Probability of early infection extinction depends linearly on the virus clearance rate.

We provide an in silico study of stochastic viral infection extinction from a pharmacokinetical viewpoint. Our work considers a non-specific antiviral drug that increases the virus clearance rate, and we investigate the effect of this drug on early infection extinction. Infection extinction data are generated by a hybrid multiscale framework that applies both continuous and discrete mathematical approaches. The central result of our paper is the observation, analysis and explanation of a linear relationship between the virus clearance rate and the probability of early infection extinction. The derivation behind this simple relationship is given by merging different mathematical toolboxes.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Linking functional habitat and fish population dynamics modeling to improve river rehabilitation planning and assessment.

In-stream habitat enhancement is widely used to improve ecological conditions in rivers, often prioritizing key fish life stages such as spawning and juvenile development. However, no standard approaches exist to predict their effects on fish recruitment and populations. Here, we use a spatially-explicit population dynamics model that integrates functional habitat dynamics to assess the impact of two rehabilitation measures in a hydropower-impacted section of the Inn River (SE Germany) on the recruitment potential of four rheophilic and lithophilic fish species - grayling, nase, barbel, and chub. Rehabilitation measures implemented included the construction of a bypass channel and an island side-channel system to improve both longitudinal connectivity and habitat conditions. In addition, we analyzed two alternatives, which would enhance functional availability of nursery habitats from actual 33.2% to 66.8% and 95.3%, respectively. The results suggest that the improved habitat conditions will yield on average additional 14.9 individuals/ha (5.6 kg/ha) of the target species. However, the limited accessibility of usable nursery habitat constitutes a significant recruitment bottleneck for all species. In the alternative scenarios, the increase of functional connectivity will result in average densities of 17.9 and 25.8 individuals/ha, respectively. However, potential further improvements are species-specific, because of distinct population responses to spawning-to-nursery habitat ratios, with density changes varying between -11.7% for grayling and +172.6% for chub. This study not only demonstrates the applicability of the modeling approach for assessing and planning rehabilitation measures but also emphasizes the importance of considering habitat ratios and their functional connectivity to optimize recruitment potential.

Modeling the Effects of Temperature and Limiting Nutrients on the Competition of an Invasive Floating Plant, Pontederia crassipes, with Submersed Vegetation in a Shallow Lake.

The potential for a non-native plant species to invade a new habitat depends on broadscale factors such as climate, local factors such as nutrient availability, and the biotic community of the habitat into which the plant species is introduced. We developed a spatially explicit model to assess the risk of expansion of a floating invasive aquatic plant species (FAV), the water hyacinth (Pontederia crassipes), an invader in the United States, beyond its present range. Our model used known data on growth rates and competition with a native submersed aquatic macrophyte (SAV). In particular, the model simulated an invasion into a habitat with a mean annual temperature different from its own growth optimum, in which we also simulated seasonal fluctuations in temperature. Twenty different nutrient concentrations and eight different temperature scenarios, with different mean annual amplitudes of seasonal temperature variation around the mean of the invaded habitat, were simulated. In each case, the ability of the water hyacinth to invade and either exclude or coexist with the native vegetation was determined. As the temperature pattern was changed from tropical towards increasingly cooler temperate levels, the competitive advantage shifted from the tropical FAV to the more temperate SAV, with a wide range in which coexistence occurred. High nutrient concentrations allowed the coexistence of FAV, even at cooler annual temperatures. But even at the highest nutrient concentrations in the model, the FAV was unlikely to persist under the current climates of latitudes in the Southeastern United States above that of Northern Alabama. This result may have some implications for where control efforts need to be concentrated.

Evidence for Admixture and Rapid Evolution During Glacial Climate Change in an Alpine Specialist.

The pace of current climate change is expected to be problematic for alpine flora and fauna, as their adaptive capacity may be limited by small population size. Yet, despite substantial genetic drift following post-glacial recolonization of alpine habitats, alpine species are notable for their success surviving in highly heterogeneous environments. Population genomic analyses demonstrating how alpine species have adapted to novel environments with limited genetic diversity remain rare, yet are important in understanding the potential for species to respond to contemporary climate change. In this study, we explored the evolutionary history of alpine ground beetles in the Nebria ingens complex, including the demographic and adaptive changes that followed the last glacier retreat. We first tested alternative models of evolutionary divergence in the species complex. Using millions of genome-wide SNP markers from hundreds of beetles, we found evidence that the N. ingens complex has been formed by past admixture of lineages responding to glacial cycles. Recolonization of alpine sites involved a distributional range shift to higher elevation, which was accompanied by a reduction in suitable habitat and the emergence of complex spatial genetic structure. We tested several possible genetic pathways involved in adaptation to heterogeneous local environments using genome scan and genotype-environment association approaches. From the identified genes, we found enriched functions associated with abiotic stress responses, with strong evidence for adaptation to hypoxia-related pathways. The results demonstrate that despite rapid demographic change, alpine beetles in the N. ingens complex underwent rapid physiological evolution.

