Landscape fragmentation overturns classical metapopulation thinking.

Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants." This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.

52,000 years of woolly rhinoceros population dynamics reveal extinction mechanisms.

The extinction of the woolly rhinoceros (Coelodonta antiquitatis) at the onset of the Holocene remains an enigma, with conflicting evidence regarding its cause and spatiotemporal dynamics. This partly reflects challenges in determining demographic responses of late Quaternary megafauna to climatic and anthropogenic causal drivers with available genetic and paleontological techniques. Here, we show that elucidating mechanisms of ancient extinctions can benefit from a detailed understanding of fine-scale metapopulation dynamics, operating over many millennia. Using an abundant fossil record, ancient DNA, and high-resolution simulation models, we untangle the ecological mechanisms and causal drivers that are likely to have been integral in the decline and later extinction of the woolly rhinoceros. Our 52,000-y reconstruction of distribution-wide metapopulation dynamics supports a pathway to extinction that began long before the Holocene, when the combination of cooling temperatures and low but sustained hunting by humans trapped woolly rhinoceroses in suboptimal habitats along the southern edge of their range. Modeling indicates that this ecological trap intensified after the end of the last ice age, preventing colonization of newly formed suitable habitats, weakening stabilizing metapopulation processes, triggering the extinction of the woolly rhinoceros in the early Holocene. Our findings suggest that fragmentation and resultant metapopulation dynamics should be explicitly considered in explanations of late Quaternary megafauna extinctions, sending a clarion call to the fragility of the remaining large-bodied grazers restricted to disjunct fragments of poor-quality habitat due to anthropogenic environmental change.

Emergent encoding of dispersal network topologies in spatial metapopulation models.

We address a generalization of the concept of metapopulation capacity for trees and networks acting as the template for ecological interactions. The original measure had been derived from an insightful phenomenological model and is based on the leading eigenvalue of a suitable landscape matrix. It yields a versatile predictor of metapopulation persistence through a threshold value of the eigenvalue determined by ecological features of the focal species. Here, we present an analytical solution to a fundamental microscopic model that incorporates key ingredients of metapopulation dynamics and explicitly distinguishes between individuals comprising the "settled population" and "explorers" seeking colonization. Our approach accounts for general network characteristics (in particular graph-driven directional dispersal which is known to significantly constrain many ecological estimates) and yields a generalized version of the original model, to which it reduces for particular cases. Through examples, including real landscapes used as the template, we compare the predictions from our approach with those of the standard model. Results suggest that in several cases of practical interest, differences are significant. We also examine, with both models, how changes in habitat fragmentation, including removal, addition, or alteration in size, affect metapopulation persistence. The current approach demonstrates a high level of flexibility, enabling the incorporation of diverse "microscopic" elements and their impact on the resulting biodiversity landscape pattern.

Genome-Wide Allele Frequency Changes Reveal That Dynamic Metapopulations Evolve Differently.

Two important characteristics of metapopulations are extinction-(re)colonization dynamics and gene flow between subpopulations. These processes can cause strong shifts in genome-wide allele frequencies that are generally not observed in "classical" (large, stable, and panmictic) populations. Subpopulations founded by one or a few individuals, the so-called propagule model, are initially expected to show intermediate allele frequencies at polymorphic sites until natural selection and genetic drift drive allele frequencies toward a mutation-selection-drift equilibrium characterized by a negative exponential-like distribution of the site frequency spectrum. We followed changes in site frequency spectrum distribution in a natural metapopulation of the cyclically parthenogenetic pond-dwelling microcrustacean Daphnia magna using biannual pool-seq samples collected over a 5-yr period from 118 ponds occupied by subpopulations of known age. As expected under the propagule model, site frequency spectra in newly founded subpopulations trended toward intermediate allele frequencies and shifted toward right-skewed distributions as the populations aged. Immigration and subsequent hybrid vigor altered this dynamic. We show that the analysis of site frequency spectrum dynamics is a powerful approach to understand evolution in metapopulations. It allowed us to disentangle evolutionary processes occurring in a natural metapopulation, where many subpopulations evolve in parallel. Thereby, stochastic processes like founder and immigration events lead to a pattern of subpopulation divergence, while genetic drift leads to converging site frequency spectrum distributions in the persisting subpopulations. The observed processes are well explained by the propagule model and highlight that metapopulations evolve differently from classical populations.

Multi-generation genetic contributions of immigrants reveal cryptic elevated and sex-biased effective gene flow within a natural meta-population.

