ECOP Subgroup Contributed Talks

Maria Kuruvilla

University of Victoria

"Quantifying the Impact of Forest Harvesting on Chum and Pink Salmon Populations in Coastal BC"

Forest harvesting in coastal British Columbia (BC) has altered watersheds, impacting salmon habitat by increasing sediment, reducing riparian cover, and altering hydrology. These changes can affect the survival and growth of salmon through mechanisms like reduced egg-to-fry survival, increased stream scour, increased thermal stress, and loss of stream complexity which is essential for salmon rearing. Despite numerous localized studies, no comprehensive analysis has examined the population-level effects of forestry on salmon across BC. After assembling forest harvest histories for 1,746 salmon-bearing watersheds (1883-2022) and salmon abundance data (1950-2022), we used stock-recruit models (Ricker and Beverton-Holt) in a hierarchical Bayesian framework to test the effects of forestry metrics (Equivalent Clearcut Area, Cumulative Percent Disturbed) on chum and pink salmon productivity. Our results show a strong negative effect of forestry on chum productivity (e.g. 25% equivalent clearcut area reduces productivity by more than 20%) and a negligible effect on pink salmon. This highlights forestry’s significant role in the decline of chum salmon populations over recent decades.

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Morgan Lavenstein Bendall

University of California, Merced

"Exploring Climate-Driven Population Changes in Aster Leafhoppers Using Age-Structured Models"

Due to their diversity and abundance, insects play essential ecological roles, including crop pollination, nutrient cycling, and serving as a food source for other species. However, climate change is predicted to heavily impact insect populations, with some expected to decline by up to 18% globally by the end of the 2020s, raising concerns about the future health of the bioeconomy. To investigate these impacts, we conducted a temperature study on Aster leafhoppers (Hemiptera: Cicadellidae: Macrosteles quadrilineatus). Using five temperature conditions, we collected physiological data over a month to assess the impact of temperature on survival, maturation, and egg production. We then developed an age-structured population model to explore how environmental temperature influences insect fitness and mortality rates. Our model is parameterized with experimental data across various climate change scenarios, providing insights into the effects of rising temperatures on insect survival and population dynamics. This work highlights the cascading effects of climate change on ecological networks and emphasizes the importance of understanding insect responses to environmental stressors.

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Alexander Moffett

Northeastern University

"Detecting selection with a null model of gene order evolution"

Recent progress in genome assembly techniques has led to an explosion in chromosome-length genome sequences. These unfragmented assemblies have enabled biologists to study molecular evolution at unprecedented scales, providing insight into the evolution of genome architecture. Microsynteny, the conservation of gene order, has proven to be a key concept in our understanding of genome evolution. However, it remains unclear when microsynteny occurs due to random chance or selection. Here, we develop a mathematical model to discriminate between these two cases. Our model describes the dynamics of synteny block size distributions in the absence of selection or other biases. By fitting this null model to data from a comparative analysis of mammalian genomes, we identify synteny blocks larger than expected in the absence of selection. This approach allows us to rigorously determine which sets of genes are likely to have selection on their ordering in a lineage-specific manner. Our model presents a powerful tool for uncovering functional relationships between genes based on their ordering and for understanding the evolution of gene co-regulation.

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Silas Poloni

University of Victoria

"Evolutionary dynamics at the leading edge of biological invasions"

Empirical evidence shows that evolution may take place during species' range expansion. Indeed, dispersal ability tends to be selected for at the leading edge of invasions, ultimately increasing a species' spreading speed. However, for organisms across many different taxa, higher dispersal comes at the cost of fitness, producing evolutionary trade-offs at the leading edge. Using reaction-diffusion equations and adaptive dynamics, we provide new insights on how such evolutionary processes take place. We show how evolution may drive phenotypes at the leading edge to maximize the asymptotic spreading speed, and conditions under which phenotypic plasticity in dispersal is selected for under different dispersal-reproduction trade-off scenarios. We provide some possible future research directions and other systems where the framework can be applied.

