Nonlinearity in interspecific interactions in response to climate change: Cod and haddock as an example

Climate change has profound ecological effects, yet our understanding of how trophic interactions among species are affected by climate change is still patchy. The sympatric Atlantic haddock and cod are co‐occurring across the North Atlantic. They compete for food at younger stages and thereafter the former is preyed by the latter. Climate change might affect the interaction and coexistence of these two species. Particularly, the increase in sea temperature (ST) has been shown to affect distribution, population growth and trophic interactions in marine systems. We used 33‐year long time series of haddock and cod abundances estimates from two data sources (acoustic and trawl survey) to analyse the dynamic effect of climate on the coexistence of these two sympatric species in the Arcto‐Boreal Barents Sea. Using a Bayesian state‐space threshold model, we demonstrated that long‐term climate variation, as expressed by changes of ST, affected species demography through different influences on density‐independent processes. The interaction between cod and haddock has shifted in the last two decades due to an increase in ST, altering the equilibrium abundances and the dynamics of the system. During warm years (ST over ca. 4°C), the increase in the cod abundance negatively affected haddock abundance while it did not during cold years. This change in interactions therefore changed the equilibrium population size with a higher population size during warm years. Our analyses show that long‐term climate change in the Arcto‐Boreal system can generate differences in the equilibrium conditions of species assemblages.     

Temperature and precipitation at migratory grounds influence demographic trends of an Arctic‐breeding bird

Anthropogenic climate disruption, including temperature and precipitation regime shifts, has been linked to animal population declines since the mid‐20th century. However, some species, such as Arctic‐breeding geese, have thrived during this period. An increased understanding of how climate disruption might link to demographic rates in thriving species is an important perspective in quantifying the impact of anthropogenic climate disruption on the global state of nature. The Greenland barnacle goose (Branta leucopsis) population has increased tenfold in abundance since the mid‐20th century. A concurrent weather regime shift towards warmer, wetter conditions occurred throughout its range in Greenland (breeding), Ireland and Scotland (wintering) and Iceland (spring and autumn staging). The aim of this study was to determine the relationship between weather and demographic rates of Greenland barnacle geese to discern the role of climate shifts in the population trend. We quantified the relationship between temperature and precipitation and Greenland barnacle goose survival and productivity over a 50 year period from 1968 to 2018. We detected significant positive relationships between warmer, wetter conditions on the Icelandic spring staging grounds and survival. We also detected contrasting relationships between warmer, wetter conditions during autumn staging and survival and productivity, with warm, dry conditions being the most favourable for productivity. Survival increased in the latter part of the study period, supporting the possibility that spring weather regime shifts contributed to the increasing population trend. This may be related to improved forage resources, as warming air temperatures have been shown to improve survival rates in several other Arctic and northern terrestrial herbivorous species through indirect bottom‐up effects on forage availability.     

Origin of volatile organic compound emissions from subarctic tundra under global warming

Warming occurs in the Arctic twice as fast as the global average, which in turn leads to a large enhancement in terpenoid emissions from vegetation. Volatile terpenoids are the main class of biogenic volatile organic compounds (VOCs) that play crucial roles in atmospheric chemistry and climate. However, the biochemical mechanisms behind the temperature‐dependent increase in VOC emissions from subarctic ecosystems are largely unexplored. Using ¹³CO₂‐labeling, we studied the origin of VOCs and the carbon (C) allocation under global warming in the soil–plant–atmosphere system of contrasting subarctic heath tundra vegetation communities characterized by dwarf shrubs of the genera Salix or Betula. The projected temperature rise of the subarctic summer by 5°C was realistically simulated in sophisticated climate chambers. VOC emissions strongly depended on the plant species composition of the heath tundra. Warming caused increased VOC emissions and significant changes in the pattern of volatiles toward more reactive hydrocarbons. The ¹³C was incorporated to varying degrees in different monoterpene and sesquiterpene isomers. We found that de novo monoterpene biosynthesis contributed to 40%–44% (Salix) and 60%–68% (Betula) of total monoterpene emissions under the current climate, and that warming increased the contribution to 50%–58% (Salix) and 87%–95% (Betula). Analyses of above‐ and belowground ^12/13C showed shifts of C allocation in the plant–soil systems and negative effects of warming on C sequestration by lowering net ecosystem exchange of CO₂ and increasing C loss as VOCs. This comprehensive analysis provides the scientific basis for mechanistically understanding the processes controlling terpenoid emissions, required for modeling VOC emissions from terrestrial ecosystems and predicting the future chemistry of the arctic atmosphere. By changing the chemical composition and loads of VOCs into the atmosphere, the current data indicate that global warming in the Arctic may have implications for regional and global climate and for the delicate tundra ecosystems.     

