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Fig 2 (heatmap) data files and R codeData and R code needed to create Fig 2 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 figure panels. Each file contains evolved D across the range in each of 500 generations of stable climate followed by 1000 generations of climate change.Fig 2 (heatmap).zipFig 3 (D lines) data and R codeData and R code needed to create Fig 3 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 models shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, averaged across 10 runs per cost per model.Fig 3 (D lines).zipFig 4 (delta.D) data and R codeData and R code needed to create Fig 4 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 4 models (ie figure rows) shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change for 30 runs per model.Fig 4 (delta.D).zipFig 6 (D vs density) data and R codeData and R code needed to create Fig 6 in Hargreaves et al (2015) J Evol Biol. Two data files (one for evolved D and one for density) for each of 2 model runs, one with dispersal (dispersal distance =1 as normal) and one run without dispersal (dispersal distance =0).Fig 6 (D vs density).zipAppendix S1 data and R code for each figureData and R code needed to create figures in Appendix S1 in Hargreaves et al (2015) J Evol Biol. All figures remake Fig 3 while varying one parameter. Fig S1.1 shows murate = .005; Fig S1.2 shows avshift = .01, .05, .2; Fig. S1.3 shows K=10; Fig. S1.4 shows effect of eliminating kin selection by randomizing individuals within columns before each dispersal event. For each figure there is 1 data file per model. Each data file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, for 10 runs per cost.Appendix S1.zipModel code Matlab fileCode to run the model simulations.rangeshift (for dryad).mFig 5 (extinction threshold) Matlab codeMatlab code to run the simulations necessary to determine the relationship between the speed of climate change (avshift) and probability of extinction.rangeshift_thresh (for dryad).m Dispersal ability will largely determine whether species track their climatic niches during climate change, a process especially important for populations at contracting (low-latitude/low-elevation) range limits that otherwise risk extinction. We investigate whether dispersal evolution at contracting range limits is facilitated by two processes that potentially enable edge populations to experience and adjust to the effects of climate deterioration before they cause extinction: (i) climate-induced fitness declines towards range limits and (ii) local adaptation to a shifting climate gradient. We simulate a species distributed continuously along a temperature gradient using a spatially explicit, individual-based model. We compare range-wide dispersal evolution during climate stability vs. directional climate change, with uniform fitness vs. fitness that declines towards range limits (RLs), and for a single climate genotype vs. multiple genotypes locally adapted to temperature. During climate stability, dispersal decreased towards RLs when fitness was uniform, but increased when fitness declined towards RLs, due to highly dispersive genotypes maintaining sink populations at RLs, increased kin selection in smaller populations, and an emergent fitness asymmetry that favoured dispersal in low-quality habitat. However, this initial dispersal advantage at low-fitness RLs did not facilitate climate tracking, as it was outweighed by an increased probability of extinction. Locally adapted genotypes benefited from staying close to their climate optima; this selected against dispersal under stable climates but for increased dispersal throughout shifting ranges, compared to cases without local adaptation. Dispersal increased at expanding RLs in most scenarios, but only increased at the range centre and contracting RLs given local adaptation to climate.

File format: NetCDFSimulated/analyzed periods: 1989-2010 (current) and 2079-2100 (future)The repository for the analysis code is attached.Entry scripts for the figures are:- figure1, 4: src/lake_effect_snow/hles_cc/plot_monthly_histograms_cc_and_domain.py- figure2(partially lake ice fraction), figure3: src/lake_effect_snow/hles_cc_validation/validate_hles_and_related_params_biases_and_obs.py- figure5: src/lake_effect_snow/hles_cc/plot_cc_2d_all_variables_for_all_periods_001.py- figure6: src/lake_effect_snow/hles_cc/hles_tt_and_pr_correlations_mean_ice_fraction.py- cold_air.m for part of Fig. 2 and hles_intensity.m for Fig. 7 The dataset contains Heavy Lake Effect Snowfall (HLES) and related parameters from GEM outputs (RCP8.5, 10 km horizontal resolution, Laurentian Great Lakes region, driven by CanESM2 at the boundaries) and observation datasets. Observation data included are: interpolated to the model grid Daymet 2m air temperature and total precipitation, CIS-NIC ice concentration observations, and REA-Interim near-surface winds.

List of DIGE-spots with homology with sequences in databases that are up-regulated in response to cold acclimation (ANOVA, Pâ