Impacts of immigration on micro-evolution and population dynamics fundamentally depend on net rates and forms of resulting gene flow into recipient populations. Yet, the degrees to which observed rates and sex ratios of physical immigration translate into multi-generational genetic legacies have not been explicitly quantified in natural meta-populations, precluding inference on how movements translate into effective gene flow and eco-evolutionary outcomes. Our analyses of three decades of complete song sparrow (Melospiza melodia) pedigree data show that multi-generational genetic contributions from regular natural immigrants substantially exceeded those from contemporary natives, consistent with heterosis-enhanced introgression. However, while contributions from female immigrants exceeded those from female natives by up to three-fold, male immigrants' lineages typically went locally extinct soon after arriving. Both the overall magnitude, and the degree of female bias, of effective gene flow therefore greatly exceeded those which would be inferred from observed physical arrivals, altering multiple eco-evolutionary implications of immigration.

Effect of unequal vaccination coverage and migration on long-term pathogen evolution in a metapopulation.

Worldwide inequalities in vaccine availability are expected to affect the spread and spatial distribution of infectious diseases. It is unclear, however, how spatial variation in vaccination coverage can affect the long-term evolution of pathogens. Here we use an analytical model and numerical simulations to analyse the influence of different imperfect vaccines on the potential evolution of pathogen virulence in a two-population model where vaccination coverage varies between populations. We focus on four vaccines, with different modes of action on the life cycle of a pathogen infecting two host populations coupled by migration. We show that, for vaccines that reduce infection risk or transmissibility, spatial heterogeneity has little effect on pathogen prevalence and host mortality, and no effect on the evolution of pathogen virulence. In contrast, vaccines that reduce pathogen virulence can select for more virulent pathogens and may lead to the coexistence of different pathogen strains, depending on the degree of spatial heterogeneity in the metapopulation. This heterogeneity is driven by two parameters: pathogen migration and the difference in the vaccination rate between the two populations. We show that vaccines that only reduce pathogen virulence select mainly for a single pathogen strategy in the long term, while vaccines that reduce both transmission and virulence can favor the coexistence of two pathogen genotypes. We discuss the implications and potential extensions of our analysis.

Limits to species' range: the tension between local and global adaptation.

We know that heritable variation is abundant, and that selection causes all but the smallest populations to rapidly shift beyond their original trait distribution. So then, what limits the range of a species? There are physical constraints and also population genetic limits to the effectiveness of selection, ultimately set by population size. Global adaptation, where the same genotype is favoured over the whole range, is most efficient when based on a multitude of weakly selected alleles and is effective even when local demes are small, provided that there is some gene flow. In contrast, local adaptation is sensitive to gene flow and may require alleles with substantial effect. How can populations combine the advantages of large effective size with the ability to specialise into local niches? To what extent does reproductive isolation help resolve this tension? I address these questions using eco-evolutionary models of polygenic adaptation, contrasting discrete demes with continuousspace.

Centrality to the metapopulation is more important for population genetic diversity than habitat area or fragmentation.

Drift and gene flow affect genetic diversity. Given that the strength of genetic drift increases as population size decreases, management activities have focused on increasing population size through preserving habitats to preserve genetic diversity. Few studies have empirically evaluated the impacts of drift and gene flow on genetic diversity. Kryptolebias marmoratus, henceforth 'rivulus', is a small killifish restricted to fragmented New World mangrove forests with gene flow primarily associated with ocean currents. Rivulus form distinct populations across patches, making them a well-suited system to test the extent to which habitat area, fragmentation and connectivity are associated with genetic diversity. Using over 1000 individuals genotyped at 32 microsatellite loci, high-resolution landcover data and oceanographic simulations with graph theory, we demonstrate that centrality (connectivity) to the metapopulation is more strongly associated with genetic diversity than habitat area or fragmentation. By comparing models with and without centrality standardized by the source population's genetic diversity, our results suggest that metapopulation centrality is critical to genetic diversity regardless of the diversity of adjacent populations. While we find evidence that habitat area and fragmentation are related to genetic diversity, centrality is always a significant predictor with a larger effect than any measure of habitat configuration.

Building pondscapes for amphibian metapopulations.

