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Wildlife models focused solely on a single strong influence (e.g., habitat components, wildlife harvest) are limited in their ability to detect key mechanisms influencing population change. Instead, we propose integrated modeling in the context of cumulative effects assessment using multispecies population dynamics models linked to landscape-climate simulation at large spatial and temporal scales.We developed an integrated landscape and population simulation model using ALCES Online as the model-building platform, and the model accounted for key ecological components and relationships among moose (Alces alces), grey wolves (Canis lupus nubilus), and woodland caribou (Rangifer tarandus caribou) in northern Ontario, Canada. We simulated multiple scenarios over 5 decades (beginning 2020) to explore sensitivity to climate change and land use and assessed effects at multiple scales. The magnitude of effect and the relative importance of key factors (climate change, roads, and habitat) differed depending on the scale of assessment. Across the full extent of the study area (654,311 km² [ecozonal scale]), the caribou population declined by 26% largely because of climate change and associated predator-prey response, which led to caribou range recession in the southern part of the study area. At the caribou range scale (108,378 km²), which focused on 2 herds in the northern part of the study area, climate change led to a 10% decline in the population and development led to an additional 7% decline. At the project scale (8,331 km²), which was focused more narrowly on the landscape surrounding 4 proposed mines, the caribou population declined by 29% largely in response to simulated development. Given that observed caribou population dynamics were sensitive to the cumulative effects of climate change, land use, interspecific interactions, and scale, insights from the analysis might not emerge under a less complex model. Our integrated modeling framework provides valuable support for broader regional assessments, including estimation of risk to caribou and Indigenous food security, and for developing and evaluating potential caribou recovery strategies.

Many mountain watersheds provide a reliable source of freshwater for habitat and human use downstream. Hydrodynamics of these basins can be particularly sensitive to change, which may arise from climate change, natural/anthropogenic alteration and other forcing. The timing and intensity of runoff and the recharge of groundwater must be at least partially understood to simulate the potential effects of change. The effects of groundwater on basin drainage are often neglected in simulations due to a lack of empirical data. This study focused on the integration of remote sensing data, geomorphic principles, theoretical distributions of heterogeneity, basin discretization methods, and saturated flow computation to apply a novel technique to understand groundwater behaviour in mountain watersheds. Methods included a geomorphometric analysis to computationally simulate the distribution of geomorphic landforms, which were used to estimate heterogeneity in the shallow subsurface and provide opportunity to evaluate groundwater flow. Morphometric attributes of various landforms were studied and compared to their genetic origin to identify potential landforms. The resulting landforms were subsequently divided into equivalent porous media units (EMUs) based on the theoretical distribution of heterogeneity within landform types. EMUs were evaluated as irregular units used to discretize a saturated groundwater flow model. Groundwater flow was calculated using recharge simulated by any hydrometeorologic model and was routed using Darcian flow from EMU to EMU. Methods of simulating groundwater flow in this study were found to be well suited for the basin type of the study area used (St. Mary Watershed, Montana, USA), albeit with limitations. Results of the geomorphometric analysis compared well with published surficial geology data. The basin discretization method presented in this research would benefit from implicit groundwater flow solving, and application in a basin where abundant data exist. An implicit scheme would allow faster computation and provide the means for a quantitative comparison of basin outflow and water table elevations, which would be useful to further evaluate the suitability of these techniques.

Bamberg et al make an observation connecting Goldbach's conjecture, the crystallographic restriction, and the orders of the elements of the symmetric group. Moreover, several theorems are presented to prove their connection.