As global climate conditions continue to change, disturbance regimes and environmental drivers will continue to shift, impacting global vegetation dynamics. Following a period of vegetation greening, there has been a progressive increase in remotely sensed vegetation browning globally. Given the many societal benefits that forests provide, it is critical that we understand vegetation dynamic alterations. Here, we review associative drivers, impacts, and feedbacks, revealing the complexity of browning. Concomitant increases in browning include the weakening of ecosystem services and functions and alterations to vegetation structure and species composition, as well as the development of potential positive climate change feedbacks. Also discussed are the current challenges in browning detection and understanding associated impacts and feedbacks. Finally, we outline recommended strategies.

Increased greenhouse gas emissions are causing unprecedented climate change, which is, in turn, altering emissions and removals (referring to the oxidation of atmospheric CH4 by methanotrophs within the soil) of the atmospheric CH4 in terrestrial ecosystems. In the global CH4 budget, wetlands are the dominant natural source and upland soils are the primary biological sink. However, it is unclear whether and how the soil CH4 exchanges across terrestrial ecosystems and the atmosphere will be affected by warming and changes in precipitation patterns. Here, we synthesize 762 observations of in situ soil CH4 flux data based on the chamber method from the past three decades related to temperature and precipitation changes across major terrestrial ecosystems worldwide. Our meta-analysis reveals that warming (average warming of +2°C) promotes upland soil CH4 uptake and wetland soil CH4 emission. Decreased precipitation (ranging from −100% to −7% of local mean annual precipitation) stimulates upland soil CH4 uptake. Increased precipitation (ranging from +4% to +94% of local mean annual precipitation) accelerates the upland soil CH4 emission. By 2100, under the shared socioeconomic pathway with a high radiative forcing level (SSP585), CH4 emissions from global terrestrial ecosystems will increase by 22.8 ± 3.6 Tg CH4 yr−1 as an additional CH4 source, which may be mainly attributed to the increase in precipitation over 30°N latitudes. Our meta-analysis strongly suggests that future climate change would weaken the natural buffering ability of terrestrial ecosystems on CH4 fluxes and thus contributes to a positive feedback spiral.

Globally, increasing drought-induced tree mortality rates under climate change are projected to have far-reaching effects on forest ecosystems. Among these forest systems, the boreal forest is considered a ‘tipping element’ of the Earth's climate system. This forest biome plays a critical role in ecosystem services, structures and functions while being highly sensitive to drought stress. Although process-based models are important tools in ecological research, very few have yet been developed that integrate advanced physiological mechanisms to simulate drought-induced mortality in boreal forests. Accordingly, based on the process-based TRIPLEX model, this study introduces the new TRIPLEX-Mortality submodule for the Canadian boreal forests at the stand level, that for the first time successfully incorporates two advanced drought-induced physiological mortality mechanisms (i.e., hydraulic failure and carbon starvation). To calibrate and validate the model, 73 permanent sample plots (PSPs) were selected across Canada's boreal forests. Results confirm a good agreement between simulated mortality and mortality observations (R2=0.79; P<0.01; IA=0.94), demonstrating good model performance in simulating drought-induced mortality in boreal forests. Sensitivity analysis indicated that parameter sensitivity increased as drought intensified, and the shape parameter (c) for calculating percentage loss of conductivity (PLC) was the most sensitive parameter (average SI = -3.51) to simulate tree mortality. Furthermore, the results of model input sensitivity analysis also showed that the model can capture changes in mortality under different drought scenarios. Consequently, our model is suitable for simulating drought-induced mortality in boreal forests while also providing new insight into improving model simulations for tree mortality and associated carbon dynamics in a progressively warmer and drier world.

