A potential growth thermal index for estimating juvenile Atlantic salmon (Salmo salar) size-at-age across geographical scales.

We present a potential growth thermal index (PGTI) and assess its correlation with juvenile Atlantic salmon Salmo salar fork length data collected near the end of the growth season in a range of latitudinal locations and geographic scales (watershed, regional, continental) across the American north-east. The PGTI is based on two components: a water temperature-dependent growth curve and a water temperature time series continuously describing the thermal environment preceding fish sampling. Testing various shapes and characteristics of the temperature-growth curve against fish length data revealed strong positive correlations for all combinations. PGTI warming, calculated only from the beginning of the growth season until maximum summer temperature is reached, consistently performed well in explaining fish size-at-age across the latitudinal gradient and the three geographic scales that were considered. Varying thermal contrasts created by repeat subsampling of the dataset showed that fish length is better explained by the level of thermal contrast within the dataset than the geographical scale of analysis. A simple generalized linear model using a log link function with PGTI warming, fish density and water discharge as predictors explained 87% of the variance of size-at-age of 0+ and 1+ juvenile Atlantic salmon.

Hydrothermal impacts of water release on early life stages of white sturgeon in the Nechako river, B.C. Canada.

Water temperature plays a crucial role in the physiology of aquatic species, particularly in their survival and development. Thus, resource programs are commonly used to manage water quality conditions for endemic species. In a river system like the Nechako River system, central British Columbia, a water management program was established in the 1980s to alter water release in the summer months to prevent water temperatures from exceeding a 20 °C threshold downstream during the spawning season of Sockeye salmon (Oncorhynchus nerka). Such a management regime could have consequences for other resident species like the white sturgeon (Acipenser transmontanus). Here, we use a hydrothermal model and white sturgeon life stage-specific experimental thermal tolerance data to evaluate water releases and potential hydrothermal impacts based on the Nechako water management plan (1980-2019). Our analysis focused mainly on the warmest five-month period of the year (May to September), which includes the water release management period (July-August). Our results show that the thermal exposure risk, an index that measures temperature impact on species physiology of Nechako white sturgeon across all early life stages (embryo, yolk-sac larvae, larvae, and juvenile) has increased substantially, especially in the 2010s relative to the management program implementations' first decade (the 1980s). The embryonic life stage was the most impacted, with a continuous increase in potential adverse thermal exposure in all months examined in the study. We also recorded major impacts of increased thermal exposure on the critical habitats necessary for Nechako white sturgeon recovery. Our study highlights the importance of a holistic management program with consideration for all species of the Nechako River system and the merit of possibly reviewing the current management plan, particularly with the current concerns about climate change impacts on the Nechako River.

Reservoirs regulated by small dams have a similar warming effect than lakes on the summer thermal regime of streams.

Small dams account for the majority of reservoirs throughout the world, yet little is known about their effects on stream temperatures. Given that water temperature is vital for maintaining the integrity of aquatic ecosystems, studying the effects of small dams is important. This study aims to understand the effect of small dams on summer stream temperatures in a protected area in southern Quebec, Canada. We assessed the effect of small surface-release dams on four attributes of the thermal regimes (magnitude, frequency and duration of warm events, and rate of change) of streams by comparing water temperature measured in the main tributary upstream and in the main outlet downstream of the reservoir. We also compared the thermal effects of reservoirs to those of natural lakes of similar size. Using a generalized additive model, we identified key determinants of stream temperature to assess the influence of reservoir and natural lake characteristics on the thermal regime of streams. In August 2020, we observed an average warming of 3.7 °C downstream of reservoirs regulated by small dams compared to conditions upstream of the reservoir. During this period, the warming effect of reservoirs was not significantly different from the warming effect of natural lakes (3.4 °C). In addition to the drainage area, distance to an upstream water body, and the proportion of the watershed occupied by water bodies were the primary determinants of stream temperature in August, demonstrating the importance of nearby water bodies on stream thermal regimes. Given their warming effect, small waterbodies may limit the available habitat for species that are sensitive to warm temperatures. As the construction of small dams is accelerating at the global scale, a clear understanding of the cumulative effects of small lakes and reservoirs on stream temperature is required to ensure the sound management of aquatic ecosystems.

Considering Fish as Recipients of Ecosystem Services Provides a Framework to Formally Link Baseline, Development, and Post-operational Monitoring Programs and Improve Aquatic Impact Assessments for Large Scale Developments.