The success of ponds constructed to restore ecological infrastructure for pond-breeding amphibians and benefit aquatic biodiversity depends on where and how they are built. We studied effects of pond and landscape characteristics, including connectivity, on metapopulation dynamics of 12 amphibian species in Switzerland. To understand the determinants of long-term occupancy (here summarized as incidence), environmental effects on both colonization and persistence should be considered. We fitted dynamic occupancy models to 20 years of monitoring data on a pond construction program to quantify effects of pond and landscape characteristics and different connectivity metrics on colonization and persistence probabilities in constructed ponds. Connectivity to existing populations explained dynamics better than structural connectivity metrics, and simple metrics (distance to the nearest neighbor population, population density) were useful surrogates for dispersal kernel-weighted metrics commonly used in metapopulation theory. Population connectivity mediated the persistence of conservation target species in new ponds, suggesting source-sink dynamics in newly established populations. Population density captured this effect well and could be used by practitioners for site selection. Ponds created where there were 2-4 occupied ponds within a radius of ∼0.5 km had >3.5 times higher incidence of target species (median) than isolated ponds. Species had individual preferences regarding pond characteristics, but breeding sites with larger (≥100 m2) total water surface area, that temporarily dried, and that were in surroundings with maximally 50% forest benefitted multiple target species. Pond diversity will foster amphibian diversity at the landscape scale.

Construcción de estanques para meta poblaciones de anfibios Resumen El éxito de los estanques construidos para restaurar la infraestructura ecológica para los anfibios que allí se reproducen y para beneficiar la biodiversidad acuática depende de en dónde y cómo se construyen. Estudiamos los efectos de las características de los estanques y el paisaje, incluida la conectividad, sobre la dinámica de las meta poblaciones de 12 especies de anfibios en Suiza. Se deben considerar los efectos ambientales sobre la colonización y la persistencia para entender las determinantes de la ocupación a largo plazo (resumida aquí como incidencia). Ajustamos los modelos dinámicos de ocupación a datos de 20 años de monitoreo de un programa de construcción de estanques para cuantificar los efectos de las características del estanque y el paisaje y las diferentes medidas de conectividad para las probabilidades de colonización y persistencia en los estanques construidos. La conectividad con las poblaciones existentes explicó mejor la dinámica que las medidas de conectividad estructural, mientras que las medidas simples (distancia a la población vecina más cercana, densidad poblacional) fueron sustitutos útiles para las medidas de dispersión ponderadas al núcleo que se usan con frecuencia en la teoría de meta poblaciones. La conectividad poblacional medió la persistencia de las especies a conservar en los estanques nuevos, lo que sugiere que hay dinámicas fuente­sumidero en las poblaciones recién establecidas. La densidad poblacional capturó muy bien este efecto y podría usarse para que los practicantes seleccionen sitios. Los estanques construidos en un radio de ≈0.5 km de dos a cuatro estanques ocupados tuvieron >3.5 más incidencia de las especies a conservar (mediana) que los estanques aislados. Las especies tuvieron preferencias individuales con respecto a las características de los estanques, aunque los sitios de reproducción con una mayor superficie total de agua (≥100 m2), que se secaban temporalmente y que estaban rodeados con un máximo de 50% de bosque beneficiaron a muchas especies a conservar. Por esto, la diversidad de estanques promoverá la diversidad de anfibios a escala de paisaje.

Mating system induced lags in rates of range expansion for different simulated mating systems and dispersal strategies: a modelling study.

Mismatches between current and potential species distributions are commonplace due to lags in the response of populations to changing environmental conditions. The prevailing mating system may contribute to such lags where it leads to mating failure at the range edge, but how active dispersers might mitigate these lags using social information to inform dispersal strategies warrants greater exploration. We used an individual-based model to explore how different mating systems for species that actively search for habitat can impose a filter on the ability to colonise empty, fragmented landscapes, and explored how using social information during dispersal can mitigate the lags caused by more constrained mating systems. The mate-finding requirements implemented in two-sex models consistently led to slower range expansion compared to those that were not mate limited (i.e., female only models), even when mating was polygynous. A mate-search settlement strategy reduced the proportion of unmated females at the range edge but had little impact on rate of spread. In contrast, a negative density-dependent settlement strategy resulted in much faster spread, which could be explained by a greater number of long-distance dispersal events. Our findings suggest that even low rates of mating failure at the range edge can lead to considerable lags in range expansion, though dispersal strategies that favour colonising more distant, sparsely occupied habitat patches may effectively mitigate these lags.

A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic.

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.

Golden lion tamarin metapopulation dynamics five years after heavy losses to yellow fever.

