Theoretical Ecology

Minimum viable populations (MVPs) are population levels large enough to surmount risk from demographic, environmental, and genetic stochasticity. MVPs are estimated by biologists to guide conservation practices. However, MVPs are generally estimated for a target population without regard for how they interact with intra- and inter-species population dynamics in the broader ecological community. Thus, how and why population dynamics interact with MVPs imposed by conservation biologists remain unclear. When MVPs are imposed on a continuous population model, traditional analyses fail to capture the range of possible outcomes those MVPs create. Here, we describe viability space decomposition (VSD) as a mathematical tool to systematically analyze the potential crossing of MVPs during population dynamics. We demonstrate that different extinction and survival outcomes can be recovered from a model with imposed MVPs using three VSD concepts in junction with a traditional phase portrait: mortality manifolds which separate conditions that lead to different existential outcomes, ordering manifolds which determine the order of extinction events for multiple populations, and collapse manifolds which determine the survival or extinction of one species given the loss of another. We employ these methods with a standard consumer-resource model, and the methods can be scaled to systems with more species. VSD is a useful tool for conservation biologists and community ecologists concerned with boundary crossing problems in any dynamical system.

Global epistasis refers to the observation that the effect of a mutation or modification depends on the state of a biological system, not its detailed composition. Such patterns have been reported across biological scales, from proteins to organisms and ecosystems. In its simplest form, global epistasis appears as a linear relationship between the change in function or fitness due to a perturbation, and the background level of function or fitness. The mechanistic basis of global epistasis, particularly in ecological systems, remains unresolved. Here, we propose that in microbial communities, global epistasis describing the impact of adding a species to a community on function arises generically from constraints imposed by shared resource pools. We illustrate this mechanism in a single-species system growing on multiple substitutable resources, where global epistasis follows directly from nutrient limitation by an essential non-substitutable resource. We then extend this framework to multi-species communities competing for a single resource and show that the marginal effect of adding a species depends linearly on background community function, with a slope determined by the fraction of the resource claimed by the added species. We show that global epistasis persists in trophic cascades, but that facilitation and niche partitioning qualitatively break the linear dependence. This study provides a simple explanation for the appearance of global epistasis in ecosystems, and suggests that global epistasis should be a null expectation in ecosystems governed by competition. Our results propose that coupling between perturbations and shared resource pools might also help explain global epistasis at the organismal level.

1Although resources are typically distributed continuously in space, species distributions often organize into discrete clusters. In his seminal paper [36], Turing demonstrated that such clusters can spontaneously arise in population densities, even when populations evolve in environments with continuously varying conditions. This phenomenon is known as Turing instability. In this work, we focus on two models grounded in population dynamics: a one-dimensional model based on the nonlocal Fisher-KPP equation, and a two-dimensional model involving an environmental gradient. We show that phenotypic clusters (sometimes referred to as "species") emerge in these models. We prove that they do not emerge because of Turing instability, but because of stochasticity, and that they disappear when stochasticity is reduced. First, for both models, we start our simulations with initial populations uniformly distributed in the state space. We show that phenotypic clusters quickly emerge and that the distances between them depend on the population size, that is, on the degree of stochasticity. Next, we start from already clearly defined phenotypic clusters. We identify three regimes in the connection between population size, the initial distances between clusters, and the distances between clusters at equilibrium. Last, on the two-dimensional model, we relax the hypothesis of complete clonality by varying the effective recombination rate, explore its effect on phenotypic clustering, and show that phenotypic clustering decays drastically with slight recombination.

Elucidating how habitat degradation facilitates extinction is critical for effective conservation efforts. Here, we propose integrating physiologically-structured population models into stochastic population viability analyses to assess how differing consequences of habitat degradation interact to drive extinction dynamics in a focal population. Using the isolated spectacled caiman Caiman crocodilus population/ecomorph from the Apaporis River as a case study, we find that threatening the resource base, which individuals increasingly rely upon, to outgrow vulnerable size ranges and mature accelerates extinction. We also found that when habitat degradation impacts both the primary adult and juvenile resource bases, this can have marked synergistic effects on threatening population viability. By contrast, destroying nesting sites has only a small effect on accelerating the impact of deteriorating prey availability. Through integrating community-level feedback between habitat degradation/change and population dynamics/structure, our approach provides a comparative framework for assessing the relative importance of distinct mechanisms through which habitat degradation ultimately drives extinction risk.