High-Resolution Estimates of N2O Emissions from Inland Waters and Wetlands in China.

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N2O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N2O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N2O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N2O emissions in China was established, employing a data-driven approach that upscaled from site-specific N2O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N2O emissions from inland waters and wetlands were 1.02 × 105 t N2O yr-1, with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N2O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Boundary Effects Cause False Signals of Range Expansions in Population Genomic Data.

Studying range expansions is central for understanding genetic variation through space and time as well as for identifying refugia and biological invasions. Range expansions are characterized by serial founder events causing clines of decreasing genetic diversity away from the center of origin and asymmetries in the two-dimensional allele frequency spectra. These asymmetries, summarized by the directionality index (ψ), are sensitive to range expansions and persist for longer than clines in genetic diversity. In continuous and finite meta-populations, genetic drift tends to be stronger at the edges of the species distribution in equilibrium populations and populations undergoing range expansions alike. Such boundary effects are expected to affect geographic patterns in genetic diversity and ψ. Here we demonstrate that boundary effects cause high false positive rates in equilibrium meta-populations when testing for range expansions. In the simulations, the absolute value of ψ (|ψ|) in equilibrium data sets was proportional to the fixation index (FST). By fitting signatures of range expansions as a function of ɛ |ψ|/FST and geographic clines in ψ, strong evidence for range expansions could be detected in data from a recent rapid invasion of the cane toad, Rhinella marina, in Australia, but not in 28 previously published empirical data sets from Australian scincid lizards that were significant for the standard range expansion tests. Thus, while clinal variation in ψ is still the most sensitive statistic to range expansions, to detect true signatures of range expansions in natural populations, its magnitude needs to be considered in relation to the overall levels of genetic structuring in the data.

Keeping Nitrogen Use in China within the Planetary Boundary Using a Spatially Explicit Approach.

Nitrogen (N) supports food production, but its excess causes water pollution. We lack an understanding of the boundary of N for water quality while considering complex relationships between N inputs and in-stream N concentrations. Our knowledge is limited to regional reduction targets to secure food production. Here, we aim to derive a spatially explicit boundary of N inputs to rivers for surface water quality using a bottom-up approach and to explore ways to meet the derived N boundary while considering the associated impacts on both surface water quality and food production in China. We modified a multiscale nutrient modeling system simulating around 6.5 Tg of N inputs to rivers that are allowed for whole of China in 2012. Maximum allowed N inputs to rivers are higher for intensive food production regions and lower for highly urbanized regions. When fertilizer and manure use is reduced, 45-76% of the streams could meet the N water quality threshold under different scenarios. A comparison of "water quality first" and "food production first" scenarios indicates that trade-offs between water quality and food production exist in 2-8% of the streams, which may put 7-28% of crop production at stake. Our insights could support region-specific policies for improving water quality.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

Fitting individual-based models of spatial population dynamics to long-term monitoring data.

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Reconstructing colonization dynamics to establish how human activities transformed island biodiversity.

Drivers and dynamics of initial human migrations across individual islands and archipelagos are poorly understood, hampering assessments of subsequent modification of island biodiversity. We developed and tested a new statistical-simulation approach for reconstructing the pattern and pace of human migration across islands at high spatiotemporal resolutions. Using Polynesian colonisation of New Zealand as an example, we show that process-explicit models, informed by archaeological records and spatiotemporal reconstructions of past climates and environments, can provide new and important insights into the patterns and mechanisms of arrival and establishment of people on islands. We find that colonisation of New Zealand required there to have been a single founding population of approximately 500 people, arriving between 1233 and 1257 AD, settling multiple areas, and expanding rapidly over both North and South Islands. These verified spatiotemporal reconstructions of colonisation dynamics provide new opportunities to explore more extensively the potential ecological impacts of human colonisation on New Zealand's native biota and ecosystems.

Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model.

Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed's area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.

Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.

Predicting the spatial expansion of an animal population with presence-only data.

Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision-making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence-only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home-range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional-scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence-only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much-needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management.

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium.

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

Three-dimensional species distribution modelling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs.

The three-dimensional structure of habitats is a critical component of species' niches driving coexistence in species-rich ecosystems. However, its influence on structuring and partitioning recruitment niches has not been widely addressed. We developed a new method to combine species distribution modelling and structure from motion, and characterized three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and gorgonians. Fine-scale roughness was the most important predictor of suitable habitat for both taxa, and their niches largely overlapped, primarily due to scleractinians' broader niche breadth. Crevices and holes at mm scales on calcareous rock with low coral cover were more suitable for octocorals than for scleractinian recruits, suggesting that the decline in scleractinian corals is facilitating the recruitment of octocorals on contemporary Caribbean reefs. However, the relative abundances of the taxa were independent of the amount of suitable habitat on the reef, emphasizing that niche processes alone do not predict recruitment rates.