The golden lion tamarin (GLT) is an Endangered primate endemic to Brazil's lowland Atlantic Forest. After centuries of deforestation and capture for the pet trade, only a few hundred individuals survived, all in isolated forest fragments 85 km from Rio de Janeiro city. Intensive conservation actions, including reintroduction of zoo-born tamarins, increased numbers to about 3700 in 2014. The most severe yellow fever epidemic/epizootic in Brazil in 80 years reduced two of the largest GLT populations by over 90%. Herein we report the results of a 2023 survey of GLTs designed to examine the dynamics of population recovery following yellow fever. Results indicate that populations hard hit by yellow fever are recovering due in part to immigration from adjacent forest fragments. No local extirpations were observed. About 4800 GLTs live in the survey area. This represents a 31% increase since the baseline survey completed in 2014. Two factors explain most of the increase: four large areas that had no GLTs or very low-density populations in 2014 are now at moderate density (three areas) or low density (one area), explaining 71% of overall increase since 2014. Increase in forest area within our survey area may explain up to 16% of the increase in GLT numbers since 2014. Results of computer simulations suggest that strengthening forest connectivity will facilitate metapopulation resilience in the face of mortality factors such as yellow fever.

A metapopulation model of social group dynamics and disease applied to Yellowstone wolves.

The population structure of social species has important consequences for both their demography and transmission of their pathogens. We develop a metapopulation model that tracks two key components of a species' social system: average group size and number of groups within a population. While the model is general, we parameterize it to mimic the dynamics of the Yellowstone wolf population and two associated pathogens: sarcoptic mange and canine distemper. In the initial absence of disease, we show that group size is mainly determined by the birth and death rates and the rates at which groups fission to form new groups. The total number of groups is determined by rates of fission and fusion, as well as environmental resources and rates of intergroup aggression. Incorporating pathogens into the models reduces the size of the host population, predominantly by reducing the number of social groups. Average group size responds in more subtle ways: infected groups decrease in size, but uninfected groups may increase when disease reduces the number of groups and thereby reduces intraspecific aggression. Our modeling approach allows for easy calculation of prevalence at multiple scales (within group, across groups, and population level), illustrating that aggregate population-level prevalence can be misleading for group-living species. The model structure is general, can be applied to other social species, and allows for a dynamic assessment of how pathogens can affect social structure and vice versa.

MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting.

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio-temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio-temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio-temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.

Genetic Drift Shapes the Evolution of a Highly Dynamic Metapopulation.

The dynamics of extinction and (re)colonization in habitat patches are characterizing features of dynamic metapopulations, causing them to evolve differently than large, stable populations. The propagule model, which assumes genetic bottlenecks during colonization, posits that newly founded subpopulations have low genetic diversity and are genetically highly differentiated from each other. Immigration may then increase diversity and decrease differentiation between subpopulations. Thus, older and/or less isolated subpopulations are expected to have higher genetic diversity and less genetic differentiation. We tested this theory using whole-genome pool-sequencing to characterize nucleotide diversity and differentiation in 60 subpopulations of a natural metapopulation of the cyclical parthenogen Daphnia magna. For comparison, we characterized diversity in a single, large, and stable D. magna population. We found reduced (synonymous) genomic diversity, a proxy for effective population size, weak purifying selection, and low rates of adaptive evolution in the metapopulation compared with the large, stable population. These differences suggest that genetic bottlenecks during colonization reduce effective population sizes, which leads to strong genetic drift and reduced selection efficacy in the metapopulation. Consistent with the propagule model, we found lower diversity and increased differentiation in younger and also in more isolated subpopulations. Our study sheds light on the genomic consequences of extinction-(re)colonization dynamics to an unprecedented degree, giving strong support for the propagule model. We demonstrate that the metapopulation evolves differently from a large, stable population and that evolution is largely driven by genetic drift.

Landscape Structure Affects Metapopulation-Scale Tipping Points.

AbstractEven when environments deteriorate gradually, ecosystems may shift abruptly from one state to another. Such catastrophic shifts are difficult to predict and sometimes to reverse (so-called hysteresis). While well studied in simplified contexts, we lack a general understanding of how catastrophic shifts spread in realistically spatially structured landscapes. For different types of landscape structures, including typical terrestrial modular and riverine dendritic networks, we here investigate landscape-scale stability in metapopulations whose patches can locally exhibit catastrophic shifts. We find that such metapopulations usually exhibit large-scale catastrophic shifts and hysteresis and that the properties of these shifts depend strongly on the metapopulation spatial structure and on the population dispersal rate: an intermediate dispersal rate, a low average degree, or a riverine spatial structure can largely reduce hysteresis size. Our study suggests that large-scale restoration is easier with spatially clustered restoration efforts and in populations characterized by an intermediate dispersal rate.

A major flood caused by a typhoon did not affect the population genetic structure of a river mayfly metapopulation.

