Biology subjects

Connors, B. M.

Publications and source records attributed to Connors, B. M..

8 recordsLinked to original sources

bioRxiv2026

Quantifying Exposure of Pacific Salmon and Steelhead to Climate Change in the Fraser River basin

Climate change can affect salmon and steelhead (Oncorhynchus spp.) throughout their anadromous life cycles, yet there have been no assessments of which Canadian populations face the greatest exposure. We developed a framework to quantify relative climate change exposure of salmon and steelhead populations based on the spatial and temporal distribution of different life stages. Exposure was calculated from climate model projections for freshwater and marine climate variables considering unique impact thresholds for each population and life stage. We applied this framework to 60 Conservation Units of Pacific salmon and steelhead in the Fraser River basin, British Columbia. Lake-type sockeye had the highest exposure, driven by elevated stream temperatures during adult freshwater migration and spawning stages and relatively low thermal tolerance of marine stages. Chinook salmon were the next most exposed, while coho, pink, and chum salmon had relatively low exposure. Uniquely, steelhead exposure was driven by high stream temperatures during incubation. Our framework is broadly applicable, and our findings provide critical input for climate change vulnerability assessments and forward-looking resilience planning for Pacific salmon.

ecology↗

bioRxiv2025

Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics

Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility, they require large datasets, are challenging to interpret, and can over-fit to experimental noise. To overcome these limitations, we develop a physics-constrained machine learning model, which we call the Neural Species Mediator (NSM), that combines a mechanistic model of metabolite dynamics with a machine learning component. The NSM is more accurate than mechanistic or machine learning components on experimental datasets and provides insights into direct biological interactions. In summary, embedding a neural network into a mechanistic model of microbial community dynamics improves prediction performance and interpretability compared to its constituent mechanistic or machine learning components. Significance statementMicrobial communities drive essential biological processes in every ecosystem. Predicting microbial community dynamics and functions and uncovering their interaction networks are challenging due to their complexity and emergent interactions. We develop a framework that combines biological knowledge with machine learning to accurately predict community and metabolite dynamics. This approach outperforms existing ecological and machine learning models by capturing environmentally mediated interactions while providing interpretable insights into microbe-metabolite interactions. Our framework enables the rational design and control of microbial communities for applications in medicine, agriculture and biotechnology

systems biology↗

bioRxiv2025

Machine learning-guided design of human gut microbiome dynamics in response to dietary fibers

Dietary fibers are key modulators of human gut microbiome dynamics and functions, yet we lack the ability to predict community dynamics and functions in response to fibers. We integrate machine learning, Bayesian optimization, and high-throughput community construction to investigate how dietary fibers shape health-relevant functions of human gut microbial communities. To efficiently navigate the landscape of fiber-microbiome interactions, we implemented a design-test-learn cycle to identify fiber-species combinations that maximize a multi-objective function capturing beneficial community properties. Our model-guided approach revealed a highly butyrogenic and robust ecological motif characterized by the copresence of inulin, Bacteroides uniformis, and Anaerostipes caccae and a higher-order interaction with Prevotella copri. In germ-free mice, model-designed species-fiber combinations impacted colonization, community diversity and short-chain fatty acid profiles. In sum, our work establishes a novel framework for designing microbial communities with desired functions in response to key nutrients such as dietary fibers.

systems biology↗

bioRxiv2025

Using fisheries risk assessment to inform precautionary and collaborative management in a declining coho salmon fishery

The conservation and management of Pacific salmon in Canada faces an uncertain future. While fisheries policies, like Canadas Wild Salmon Policy, increasingly emphasize conservation, salmon continue to decline due to cumulative pressures of climate change, habitat loss, and overfishing, requiring precautionary management. We quantified population dynamics for 52 coho salmon populations along the North and Central Coast of British Columbia since 1980 to determine population status, assess risks posed by a mixed-stock fishery spanning U.S., Canadian, and Indigenous jurisdictions, and implemented forward simulations of alternative productivity trends and harvest scenarios to inform collaborative management tables. We found declining abundances for 51% of coho populations since 2017, driven by productivity regime shifts associated with recent marine heatwaves. Although long-term coho recovery depended on future productivity trends, reduced harvest rates across U.S. and Canadian fisheries can improve short-term recovery prospects. While rebuilding coho will depend largely on whether productivity improves, harvest management remains one of the few tools available to provide a safe operating space for coho populations, and their fisheries, to adapt to ongoing ecosystem changes.