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Jacob Serpico

University of Alberta

"Decoding the spatial spread of cyanobacterial blooms in an epilimnion"

Cyanobacterial blooms (CBs) pose significant global challenges due to their harmful toxins and socio-economic impacts, with nutrient availability playing a key role in their growth, as described by ecological stoichiometry (ES). However, real-world ecosystems exhibit spatial heterogeneity, limiting the applicability of simpler, spatially uniform models. To address this, we develop a spatially explicit partial differential equation model based on ES to study cyanobacteria in the epilimnion of freshwater systems. We establish the well-posedness of the model and perform a stability analysis, showing that it admits two linearly stable steady states, leading to either extinction or saturation. We use the finite elements method to numerically solve our system on a real lake domain derived from Geographic Information System (GIS) data and realistic wind conditions extrapolated from ERA5-Land. Our numerical results highlight the importance of lake shape and size in CB monitoring, while global sensitivity analysis using Sobol Indices identifies light attenuation and intensity as primary drivers of bloom variation, with water movement influencing early bloom stages and nutrient input becoming critical over time. This model supports continuous water-quality monitoring, informing agricultural, recreational, economic, and public health strategies for mitigating CBs.

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Farshad Shirani

Emory University

"Environmental “Knees” and “Wiggles” as Stabilizers of Species Range Limits Set by Interspecific Competition"

Whether interspecific competition is a major contributing factor in setting species' range limits has been debated for a long time. Theoretical studies using evolutionary models have proposed that the interaction between interspecific competition and disruptive gene flow along an environmental gradient can halt range expansion of ecologically related species where they meet. However, the stability of such range limits has not been well addressed. In this talk, I present our work on investigating the stability of competitively formed range limits using a deterministic model of adaptive range evolution. We show that the range limits are unlikely to be evolutionarily stable if the environmental optima for fitness-related traits vary linearly in space. However, we demonstrate that environmental nonlinearities such as “knees” and “wiggles”, wherein an isolated sharp change or a step-like change occurs in the steepness of a trait optimum, can strongly stabilize the range limits. We show that the stability of the range limits established at such nonlinearities is robust against moderate environmental disturbances. Although strong climatic changes can still destabilize the range limits, such destabilization depends on how the relative dominance of the competing species changes across the environmental nonlinearity. Therefore, our results highlight the importance of measuring the competitive ability of species when predicting their response to climate change.

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Maximilian Strobl

Cleveland Clinic

"Towards Quantitative and Predictive Models of Tumour Ecology: A Framework for Calibrating Evolutionary Game Theory with Experimental Data"

Tumours are complex ecosystems where diverse cancer cell subpopulations interact with each other and with non-cancer cells around them. Evolutionary game theory (EGT) has established itself as a powerful mathematical framework to study the implications of such ecological interactions, demonstrating an important role in shaping oncogenesis and treatment response. However, much of this work has been theoretical using parameters that are only loosely grounded in biological data. To move towards quantitative and predictive models of tumour ecology it is crucial to develop theoretical and experimental methodology to empirically calibrate and validate EGT models. We present an in silico study to optimize the 'Game Assay' for measuring ecological interactions between cancer cell populations in vitro. This assay, originally developed by Kaznatcheev et al (2017), involves co-culturing populations at different ratios, monitoring growth rates via time-lapse microscopy, and inferring frequency-dependent interactions. We begin by characterizing the accuracy and precision of this assay in a simulation study in which we use the replicator equation as the “ground truth”. Our simulations reveal potential biases in estimating fitness differences and interaction parameters, highlighting the need for careful experimental design. We provide guidelines for optimizing seeding ratios, number of replicates, and frequency of measurements, and present a new analysis techniques to improve the accuracy and precision of interaction measurements. Finally, we apply our optimized protocol to quantify interactions between 4 drug-sensitive and resistant lung cancer cell lines, revealing diverse ecological dynamics. This work demonstrates the power of integrating mathematical modeling with experimental approaches to develop robust empirical protocols and gain a quantitative understanding of tumour ecology.

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Sureni Wickramasooriya

University of California - Davis

"Mathematical Model for Gene Drive Mosquito Releae On Principe Island"

Genetically engineered mosquitoes (GEMs) offer a promising malaria control strategy, yet their ecological interactions, dispersal, and long-term effects remain uncertain. Accurate modeling is essential to optimize GEM release strategies and assess their effectiveness in natural ecosystems. This study presents a high-performance, exascale agent-based model (ABM) simulating gene drive dynamics in wild mosquito populations. Incorporating mosquito population dynamics, spatial ecology, and genotype inheritance, the model provides insights into optimizing release timing, locations, and dispersal strategies. Our findings indicate that under optimal dispersal conditions, GEMs can achieve a 95% prevalence in wild populations within 112 days. Furthermore, our findings indicate that strategically coordinating GEM releases across multiple sites does not significantly impact gene drive establishment on the island. By capturing mosquito behaviors and movement in heterogeneous environments, this ABM serves as a powerful tool for evaluating GEM interventions, supporting evidence-based malaria control strategies, and enhancing ecological understanding of gene drive propagation..

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