Floods affect the population structure of organisms that inhabit streams. In recent decades, the scale of floods has become larger due to climate change. Under these circumstances, on 12 October 2019, the largest typhoon in the history of observation in Japan struck the Japanese Archipelago. This typhoon caused heavy rainfall in various places, and the Chikuma-Shinano River System (Japan's largest) suffered great damage. Eight years before the large-scale disturbance in the river system, the population structure of the mayfly Isonychia japonica was studied in detail using quantitative sampling (population numbers and biomass) and by sequencing the mtDNA cytochrome c oxidase subunit I. To understand the impact of the flood on the population and genetic structures, we repeated the same research approximately 1 year after the flood. Direct comparison of sites before and after the flood revealed no significant changes between pre- and post-flood population genetic structure. This indicates high in situ resistance and/or resilience recovery of the populations to the disturbance. We hypothesize that this high resistance/resilience to flood disturbance is a result of strong selection for such traits in the rivers of the Japanese Archipelago, which are short, steep, flow rapidly and violently, and are strongly affected by floods.

Taylor's law for exponentially growing local populations linked by migration.

We consider the dynamics of a collection of n>1 populations in which each population has its own rate of growth or decay, fixed in continuous time, and migrants may flow from one population to another over a fixed network, at a rate, fixed over time, times the size of the sending population. This model is represented by an ordinary linear differential equation of dimension n with constant coefficients arrayed in an essentially nonnegative matrix. This paper identifies conditions on the parameters of the model (specifically, conditions on the eigenvalues and eigenvectors) under which the variance of the n population sizes at a given time is asymptotically (as time increases) proportional to a power of the mean of the population sizes at that given time. A power-law variance function is known in ecology as Taylor's Law and in physics as fluctuation scaling. Among other results, we show that Taylor's Law holds asymptotically, with variance asymptotically proportional to the mean squared, on an open dense subset of the class of models considered here.

Evolution of dispersal under spatio-temporal heterogeneity.

Theoretical studies over the past decades have revealed various factors that favor or disfavor the evolution of dispersal. Among these, environmental heterogeneity is one driving force that can impact dispersal traits, because dispersing individuals can obtain a fitness benefit through finding better environments. Despite this potential benefit, some previous works have shown that the existence of spatial heterogeneity hinders evolution of dispersal. On the other hand, temporal heterogeneity has been shown to promote dispersal through a bet-hedging mechanism. When they are combined in a patch-structured population in which the quality of each patch varies over time independently of the others, it has been shown that spatiotemporal heterogeneity can favor evolution of dispersal. When individuals can use patch quality information so that dispersal decision is conditional, the evolutionary outcome can be different since individuals have options to disperse more/less offspring from bad/good patches. In this paper, we generalize the model and results of previous studies. We find richer dynamics including bistable evolutionary dynamics when there is arrival bias towards high-productivity patches. Then we study the evolution of conditional dispersal strategy in this generalized model. We find a surprising result that no offspring will disperse from a patch whose productivity was low when these offspring were born. In addition to mathematical proofs, we also provide intuition behind this initially counter-intuitive result based on reproductive-value arguments. Dispersal from high-productivity patches can evolve, and its parameter dependence behaves similarly, but not identically, to the case of unconditional dispersal. Our results unveil an importance of whether or not individuals can use patch quality information in dispersal evolution.

The effect of population model choice and network topologies on extinction time patterns in dendritic ecological networks.

Models of populations in habitat networks are vital for understanding and linking processes and patterns across individuals, environments, ecological interactions, and population structures. River ecosystem models combine the physical structure of the networks with the biological processes of the organisms using structural and functional models, respectively. Previous studies on dendritic river networks have employed different functional (population) models and either directly claimed or implied that the results illustrate general properties of actual river systems. However, these studies have used different approaches and assumptions when modeling population characteristics and behavior, and it is possible that inferences regarding a system may vary based on the combination of functional model and the spatial structure of a network. This study aims to understand if different functional models in river systems produce substantially different model results and, therefore, whether conclusions are model-dependent. We compare variation in extinction time and occupancy proportion of river networks with linear, trellis, dendritic and ring-lattice topologies, using three population models (uniform, age-class and individual based) and one metapopulation-based (patch-occupancy) model. Dendritic, linear, and trellis structures did not show notable differences among extinction times for any of the four models. The difference between topologies was higher for the patch-occupancy model compared to the three population models. There were significant differences in the variations of patch-occupancy between the metapopulation and the population models, but the three population models of differing complexity produced broadly similar results. Therefore, if the occupancy data is obtained based on local subpopulations, spatial arrangement and connectivity does not appear to be the sole predictor of single-species metapopulation responses. We conclude that the outputs from functional models are robust to assumptions and varying levels of detail as long as they contain at least some detail at the level of individuals within habitat nodes. Also, if we are modeling network-scale populations, models that include at least some detailed information on individuals are a far better choice than considering populations implicitly.