1.Increasing demand for benefits provided by riverine ecosystems threatens their sustainable provision. The ecosystem service concept is a promising avenue to inform riverine ecosystem management, but several challenges have prevented the application of this concept. 2.We quantitatively assess the field of riverine ecosystem services’ progress in meeting these challenges. We highlight conceptual and methodological gaps, which have impeded integration of the ecosystem service concept into management. 3.Across 89 relevant studies, 33 unique riverine ecosystem services were evaluated, for a total of 404 ecosystem service quantifications. Studies quantified between one and 23 ecosystem services, although the majority (55%) evaluated three or less. Among studies that quantified more than one service, 58% assessed interactions between services. Most studies (71%) did not include stakeholders in their quantification protocols, and 34% developed future scenarios of ecosystem service provision. Almost half (45%) conducted monetary valuation, using 16 methods. Only 9% did not quantify or discuss uncertainties associated with service quantification. The indicators and methods used to quantify the same type of ecosystem service varied. Only 3% of services used indicators of capacity, flow, and demand in concert. 4.Our results suggest indicators, data sources, and methods for quantifying riverine ecosystem services should be more clearly defined and accurately represent the service they intend to quantify. Furthermore, more assessments of multiple services across diverse spatial extents and of riverine service interactions are needed, with better inclusion of stakeholders. Addressing these challenges will help riverine ecosystem service science inform river management. 5.Synthesis and applications. The ecosystem service concept has great potential to inform riverine ecosystem management and decision making processes. However, this review of riverine ecosystem service quantification uncovers several remaining research gaps, impeding effective use of this tool to manage riverine ecosystems. We highlight these gaps and point to studies showcasing methods that can be used to address them. Review of riverine ecosystem service quantification studiesThis file contains a database of studies that quantified riverine ecosystem services prior to April 2016, as well as quantitative data on the location of each study, the types and numbers of ecosystem services evaluated, and the methods used to quantify services.Hanna_Riverine ES Review Database.xlsx

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: Gabriel Marineau-Plante, Frank Juvenal, Adam Langlois, Daniel Fortin, Armand Soldera, Pierre D. Harvey|2018|Chem.Commun.|54|976|doi:10.1039/C7CC09503A

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: Gabriel Marineau-Plante, Frank Juvenal, Adam Langlois, Daniel Fortin, Armand Soldera, Pierre D. Harvey|2018|Chem.Commun.|54|976|doi:10.1039/C7CC09503A

Background: Sea ice across the Arctic is declining and altering physical characteristics of marine ecosystems. Polar bears (Ursus maritimus) have been identified as vulnerable to changes in sea ice conditions. We use sea ice projections for the Canadian Arctic Archipelago from 2006 – 2100 to gain insight into the conservation challenges for polar bears with respect to habitat loss using metrics developed from polar bear energetics modeling. Principal Findings: Shifts away from multiyear ice to annual ice cover throughout the region, as well as lengthening ice-free periods, may become critical for polar bears before the end of the 21st century with projected warming. Each polar bear population in the Archipelago may undergo 2–5 months of ice-free conditions, where no such conditions exist presently. We identify spatially and temporally explicit ice-free periods that extend beyond what polar bears require for nutritional and reproductive demands. Conclusions/Significance: Under business-as-usual climate projections, polar bears may face starvation and reproductive failure across the entire Archipelago by the year 2100. Depth-bathymetry fileUse as land mask file when depth=0depth.ncMITgcm_SeaIce_GFDL_CM3_RCP85_2006-2100Monthly average sea ice and snow conditions in the Canadian Arctic Archipelago 2006-2100 under climate warming scenario RCP85. Model output in netcdf files, time steps of 1 month starting on January 2006.MITgcm_SeaIce_GFDL_CM3_RCP85_2006_2100.zip

Related Article: Nathan Yee, Afshin Dadvand, Dmitrii F. Perepichka|2020|Mater. Chem. Front.|4|3669|doi:10.1039/D0QM00500B

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: N. M. Bonanno, A. J. Lough, K. E. Prosser, C. J. Walsby, P. K. Poddutoori, M. T. Lemaire|2016|Dalton Trans.|45|5460|doi:10.1039/C5DT04061B

Spatial heterogeneity in the strength of trophic interactions is a fundamental property of food web spatial dynamics. The feeding effort of herbivores should reflect adaptive decisions that only become rewarding when foraging gains exceed 1) the metabolic costs, 2) the missed opportunity costs of not foraging elsewhere, and 3) the foraging costs of anti-predator behaviour. Two aspects of these costs remain largely unexplored: the link between the strength of plant-herbivore interactions and the spatial scale of food-quality assessment, and the predator-prey spatial game. We modeled the foraging effort of free-ranging plains bison (Bison bison bison) in winter, within a mosaic of discrete meadows. Spatial patterns of bison herbivory were largely driven by a search for high net energy gains and, to a lesser degree, by the spatial game with grey wolves (Canis lupus). Bison decreased local feeding effort with increasing metabolic and missed opportunity costs. Bison herbivory was most consistent with a broad-scale assessment of food patch quality, i.e., bison grazed more intensively in patches with a low missed opportunity cost relative to other patches available in the landscape. Bison and wolves had a higher probability of using the same meadows than expected randomly. This co-occurrence indicates wolves are ahead in the spatial game they play with bison. Wolves influenced bison foraging at fine scale, as bison tended to consume less biomass at each feeding station when in meadows where the risk of a wolf's arrival was relatively high. Also, bison left more high-quality vegetation in large than small meadows. This behavior does not maximize their energy intake rate, but is consistent with bison playing a shell game with wolves. Our assessment of bison foraging in a natural setting clarifies the complex nature of plant-herbivore interactions under predation risk, and reveals how spatial patterns in herbivory emerge from multi-scale landscape heterogeneity. HarveyFortinDataset S1Field data