The Biodiversity-Ecosystem Functioning (BEF) literature has shown species diversity to be essential for ecosystem functioning and services. Yet although acquiring information through interspecific networks can impact ecosystem functioning, it is unclear how it is modulated by species diversity. Eliciting vocal responses using predator models across a latitudinal gradient, we first show that the species diversity of birds increases public information about predation both in the low-cost system of mobbing and in the higher-cost system of alarm calls. A similar result was also found across a fragment area gradient for mobbing; this system was then used to test how species diversity affects interspecific information flow in mobbing communities. We set up two BEF playback experiments, manipulating the species richness level of the playback sound files by varying the number of species producing mobbing calls (one, two, four, eight species). In an experiment in which the call rate across treatments was held constant, and only heterospecific responses were counted, increasing species richness of the sound files increased the number of species and individuals responding, the number of calls produced and their frequency range, and decreased latency to call. An experiment in which call rate increased with the addition of species in each treatment showed a similar, but stronger pattern. There was little evidence that the signals of one particular species changed responses. This supports the hypothesis that the species diversity of a community is a key component influencing the quantity and quality of information flow inside it.

Natural populations are often nonlinear and exhibit substantial variability. A central question is how stochasticity interacts with density-dependent regulation to shape population stability. We address this using four long-term time series of Trinidadian guppies and find that their dynamics are well described by a stochastic logistic model with multiplicative environmental noise. The model predicts that stochasticity does not merely add fluctuations around deterministic carrying capacity, but alters the equilibrium structure. Using stochastic bifurcation theory, we show that increasing noise shifts the most-probable population size below the deterministic equilibrium and can push populations closer to a noise-induced bifurcation, even when mean growth rates remain positive. The effects of stochasticity across populations align with known ecological differences among streams, particularly the effects of light level and seasonality. The analysis also identifies populations most sensitive to perturbations, which are not detected by standard early warning indicators. Temporal and spectral analyses further show that intrinsic growth rate governs local recovery, while seasonal variation interacts with density-dependence to shape longer-term population fluctuations. Together, our results show that stochasticity can alter resilience and vulnerability by reshaping ecological stability landscapes.

Infectious disease time series often show signs of epidemic transitions, such as the peaks and troughs of the time series. In these time series, key system parameters can lead to catastrophic changes in the dynamical system behaviour (often called critical transitions). Modellers have increasingly shown that early warning signals can anticipate these transitions, both critical and non-critical, in infectious disease time series. Existing methods, however, generally focus on univariate time series data, or ignore spatiotemporal patterns that may be present as a disease spreads through a population. Recent ecological literature developments expand existing temporal and spatial methods to consider the covariance matrix of multiple, related time series. However, many of these proposed signals still make an assumption of stationary time series/system equilibrium. Whilst often true in ecological modelling, disease systems are seldom at equilibrium. In this paper, we propose the usage of the eigendecomposition of the non-stationary covariance matrix as a more suitable early warning signal for epidemiological data. We first analyse the expected trends in the eigenvalues and eigenbasis of the covariance matrix on approach to a transition. Next we apply these methods to a spatially-structured susceptible-infectious-recovered model to explore how the eigenbasis may provide extra information to modellers. Finally, we test these methods on SARS-CoV-2 case data during the 2020-2021 pandemic period in England.