In most countries, major development projects must satisfy an Environmental Impact Assessment (EIA) process that considers positive and negative aspects to determine if it meets environmental standards and appropriately mitigates or offsets negative impacts on the values being considered. The benefits of before-after-control-impact monitoring designs have been widely known for more than 30 years, but most development assessments fail to effectively link pre- and post-development monitoring in a meaningful way. Fish are a common component of EIA evaluation for both socioeconomic and scientific reasons. The Ecosystem Services (ES) concept was developed to describe the ecosystem attributes that benefit humans, and it offers the opportunity to develop a framework for EIA that is centred around the needs of and benefits from fish. Focusing an environmental monitoring framework on the critical needs of fish could serve to better align risk, development, and monitoring assessment processes. We define the ES that fish provide in the context of two common ES frameworks. To allow for linkages between environmental assessment and the ES concept, we describe critical ecosystem functions from a fish perspective to highlight potential monitoring targets that relate to fish abundance, diversity, health, and habitat. Finally, we suggest how this framing of a monitoring process can be used to better align aquatic monitoring programs across pre-development, development, and post-operational monitoring programs.

Heat-related mortality prediction using low-frequency climate oscillation indices: Case studies of the cities of Montréal and Québec, Canada.

Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer weather and therefore constitute important tools to understand and predict the variations in heat-related mortality. Methods: In this article, we propose to model summer heat-related mortality using seven climate indices through a two-stage analysis using data covering the period 1981-2018 in two metropolitan areas of the province of Québec (Canada): Montréal and Québec. In the first stage, heat attributable fractions are estimated through a time series regression design and distributed lag nonlinear specification. We consider different definitions of heat. In the second stage, estimated attributable fractions are predicted using climate index curves through a functional linear regression model. Results: Results indicate that the Atlantic Multidecadal Oscillation is the best predictor of heat-related mortality in both Montréal and Québec and that it can predict up to 20% of the interannual variability. Conclusion: We found evidence that one climate index is predictive of summer heat-related mortality. More research is needed with longer time series and in different spatial contexts. The proposed analysis and the results may nonetheless help public health authorities plan for future mortality related to summer heat.

River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems.

There is growing evidence that river temperatures are increasing under climate change, which is expected to be exacerbated by increased abstractions to satisfy human water demands. Water temperature research has experienced crucial advances, both in terms of developing new monitoring and modelling tools, as well as understanding the mechanisms of temperature feedbacks with biogeochemical and ecological processes. However, water practitioners and regulators are challenged with translating the widespread and complex technological, modelling and conceptual advances made in river temperature research into improvements in management practice. This critical review provides a comprehensive overview of recent advances in the state-of-the-art monitoring and modelling tools available to inform ecological research and practice. In so doing, we identify pressing research gaps and suggest paths forward to address practical research and management challenges. The proposed research directions aim to provide new insights into spatio-temporal stream temperature dynamics and unravel drivers and controls of thermal river regimes, including the impacts of changing temperature on metabolism and aquatic biogeochemistry, as well as aquatic organisms. The findings of this review inform future research into ecosystem resilience in the face of thermal degradation and support the development of new management strategies cutting across spatial and temporal scales.

A new look at weather-related health impacts through functional regression.

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality.

In a number of environmental studies, relationships between nat4ural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

Monitoring stream sediment loads in response to agriculture in Prince Edward Island, Canada.

Increased agricultural land use leads to accelerated erosion and deposition of fine sediment in surface water. Monitoring of suspended sediment yields has proven challenging due to the spatial and temporal variability of sediment loading. Reliable sediment yield calculations depend on accurate monitoring of these highly episodic sediment loading events. This study aims to quantify precipitation-induced loading of suspended sediments on Prince Edward Island, Canada. Turbidity is considered to be a reasonably accurate proxy for suspended sediment data. In this study, turbidity was used to monitor suspended sediment concentration (SSC) and was measured for 2 years (December 2012-2014) in three subwatersheds with varying degrees of agricultural land use ranging from 10 to 69 %. Comparison of three turbidity meter calibration methods, two using suspended streambed sediment and one using automated sampling during rainfall events, revealed that the use of SSC samples constructed from streambed sediment was not an accurate replacement for water column sampling during rainfall events for calibration. Different particle size distributions in the three rivers produced significant impacts on the calibration methods demonstrating the need for river-specific calibration. Rainfall-induced sediment loading was significantly greater in the most agriculturally impacted site only when the load per rainfall event was corrected for runoff volume (total flow minus baseflow), flow increase intensity (the slope between the start of a runoff event and the peak of the hydrograph), and season. Monitoring turbidity, in combination with sediment modeling, may offer the best option for management purposes.

Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs).

The assessment of the adequacy of sampling locations is an important aspect in the validation of an effective and efficient water quality monitoring network. Two geostatistical approaches (e.g., kriging and Moran's I) are presented to assess multiple sampling locations. A flexible and comprehensive framework was developed for the selection of multiple sampling locations of multiple variables which was accomplished by coupling geostatistical approaches with principal component analysis (PCA) and fuzzy optimal model (FOM). The FOM was used in the integrated assessment of both multiple principal components and multiple geostatistical approaches. These integrated methods were successfully applied to the assessment of two independent water quality monitoring networks (WQMNs) of Lake Winnipeg, Canada, which respectively included 14 and 30 stations from 2006 to 2010.