Spruce budworm (SBW) outbreaks are a major natural disturbance in boreal forests of eastern North America. During large?scale infestations, aerial spraying of bacterial insecticides is used to suppress local high?density SBW populations. While the primary goal of spraying is the protection of wood volume for later harvest, it should also maintain carbon stored in trees. This study provides the first quantitative analysis of the efficacy of aerial spraying against SBW on carbon dynamics in balsam fir, spruce, and mixed fir–spruce forests. In this study, we used the TRIPLEX?Insect model to simulate carbon dynamics with and without spray applications in 14 sites of the boreal forest located in various regions of Québec. We found that the efficacy of aerial spraying on reducing annual defoliation was greater in the early stage (<5 yr since the outbreak began) of the outbreak than in later (5–10 yr since the outbreak began) stage. Our results showed that more net ecosystem productivity is maintained in balsam fir (the most vulnerable species) than in either spruce or mixed fir–spruce forests following spraying. Also, average losses in aboveground biomass due to the SBW following spraying occurred more slowly than without spraying in balsam fir forests. Our findings suggest that aerial spraying could be used to maintain carbon in conifer forests during SBW disturbances, but that the efficacy of spray programs is affected by host species and stage of the SBW outbreak.

L’apprentissage automatique, une branche importante de l’intelligence artificielle, est de plus en plus mis en application dans le domaine des sciences comme l’écologie forestière. Ici, nous examinons et faisons le point sur trois méthodes d’apprentissage automatique généralement utilisées incluant l’apprentissage par arbre de décision, le réseau de neurones artificiels et la machine à vecteurs de support, ainsi que leurs applications au niveau de quatre aspects différents de l’écologie forestière au cours de la dernière décennie. Ces applications incluent : (i) les modèles de répartition des espèces, (ii) les cycles de carbone, (iii) l’évaluation et la prédiction des dangers et (iv) d’autres applications en gestion forestière. Alors que les approches d’apprentissage automatique sont utiles au niveau de la classification, de la modélisation et de la prédiction en recherche dans le domaine de l’écologie forestière, le développement accru des technologies d’apprentissage automatique est limité par le manque de données pertinentes et le « seuil relativement plus élevé » des applications. Cependant, l’utilisation combinée d’algorithmes multiples et de communication et coopération améliorées entre les chercheurs en écologie et les concepteurs d’apprentissage automatique présente toujours des défis importants et des tâches en vue de l’amélioration de la recherche écologique à l’avenir. Nous laissons entendre que les applications futures d’apprentissage automatique en écologie deviendront un outil de plus en plus intéressant pour les écologistes face aux « données massives » et que les écologistes auront accès à plus de types de données tel que le son et la vidéo dans un proche avenir ce qui ouvrira probablement de nouvelles avenues en matière de recherche en écologie forestière. [Traduit par la Rédaction]

Forest insects are major disturbances that induce tree mortality in eastern coniferous (or fir-spruce) forests in eastern North America. The spruce budworm (SBW) (Choristoneura fumiferana [Clemens]) is the most devastating insect causing tree mortality. However, the relative importance of insect-caused mortality versus tree mortality caused by other agents and how this relationship will change with climate change is not known. Based on permanent sample plots across eastern Canada, we combined a logistic model with a negative model to estimate tree mortality. The results showed that tree mortality increased mainly due to forest insects. The mean difference in annual tree mortality between plots disturbed by insects and those without insect disturbance was 0.0680 per year (P < 0.0001, T-test), and the carbon sink loss was about 2.87t C ha?1 year?1 larger than in natural forests. We also found that annual tree mortality increased significantly with the annual climate moisture index (CMI) and decreased significantly with annual minimum temperature (Tmin), annual mean temperature (Tmean) and the number of degree days below 0°C (DD0), which was inconsistent with previous studies (Adams et al. 2009; van Mantgem et al. 2009; Allen et al. 2010). Furthermore, the results for the trends in the magnitude of forest insect outbreaks were consistent with those of climate factors for annual tree mortality. Our results demonstrate that forest insects are the dominant cause of the tree mortality in eastern Canada but that tree mortality induced by insect outbreaks will decrease in eastern Canada under warming climate.