Existing theory examining the coevolutionary dynamics of species range borders assumes random dispersal, which causes maladaptive gene flow from the range core to the range margins and contributes to the formation of range limits. However, dispersal is unlikely to be random for many organisms in nature, calling into question existing theoretical predictions. For example, if individuals exhibit phenotype-dependent adaptive dispersal strategies such as matching habitat choice, then the resulting adaptive gene flow toward species range margins could facilitate range expansions and potentially prevent the formation of range limits by interspecific competition. To test this idea, we use a comprehensive mathematical model to develop a quantitative theory of range border coevolution that incorporates phenotype-optimal dispersal--a particular form of matching habitat choice in which individuals follow the gradient in an environmental optimum phenotype to settle in the habit best suited for their phenotype. We find that instead of preventing competitively formed range limits, adaptive dispersal leads to sharper range limits and reduced character displacement in sympatry. These differences are particularly remarkable when natural selection is weak, when individuals are specialized in their resource use, or when individuals are highly sensitive to environmental conditions. We show that matching habitat choice causes backward edge-to-core movements which dynamically interact with the effects of interspecific competition to establish the range limits. Thus, the formation of range limits by interspecific competition is robust to assumptions about individual dispersal. Further, our results identify the competitive advantage of evolving matching habitat choice in steep environmental gradients, especially for slowly-growing species in rapidly fluctuating climates.

Herbivore-induced plant volatiles (HIPVs) play a critical role in inducible plant defense as information-bearing airborne signals. Released from damaged tissues, HIPVs induce defense responses in undamaged parts of the plant, thereby reducing the risk of subsequent herbivore attack. Although both emission and perception are fundamental components of HIPV-mediated signaling, the co-evolutionary dynamics of these traits under herbivore-driven selection remain poorly understood. Here, we develop a mathematical model of within-plant signaling that explicitly incorporates both inducible signal emission and perception as evolving traits. Using the model, we derived the optimal level of HIPV signal emission and signal perception under successive herbivore attacks. Our results show that the strategy with both signal emission and signal perception, which underlies HIPV-mediated signaling, is favored only under intermediate levels of herbivory. Within this range, increasing herbivory frequency drives the joint evolution of reduced signal emission and enhanced sensitivity to released signal. Furthermore, extending the model to include perception-independent functions of HIPVs, such as the attraction of natural enemies and the deterrence of herbivores, expands the range of conditions under which HIPV-mediated signaling is favored. At the same time, it also allows the emergence of emission-only strategies lacking signal perception, suggesting the potential decoupling of the co-evolution of emission and responsiveness. These findings provide a theoretical framework for understanding how emission and perception jointly shape the evolution of volatile-mediated signaling systems in plants.

Cooperative and competitive interactions among individuals harvesting resources can shape environmental states, such as prey abundance. In turn, environmental conditions feed back to influence strategic interactions. Eco-evolutionary game theory studies how these feedbacks shape the co-evolution of behavior and environment. Existing models typically assume deterministic, noise-free environmental dynamics. However, real environments are inherently stochastic, for example due to finite resources, and noise can qualitatively alter social outcomes. Here, we incorporate stochastic environmental dynamics into eco-evolutionary game theory. When environmental change is slow relative to strategy updates, we show that behavior reflects a mixture of the games associated with low and high environmental states, often yielding outcomes qualitatively distinct from deterministic predictions. In particular, environmental stochasticity can eliminate bistability and enforce dominance of a single behavior. When environmental dynamics are faster, populations have less opportunity to track fluctuations, and behavior converges toward strategies that are optimal on average. Stochasticity can even causes persistent oscillations in the tragedy of commons, in regimes where classical models predict stability. Our framework provides a tractable approach for analyzing social behavior linked to environmental dynamics how noise shapes long-term eco-evolutionary outcomes.

Helminths are widespread parasites that can modulate host immunity, potentially increasing susceptibility to viral infections. However, evidence for these effects varies across systems and environments, and links between laboratory and wild populations remain unclear. We developed a tractable system using wood mice, Heligmosomoides spp. nematodes, and wood mouse herpes virus (WMHV) to bridge this gap. Combining laboratory and field experiments with population modelling, we examined how helminth infection, anthelmintic treatment and diet affect viral dynamics. Across lab and field data, helminth infection consistently increased WMHV risk, with stronger effects at higher worm burdens. Field results showed that anthelmintic treatment reduced viral infection, and laboratory experiments showed that improved nutrition mitigates helminth-induced increases in viral susceptibility. Our population-level modelling suggested that helminth burden-dependent facilitation can generate nonlinear effects on viral spread, dependent on helminth virulence. Our findings highlight the potential importance of helminths as facilitators of viral infections, and suggest that anthelmintic treatment may provide indirect benefits for viral control. We also show the value of integrating lab and field approaches on the same (or closely related) species, in particular the potential offered by the wood mouse - Heligmosomoides - WMHV system, to understand the drivers and consequences of host-helminth-viral interactions.

