data and stage-structured matrix transition model versus a size frequency distribution and integral projection model. Survival Rate x x Realized Fecundity x x Population Growth Rate (λ) x x Stable Size Distribution x x Reproductive Value x Sensitivity Analysis x Elasticity Analysis x Average Growth Rate x Size Distribution of Age-0 cohort x Information & Authors Information Version history V1 Version 1 05 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords 1.060: evolutionary ecology 1: field of ecology 2.010: age structure 2.070: population dynamics 2.100: predation 2.150: life history evolution 2: topic demographic value of reproduction integral projection model matrix transition model population vital rates southern leatherside chub Authors Affiliations Josh Rasmussen 0000-0002-9943-1892 [email protected] Conservation Logic LLC View all articles by this author Eric Billman Brigham Young University-Idaho View all articles by this author Jerry Johnson Brigham Young University College of Life Sciences View all articles by this author Brenden Mikel Orocu 0009-0002-3277-5233 Brigham Young University College of Life Sciences View all articles by this author Robert Richardson Brigham Young University View all articles by this author Jaime Zuniga-Vega 0000-0002-9661-1521 Universidad Nacional Autonoma de Mexico View all articles by this author Mark Belk 0000-0002-0576-0717 Brigham Young University View all articles by this author Metrics & Citations Metrics Article Usage 205 views 106 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Josh Rasmussen, Eric Billman, Jerry Johnson, et al. 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