Evidence of non‐stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests

Climate and forest structure are considered major drivers of forest demography and productivity. However, recent evidence suggests that the relationships between climate and tree growth are generally non‐stationary (i.e. non‐time stable), and it remains uncertain whether the relationships between climate, forest structure, demography and productivity are stationary or are being altered by recent climatic and structural changes. Here we analysed three surveys from the Spanish Forest Inventory covering c. 30 years of information and we applied mixed and structural equation models to assess temporal trends in forest structure (stand density, basal area, tree size and tree size inequality), forest demography (ingrowth, growth and mortality) and above‐ground forest productivity. We also quantified whether the interactive effects of climate and forest structure on forest demography and above‐ground forest productivity were stationary over two consecutive time periods. Since the 1980s, density, basal area and tree size increased in Iberian forests, and tree size inequality decreased. In addition, we observed reductions in ingrowth and growth, and increases in mortality. Initial forest structure and water availability mainly modulated the temporal trends in forest structure and demography. The magnitude and direction of the interactive effects of climate and forest structure on forest demography changed over the two time periods analysed indicating non‐stationary relationships between climate, forest structure and demography. Above‐ground forest productivity increased due to a positive balance between ingrowth, growth and mortality. Despite increasing productivity over time, we observed an aggravation of the negative effects of climate change and increased competition on forest demography, reducing ingrowth and growth, and increasing mortality. Interestingly, our results suggest that the negative effects of climate change on forest demography could be ameliorated through forest management, which has profound implications for forest adaptation to climate change.     

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha^?1 year^?1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha^?1 year^?1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha^?1 year^?1 under projected climate. On average, this was 0.3 Mg C ha^?1 year^?1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.     

Assessing influence in biofuel production and ecosystem services when environmental changes affect plant–pest relationships

Climate change is currently affecting both biodiversity and human activities; land use change and greenhouse gas emissions are the main drivers. Many agricultural services are affected by the change, which in turn reflects on the basic provisioning services, which supply food, fibre and biofuels. Biofuels are getting increasing interest because of their sustainability potential. Jatropha curcas gained popularity as a biodiesel crop, due to its ease of cultivation even in harsh environmental conditions. Notwithstanding its high economic importance, few studies are available about its co‐occurrence with pests of the genus Aphthona in sub‐Saharan Africa, where these insects feed on J. curcas, leading to relevant economic losses. Using ecological niche modelling and GIS post‐modelling analyses, we infer the current and future suitable territories for both these taxa, delineating areas where J. curcas cultivations may occur without suffering the presence of Aphthona, in the context of future climate and land use changing. We introduce an area‐normalized index, the ‘Potential‐Actual Cultivation Index’, to better depict the ratio between the suitable areas shared both by the crop and its pest, and the number of actual cultivations, in a target country. Moreover, we find high economic losses (~?50%) both in terms of carbon sequestration and in biodiesel production when J. curcas co‐occur with the Aphthona cookei species group.     

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Crop residue exploitation for bioenergy can play an important role in climate change mitigation without jeopardizing food security, but it may be constrained by impacts on soil organic carbon (SOC) stocks, and market, logistic and conversion challenges. We explore opportunities to increase bioenergy potentials from residues while reducing environmental impacts, in line with sustainable intensification. Using the case study of North Rhine‐Westphalia in Germany, we employ a spatiotemporally explicit approach combined with stakeholder interviews. First, the interviews identify agronomic and environmental impacts due to the potential reduction in SOC as the most critical challenge associated with enhanced crop residue exploitation. Market and technological challenges and competition with other residue uses are also identified as significant barriers. Second, with the use of agroecosystem modelling and estimations of bioenergy potentials and greenhouse gas emissions till mid‐century, we evaluate the ability of agricultural management to tackle the identified agronomic and environmental challenges. Integrated site‐specific management based on (a) humus balancing, (b) optimized fertilization and (c) winter soil cover performs better than our reference scenario with respect to all investigated variables. At the regional level, we estimate (a) a 5% increase in technical residue potentials and displaced emissions from substituting fossil fuels by bioethanol, (b) an 8% decrease in SOC losses and associated emissions, (c) an 18% decrease in nitrous oxide emissions, (d) a 37% decrease in mineral fertilizer requirements and emissions from their production and (e) a 16% decrease in nitrate leaching. Results are spatially variable and, despite improvements induced by management, limited amounts of crop residues are exploitable for bioenergy in areas prone to SOC decline. In order to sustainably intensify crop residue exploitation for bioenergy and reconcile climate change mitigation with other sustainability objectives, such as those on soil and water quality, residue management needs to be designed in an integrated and site‐specific manner.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance

The Neotropical region exhibits the greatest worldwide diversity and the diversification history of several clades is related to the puzzling geomorphologic and climatic history of this region. The freshwater Amazon ecoregion contains the main hydrographic basins of the Neotropical region that are highly dendritic and ecologically diverse. It contains a rich and endemic fish fauna, including one of its most iconic and economically important representatives, the bony-tongue Arapaima gigas (Teleostei, Osteoglossiformes). Here, we evaluated the projected distribution of the genus in different historical periods (Present, Last Glacial Maximum, Last Interglacial Maximum and Near Future) and interpreted these results in light of the genomic diversity and modeled historical demography. For that, we combined species distribution models, population genetic analysis using SNPs and deep learning model selection. We analyzed a representative sample of the genus from the two basins where it naturally occurs, four localities in the Amazon (Am) and three in the Tocantins-Araguaia (To-Ar) basin, as well as individuals from three fish farms. We inferred a potentially smaller distribution in the glacial period, with a possible refuge in central Am. Our genetic data agrees with this result, suggesting a higher level of genetic diversity in the Am basin, compared to that observed in To-Ar. Our deep learning model comparison indicated that the To-Ar basin was colonized by the population from the Am basin. Considering a global warming scenario in the near future, A. gigas could reach an even larger range, especially if anthropogenic related dispersal occurs, potentially invading new areas and impacting their communities.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.