A recurring question across biological systems is when gains accrued by one part of a system also benefit the whole, and when they instead impose a collective cost. In ecological communities, consumers can increase their energetic gains through trophic interactions, yet those same interactions also determine whether all species persist. Here we show that food-web architecture governs whether predator advantage supports collective persistence, and that omnivory is a key condition under which the two diverge. Using a Lotka- Volterra-type food-web model formulated in terms of energy fluxes, we compare predator output power with the probability of feasibility, which quantifies the range of growth conditions compatible with positive coexistence. In two-species systems, these objectives show no generic alignment. In trophic chains, by contrast, increasing encounter rates makes predator advantage and coexistence mutually reinforcing. Basal omnivory reverses this pattern by shifting the power optimum towards the boundary of coexistence, where the intermediate consumer is lost. This pattern persists in larger networks, under heterogeneous encounter rates, and with saturating functional responses. Our results identify food-web architecture as the determinant of whether local energetic advantage scales up as collective persistence or instead becomes a coexistence cost.

The Red Queen Hypothesis proposes that genetic variation is maintained in populations through antagonistic coevolution of hosts and parasites. A major assumption of the Red Queen Hypothesis is tight genetic specificity for infection. However, it has been argued that this genetic interaction of host and parasite (GHxGP) is sensitive to environmental context (GHxGPxE). Environmental change could accordingly disrupt coevolutionary oscillations on relevant time scales, calling into question antagonistic coevolution as a general and robust explanation for the maintenance of genetic diversity. To evaluate this critique, we used the plant-parasitic nematode Meloidogyne arenaria and its natural bacterial parasite Pasteuria penetrans to determine if specificity is altered by temperature. We exposed six isofemale host lines to five parasite sources at three ecologically relevant temperatures. We found that, at two of three temperatures, susceptibility to infection depended on the specific combination of host line and parasite source (GHxGP). This specificity varied across temperatures, consistent with a GHxGPxE effect. This three-way interaction was driven both by quantitative changes in the strength of specificity across temperatures and shifts in the susceptibility rankings of host-parasite combinations. Our study contributes a rare experimental test of a proposed challenge to the Red Queen Hypothesis and suggests the potential for environmental context to change host-parasite specificity.

Wildlife vaccination could become a powerful strategy to mitigate disease-induced biodiversity losses, yet many vaccines for wildlife diseases provide only limited protection. Notably, tools to control the fungal pathogen Batrachochytrium dendrobatidis (Bd) are urgently needed for amphibian conservation. Laboratory experiments have demonstrated that prophylactic exposure to Bd metabolites increases host resistance, significantly reducing infection intensity in amphibians subsequently challenged with live Bd. Because Bd metabolites are non-infectious and applied topically, this treatment has potential to be administered to waterbodies to vaccinate and protect amphibians. We developed an agent-based model that indicated imperfect vaccination could reduce or amplify Bd infections at the population level, depending on degree of enhanced resistance or tolerance. Utilizing a Before-After-Control-Impact design with ten years of data, we conducted an ecosystem-level trial where we applied low levels of Bd metabolites or a sham control treatment to ponds in California and subsequently quantified Bd prevalence and infection intensity in metamorphosing Pacific chorus frogs (Pseudacris regilla). Unexpectedly, infection intensity was significantly greater in treated ponds relative to control ponds following metabolite addition. Additional model simulations indicated that this could occur via two mechanisms: (1) if treatment greatly increased tolerance alone or in combination with smaller increases in resistance, or (2) if a deleterious environmental interaction caused the treatment to increase susceptibility, rather than promote resistance. Future research is needed to determine whether tolerance or environmental factors drove heightened Bd infection intensities in this field trial to identify contexts in which this treatment can be used as a conservation tool. Significance statementAlthough wildlife vaccination is increasingly explored as a strategy to mitigate disease-induced population declines, many available vaccines provide limited protection, requiring careful consideration to design successful conservation campaigns. Here, we use both an eco-epidemiological model and field manipulation experiment to assess the effectiveness of an imperfect prophylactic treatment (akin to a prototype vaccine) for chytridiomycosis, a disease implicated in the massive decline of amphibian biodiversity worldwide. We unexpectedly found that prophylaxis-treated ponds had higher pathogen loads relative to control populations and models suggest this could result from enhanced tolerance or an adverse environmental interaction.

