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Although the impacts of climate change on biodiversity are increasing worldwide, few studies have attempted to forecast these impacts on Amazon Tropical Forest. In this study, we estimated the impact of climate change on Amazonian avian assemblages considering range shifts, species loss, vulnerability of ecosystem functioning, future effectiveness of current protected areas and potential climatically stable areas for conservation actions. Species distribution modelling based on two algorithms and three different scenarios of climate change was used to forecast 501 avian species, organized on main ecosystem functions (frugivores, insectivores and nectarivores) for years 2050 and 2070. Considering the entire study area, we estimated that between 4 and 19% of the species will find no suitable habitat. Inside the currently established protected areas, species loss could be over 70%. Our results suggest that frugivores are the most sensitive guild, which could bring consequences on seed dispersal functions and on natural regeneration. Moreover, we identified the western and northern parts of the study area as climatically stable. Climate change will potentially affect avian assemblages in southeastern Amazonia with detrimental consequences to their ecosystem functions. Information provided here is essential to conservation practitioners and decision makers to help on planning their actions.

AbstractEcologists are increasingly aware of the importance of environmental variability in natural systems. Climate change is affecting both the mean and the variability in weather and, in particular, the effect of changes in variability is poorly understood. Organisms are subject to selection imposed by both the mean and the range of environmental variation experienced by their ancestors. Changes in the variability in a critical environmental factor may therefore have consequences for vital rates and population dynamics. Here, we examine ≥90‐year trends in different components of climate (precipitation mean and coefficient of variation (CV); temperature mean, seasonal amplitude and residual variance) and consider the effects of these components on survival and recruitment in a population of Eurasian beavers (n = 242) over 13 recent years. Within climatic data, no trends in precipitation were detected, but trends in all components of temperature were observed, with mean and residual variance increasing and seasonal amplitude decreasing over time. A higher survival rate was linked (in order of influence based on Akaike weights) to lower precipitation CV (kits, juveniles and dominant adults), lower residual variance of temperature (dominant adults) and lower mean precipitation (kits and juveniles). No significant effects were found on the survival of nondominant adults, although the sample size for this category was low. Greater recruitment was linked (in order of influence) to higher seasonal amplitude of temperature, lower mean precipitation, lower residual variance in temperature and higher precipitation CV. Both climate means and variance, thus proved significant to population dynamics; although, overall, components describing variance were more influential than those describing mean values. That environmental variation proves significant to a generalist, wide‐ranging species, at the slow end of the slow‐fast continuum of life histories, has broad implications for population regulation and the evolution of life histories.

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the “high hazardous” category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.

Peatlands are a critical carbon store comprising 30% of the Earth’s terrestrial soil carbon. Sphagnum mosses comprise up to 90% of peat in the northern hemisphere but impacts of climate change on Sphagnum mosses are poorly understood, limiting development of sustainable peatland management and restoration. This study investigates the effects of elevated atmospheric CO2 (eCO2) (800 ppm) and hydrology on the growth of Sphagnum fallax, Sphagnum capillifolium and Sphagnum papillosum and greenhouse gas fluxes from moss–peat mesocosms. Elevated CO2 levels increased Sphagnum height and dry weight but the magnitude of the response differed among species. The most responsive species, S. fallax, yielded the most biomass compared to S. papillosum and S. capillifolium. Water levels and the CO2 treatment were found to interact, with the highest water level (1 cm below the surface) seeing the largest increase in dry weight under eCO2 compared to ambient (400 ppm) concentrations. Initially, CO2 flux rates were similar between CO2 treatments. After week 9 there was a consistent three-fold increase of the CO2 sink strength under eCO2. At the end of the experiment, S. papillosum and S. fallax were greater sinks of CO2 than S. capillifolium and the − 7 cm water level treatment showed the strongest CO2 sink strength. The mesocosms were net sources of CH4 but the source strength varied with species, specifically S. fallax produced more CH4 than S. papillosum and S. capillifolium. Our findings demonstrate the importance of species selection on the outcomes of peatland restoration with regards to Sphagnum’s growth and GHG exchange.

AbstractCellulose and lignin nanoparticles are high‐value‐added products obtained from lignocellulosic biomasses through several steps of cellulose purification and lignin extraction. These steps drastically reduce the potential feedstock revenue when carried out as stand‐alone methodologies. To increase biomass yields, we describe here a strategy to design a biorefinery focused on producing cellulose and lignin nanoparticles as main products, but also aim to recover and benefit from other biomass components using only water‐based processes. Sequential pressurized liquid extractions and diluted acid and alkaline treatments were carried out to fractionate elephant grass biomass, yielding (for every 100 g of biomass): 30 g of cellulose pulp (converted to 9 g of cellulose nanocrystals and 9 g of cellulose nanofibers); 10 g of lignin (used to produce 8.5 g of stable colloidal lignin nanoparticles by probe‐sonication in water); 7.5 g of extractives (e.g. sterols and phenolics) and 23 g of xylose (converted to 4.1 g of furfural). Alternatively, to allow for the flexible use of the cellulose fraction in the proposed biorefinery, 22 g of glucose could be produced by enzymatic hydrolysis. The results demonstrate that water‐based processes are suitable for a holistic use of biomass, providing a comprehensive set of high‐value‐added co‐products that are renewable and cost‐effective chemical, cosmetic, food, polymer and pharmaceutical solutions.

AbstractAimIdentifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects.LocationNew Hampshire and Vermont, USA.MethodsUsing causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high‐emission scenario (RCP8.5) of projected snow and forest biomass change.ResultsOur hypothesis that causal models of climate‐mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well‐supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high‐elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx.Main conclusionsOur comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.

Featured paper: See Editorial p553