Demography-environment relationships improve mechanistic understanding of range dynamics under climate change.

Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography-climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes-juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

A risk-based approach to cumulative effects assessment for large marine ecosystems to support transboundary marine spatial planning: A case study of the yellow sea.

Cumulative effects assessment (CEA) should be conducted at ecologically meaningful scales such as large marine ecosystems to halt further ocean degradation caused by anthropogenic pressures and facilitate ecosystem-based management such as transboundary marine spatial planning (MSP). However, few studies exist at large marine ecosystems scale, especially in the West Pacific seas, where countries have different MSP processes yet transboundary cooperation is paramount. Thus, a step-wise CEA would be informative to help bordering countries set a common goal. Building on the risk-based CEA framework, we decomposed CEA into risk identification and spatially-explicit risk analysis and applied it to the Yellow Sea Large Marine Ecosystem (YSLME), aiming to understand the most influential cause-effect pathways and risk distribution pattern. The results showed that (1) seven human activities including port, mariculture, fishing, industry and urban development, shipping, energy, and coastal defence, and three pressures including physical loss of seabed, input of hazardous substances, nitrogen, and phosphorus enrichment were the leading causes of environmental problems in the YSLME; (2) benthic organisms, fishes, algae, tidal flats, seabirds, and marine mammals were the most vulnerable ecosystem components on which cumulative effects acted; (3) areas with relatively high risk mainly concentrated on nearshore zones, especially Shandong, Liaoning, and northern Jiangsu, while coastal bays of South Korea also witnessed high risk; (4) certain risks could be observed in the transboundary area, of which the causes were the pervasive fishing, shipping, and sinking of pollutants in this area due to the cyclonic circulation and fine-grained sediments. In future transboundary cooperation on MSP, risk criteria and evaluation of existing management measures should be incorporated to determine whether the identified risk has exceeded the acceptable level and identify the next step of cooperation. Our study presents an example of CEA at large marine ecosystems scale and provides a reference to other large marine ecosystems in the West Pacific and elsewhere.

Consumption in Non-Pastoral Regions Drove Three-Quarters of Forage-Livestock Conflicts in China.

Forage-livestock conflict (FLC) is a major anthropogenic cause of rangeland degradation. It poses tremendous threats to the environment owing to its adverse impacts on carbon sequestration, water supply and regulation, and biodiversity conservation. Existing policy interventions focus on the in situ FLCs induced by local production activities but overlook the role of consumption activities in driving FLCs. Here, we investigate the spatiotemporal variations in China's FLCs and the domestic final consumers at the county level by combining remote sensing data and multi-regional input-output model. Results show that during 2005-2015, China's pastoralism induced an average of 82 million tons of FLCs per year. Domestic final demand was responsible for 85-93% of the FLCs in China. There was spatiotemporal heterogeneity in domestic consumption driving China's FLCs. In particular, the final demand of non-pastoral regions was responsible for around three-quarters (74-79%) of the total FLCs throughout the decade. The rangeland-based livestock raising, agricultural and sideline product processing, and catering sectors are important demand-side drivers. These findings can support targeted demand-side strategies and interregional cooperation to reduce China's FLCs, thus mitigating rangeland degradation.

Evaluating the evolutionary mechanisms maintaining alternative mating strategies in a simulated bull trout (Salvelinus confluentus) population.

The coexistence of distinct alternative mating strategies (AMS) is often explained by mechanisms involving trade-offs between reproductive traits and lifetime fitness; yet their relative importance remains poorly understood. Here, we used an established individual-based, spatially explicit model to simulate bull trout (Salvelinus confluentus) in the Skagit River (Washington, USA) and investigated the influence of female mating preference, sneaker-specific mortality, and variation in age-at-maturity on AMS persistence using global sensitivity analyses and boosted regression trees. We assumed that two genetically fixed AMS coexisted within the population: sneaker males (characterized by younger age-at-maturity, greater AMS-specific mortality, and lower reproductive fitness) and territorial males. After 300 years, variation in relative sneaker success in the system was explained by sneaker males' reproductive fitness (72%) and, to a lesser extent, the length of their reproductive lifespan (21%) and their proportion in the initial population (8%). However, under a wide range of parameter values, our simulated scenarios predicted the extinction of territorial males or their persistence in small, declining populations. Although these results do not resolve the coexistence of AMS in salmonids, they reinforce the importance of mechanisms reducing sneaker's lifetime reproductive success in favoring AMS coexistence within salmonid populations but also limit the prediction that, without any other selective mechanisms at play, strong female preference for mating with territorial males and differences in reproductive lifespan allow the stable coexistence of distinct AMS.