A hosts diet can alter the course of parasite infection. This is especially true of trophic parasites, which a host acquires through feeding. While a large body of work attests to the role of diet in the spread of disease within-hosts, diet can also impact host density and encounter rate with parasites, both of which are expected to modify disease dynamics. When parasites are acquired through feeding, epidemics may be larger and more severe on high-quality diets if these diets support a higher density of hosts that feed more and thus ingest more parasites. Alternately, epidemics may be more severe on low-quality diets if malnourishment decreases hosts ability to resist disease. To differentiate these hypothesized effects of diet on disease, we quantified individual infections and epidemic dynamics for the natural microsporidian parasite Nematocida ironsii infecting its nematode host Caenorhabditis elegans. We measured feeding rate, parasite transmission, and host fitness across three bacterial diets that vary in quality and elicit distinct feeding behaviors in C. elegans. We found that low-quality diets reduced feeding rate, which corresponded to reduced acquisition of parasite spores. However, these diet-mediated differences in parasite acquisition did not directly map onto fitness consequences: hosts eating the poor-quality diet had similar reductions in fitness to those on higher quality diets. During epidemics, a combination of increased parasite acquisition and higher population growth rates resulted in higher parasite abundance for hosts on high-quality diets. Our work underscores the importance of considering both individual- and population-level impacts acting in concert to determine how diet affects the spread of infectious disease.

Non-compliance with regulations threatens the sustainability of fisheries worldwide. Understanding the interconnected feedbacks of this complex social-ecological problem is key for sustainability but rarely integrated into fisheries management. We provide an adaptive stochastic modelling framework that integrates economic, social behavior, and ecological aspects of the Chilean kelp fishery, which plays a critical economic and ecological role in coastal social-ecological ecosystem. High levels of non-compliance is threatening sustainability, fishers well-being, and ecosystem health. Our model considers inherent environmental uncertainties and enables the assessment of different harvesting and compliance scenarios and the role of market-based economic incentives in reducing non-compliance. Results show that, unlike the sustainability obtained under an idealized full-compliance scenario, under dynamic compliance the social, economic, and ecological feedbacks leads to system collapse. Importantly, price premiums can promote compliance and sustainability, but the probability of collapse, albeit small, still exist. Our generalizable stochastic modeling framework evidenced that accounting for inherent uncertainty in natural resource management is key to designing interventions for sustainability.

The basic reproduction number R0 is central to malaria epidemiology, yet it is typically treated as a static quantity derived under memoryless assumptions for mosquito demography. In natural systems, however, mosquito populations are shaped by delayed processes such as larval development and density-dependent feedback, introducing biological memory into vector dynamics. We develop a minimal delay-based framework that incorporates this memory into the Ross-Macdonald model by describing adult mosquito abundance with a retarded differential equation. This formulation induces a time-dependent transmission potential R0(t). Using complex analysis and the argument principle, we derive an explicit stability threshold [Formula], which separates stable from oscillatory transmission regimes. Near this threshold, delayed feedback produces slow relaxation times and sustained transient oscillations, implying that transmission potential may vary intrinsically even in the absence of external forcing. To account for ecological variability, we extend this deterministic condition into a probabilistic framework and define the stability probability as [Formula]. Numerical simulations and global sensitivity analysis show that recruitment and developmental delays are the primary drivers of instability, while adult mortality has a weaker stabilizing effect. These results indicate that malaria interventions may influence not only the magnitude of malaria transmission but also its dynamical stability. By linking delay dynamics, transmission theory, and uncertainty quantification, this framework provides a basis for stability-aware modeling and interpretation of malaria transmission under ecological variability. Author summaryMalaria transmission is often summarized by a single number, R0, treated as a fixed indicator of whether transmission will increase or decline. This assumes mosquito populations respond instantly to environmental conditions. In reality, mosquitoes develop through stages where larval conditions, such as crowding, nutrition, or temperature, affect adult populations only after a delay. This creates biological memory: todays mosquitoes reflect past environments. We show that this memory can fundamentally reshape transmission dynamics. When developmental delays are included, transmission potential is no longer constant but can fluctuate over time, even in stable environments. These fluctuations can persist or amplify depending on the balance between mosquito growth, mortality, and delay. As a result, variability in mosquito abundance or malaria transmission may arise from intrinsic dynamics rather than external drivers alone. Under ecological variability, stability becomes probabilistic, allowing estimation of how likely transmission is to remain stable. Interventions that reduce larval productivity or increase adult mortality may therefore both lower transmission and make it more predictable, improving interpretation and control strategies.