ecology↗

bioRxiv2022

Model-guided design of the diversity of a synthetic human gut community

Microbial communities have tremendous potential as therapeutics. However, a major bottleneck is manufacturing high-diversity microbial communities with desired species compositions. We develop a two-stage, model-guided framework to produce microbial communities with target species compositions. We apply this method to optimize the diversity of a synthetic human gut community. The first stage exploits media components to enable uniform growth responses of individual species and the second stage uses a design-test-learn cycle with initial species abundance as a control point to manipulate community composition. Our designed culture conditions yield 91% of the maximum possible diversity. Leveraging these data, we construct a dynamic ecological model to guide the design of lower-order communities with desired temporal properties over a longer timescale. In sum, a deeper understanding of how microbial community assembly responds to changes in environmental factors, initial species abundances, and inter-species interactions can enable the predictable design of community dynamics.

systems biology↗

bioRxiv2021

Trends in Pacific Canadian groundfish stock status

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stocks (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6-3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3-1.9) and 1.4 (95% CI: 1.1-1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the " critical zone"); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the " cautious zone"). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the " healthy zone").

ecology↗

bioRxiv2021

Linking habitat and population dynamics to inform conservation benchmarks for data-limited salmon stocks

Management of data-limited populations is a key challenge to the sustainability of fisheries around the world. For example, sockeye salmon (Oncorhynchus nerka) spawn and rear in many remote coastal watersheds of British Columbia (BC), Canada, making population assessment a challenge. Estimating conservation and management targets for these populations is particularly relevant given their importance to First Nations and commercial fisheries. Most sockeye salmon have obligate lake-rearing as juveniles, and total abundance is typically limited by production in rearing lakes. Although methods have been developed to estimate population capacity based on nursery lake photosynthetic rate (PR) and lake area or volume, they have not yet been widely incorporated into stock-recruit analyses. We tested the value of combining lake-based capacity estimates with traditional stock-recruit based approaches to assess population status using a hierarchical-Bayesian stock-recruit model for 70 populations across coastal BC. This analysis revealed regional variation in sockeye population productivity (Ricker ), with coastal stocks exhibiting lower mean productivity than those in interior watersheds. Using moderately-informative PR estimates of capacity as priors reduced model uncertainty, with a more than five-fold reduction in credible interval width for estimates of conservation benchmarks (e.g. SMAX - spawner abundance at carrying capacity). We estimated that almost half of these remote sockeye stocks are below one commonly applied conservation benchmarks (SMSY), despite substantial reductions in fishing pressure in recent decades. Thus, habitat-based capacity estimates can dramatically reduce scientific uncertainty in model estimates of management targets that underpin sustainable sockeye fisheries. More generally, our analysis reveals opportunities to integrate spatial analyses of habitat characteristics with population models to inform conservation and management of exploited species where population data are limited.

ecology↗

bioRxiv2018

Durability of environment-recruitment relationships in aquatic ecosystems: insights from long-term monitoring in a highly modified estuary and implications for management

The environment can strongly influence the survival of aquatic organisms and their resulting dynamics. Our understanding of these relationships, typically based on correlations, underpins many contemporary resource management decisions and conservation actions. However, such relationships can break down over time as ecosystems evolve. Even when durable, they may not be very useful for management if they exhibit high variability, context dependency, or non-stationarity. Here, we systematically review the literature to identify trends across environment-recruitment relationships for aquatic taxa from Californias San Francisco Bay and Sacramento-San Joaquin Delta Estuary. This is one of the most heavily modified aquatic ecosystems in North America, and home to numerous species of concern whose relationships with the environment inform regulatory actions and constraints. We retested 23 of these relationships spanning 9 species using data that have accumulated in the years since they were first published (9-40 additional years) to determine whether they persisted. Most relationships were robust (i.e., same or stronger in magnitude) to the addition of new data, but the ability to predict how a species will respond to environmental change did not generally improve with more data. Instead, prediction error generally increased over time and in some cases very quickly, suggesting a rapid regime shift. Our results suggest that more data alone will not necessarily improve the ability of these relationships to inform decision making. We conclude by synthesizing emerging insights from the literature on best practices for the analysis, use, and refinement of environment-recruitment relationships to inform decision making in dynamic ecosystems.

ecology↗