Guinea worm disease eradication efforts may benefit from environmental surveillance methods capable of detecting infected copepod intermediate hosts in aquatic habitats. We developed a three-dimensional, spatially explicit agent-based model to examine how ecological processes influence detection probability for a hypothetical water sampling method. The results show that surveillance sensitivity is shaped by the combined effects of larval diffusion, copepod density, and pond size, with interactions among these factors producing nonlinear relationships. Detection, in our model, was concentrated within a relatively restricted period after larvae matured to the infective stage and before dispersal and mortality reduced presence, indicating a limited spatiotemporal window for effective sampling. Surveillance performance peaked under intermediate dispersal regimes that generated sufficient spatial overlap between larvae and intermediate hosts, while both limited dispersal and excessive diffusion reduced detection by constraining encounters or diluting larval concentrations. Increasing habitat size reduced detection by diluting larval concentrations, but the magnitude of this effect depended on copepod density and dispersal dynamics, producing nonlinear and threshold responses rather than simple scaling with pond volume. Spatial and temporal patterns of detection shifted as larvae dispersed, with the most favorable detection periods occurring when both larval abundance and intermediate host encounters were elevated. These findings indicate that surveillance can be guided by local ecological conditions. When the timing of larval introduction is uncertain, effective surveillance requires repeated sampling over time to capture transient windows of detectability and the sampling will be less effective in very stagnant and highly mixed waterbodies. Overall, this study demonstrates how mechanistic modeling can support the design and interpretation of environmental surveillance strategies for Guinea worm eradication programs. Author summaryGuinea worm disease is close to eradication but confirming that transmission has fully stopped remains difficult because detecting infectious larvae in water is challenging. Transmission depends on freshwater copepods that become infected after ingesting Guinea worm larvae. These copepods are short-lived and unevenly distributed within ponds, and infected individuals may die before larvae reach the infective stage. As a result, environmental detection is inherently uncertain. We developed a three-dimensional agent-based model to simulate larval dispersal, copepod infection, and water sampling in a pond environment. The model shows that detection is constrained to a brief period when mature larvae and copepods overlap in space and time, and that this window depends strongly on local ecological conditions such as larval dispersal, copepod density, and pond size. Because infected copepods can be present outside these narrow detection windows, negative water samples do not necessarily indicate absence of transmission, highlighting the need for repeated, spatially targeted surveillance during the final stages of eradication.

Bacterial communities grow as dynamic populations that respond to their environments. A clinically relevant example is the inactivation of beta-lactam antibiotics by intracellular beta-lactamase in E. faecalis resistant strains. In these populations, resistant bacteria act as antibiotic sinks, detoxifying the environment and allowing sensitive bacteria to survive treatment through a cooperative interaction. In this work, we study strongly coupled planktonic and biofilm populations of mixed sensitive-resistant E. faecalis bacteria under antibiotic stress using fluorescent microscopy. The presence of resistant bacteria in the system benefits both resistant and sensitive cells, leading to mixed planktonic and biofilm populations at super-inhibitory drug concentrations. We show that a beta-lactam antibiotic with or without the addition of a beta-lactam inhibitor can lead to a population inversion effect, characterized by a non-monotonic relation between initial and final fractions of resistant bacteria. The effect is observed in both the planktonic and biofilm populations and is modulated by the total initial cell density. A well-mixed model with competition mediated by resource sharing and cooperation from global degradation of toxins predicts the experimentally observed behavior. These observations suggest underlying population-level mechanisms that are largely independent of biofilm spatial structure.

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.