A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon.

Understanding direct deforestation drivers at a fine spatial and temporal scale is needed to design appropriate measures for forest management and monitoring. To achieve this, reference datasets with which to design Artificial Intelligence (AI) approaches to classify direct deforestation drivers within areas experiencing forest loss in a detailed, comprehensive and locally-adapted way are needed. This is the case for Cameroon, in the Congo Basin, which has known increasing deforestation rates in recent years. Here, we created an Earth Observation dataset with associated labels to classify detailed direct deforestation drivers in Cameroon, which includes satellite imagery (Landsat and PlanetScope) and auxiliary data on infrastructure and biophysical properties. The dataset provides the following fifteen labels: oil palm, timber, fruit, rubber and other-large scale plantations; grassland/shrubland; small-scale oil palm or maize plantations and other small-scale agriculture; mining; selective logging; infrastructure; wildfires; hunting; and other.

Can we set a global threshold age to define mature forests?

Globally, mature forests appear to be increasing in biomass density (BD). There is disagreement whether these increases are the result of increases in atmospheric CO2 concentrations or a legacy effect of previous land-use. Recently, it was suggested that a threshold of 450 years should be used to define mature forests and that many forests increasing in BD may be younger than this. However, the study making these suggestions failed to account for the interactions between forest age and climate. Here we revisit the issue to identify: (1) how climate and forest age control global forest BD and (2) whether we can set a threshold age for mature forests. Using data from previously published studies we modelled the impacts of forest age and climate on BD using linear mixed effects models. We examined the potential biases in the dataset by comparing how representative it was of global mature forests in terms of its distribution, the climate space it occupied, and the ages of the forests used. BD increased with forest age, mean annual temperature and annual precipitation. Importantly, the effect of forest age increased with increasing temperature, but the effect of precipitation decreased with increasing temperatures. The dataset was biased towards northern hemisphere forests in relatively dry, cold climates. The dataset was also clearly biased towards forests <250 years of age. Our analysis suggests that there is not a single threshold age for forest maturity. Since climate interacts with forest age to determine BD, a threshold age at which they reach equilibrium can only be determined locally. We caution against using BD as the only determinant of forest maturity since this ignores forest biodiversity and tree size structure which may take longer to recover. Future research should address the utility and cost-effectiveness of different methods for determining whether forests should be classified as mature.

Wood production response to climate change will depend critically on forest composition and structure.

Established forests currently function as a major carbon sink, sequestering as woody biomass about 26% of global fossil fuel emissions. Whether forests continue to act as a global sink will depend on many factors, including the response of aboveground wood production (AWP; MgC ha(-1 ) yr(-1) ) to climate change. Here, we explore how AWP in New Zealand's natural forests is likely to change. We start by statistically modelling the present-day growth of 97 199 individual trees within 1070 permanently marked inventory plots as a function of tree size, competitive neighbourhood and climate. We then use these growth models to identify the factors that most influence present-day AWP and to predict responses to medium-term climate change under different assumptions. We find that if the composition and structure of New Zealand's forests were to remain unchanged over the next 30 years, then AWP would increase by 6-23%, primarily as a result of physiological responses to warmer temperatures (with no appreciable effect of changing rainfall). However, if warmth-requiring trees were able to migrate into currently cooler areas and if denser canopies were able to form, then a different AWP response is likely: forests growing in the cool mountain environments would show a 30% increase in AWP, while those in the lowland would hardly respond (on average, -3% when mean annual temperature exceeds 8.0 °C). We conclude that response of wood production to anthropogenic climate change is not only dependent on the physiological responses of individual trees, but is highly contingent on whether forests adjust in composition and structure.

Patterns and drivers of tree mortality in iberian forests: climatic effects are modified by competition.

Tree mortality is a key process underlying forest dynamics and community assembly. Understanding how tree mortality is driven by simultaneous drivers is needed to evaluate potential effects of climate change on forest composition. Using repeat-measure information from c. 400,000 trees from the Spanish Forest Inventory, we quantified the relative importance of tree size, competition, climate and edaphic conditions on tree mortality of 11 species, and explored the combined effect of climate and competition. Tree mortality was affected by all of these multiple drivers, especially tree size and asymmetric competition, and strong interactions between climate and competition were found. All species showed L-shaped mortality patterns (i.e. showed decreasing mortality with tree size), but pines were more sensitive to asymmetric competition than broadleaved species. Among climatic variables, the negative effect of temperature on tree mortality was much larger than the effect of precipitation. Moreover, the effect of climate (mean annual temperature and annual precipitation) on tree mortality was aggravated at high competition levels for all species, but especially for broadleaved species. The significant interaction between climate and competition on tree mortality indicated that global change in Mediterranean regions, causing hotter and drier conditions and denser stands, could lead to profound effects on forest structure and composition. Therefore, to evaluate the potential effects of climatic change on tree mortality, forest structure must be considered, since two systems of similar composition but different structure could radically differ in their response to climatic conditions.

Influences of forest structure, climate and species composition on tree mortality across the eastern US.

Few studies have quantified regional variation in tree mortality, or explored whether species compositional changes or within-species variation are responsible for regional patterns, despite the fact that mortality has direct effects on the dynamics of woody biomass, species composition, stand structure, wood production and forest response to climate change. Using bayesian analysis of over 430,000 tree records from a large eastern US forest database we characterised tree mortality as a function of climate, soils, species and size (stem diameter). We found (1) mortality is U-shaped vs. stem diameter for all 21 species examined; (2) mortality is hump-shaped vs. plot basal area for most species; (3) geographical variation in mortality is substantial, and correlated with several environmental factors; and (4) individual species vary substantially from the combined average in the nature and magnitude of their mortality responses to environmental variation. Regional variation in mortality is therefore the product of variation in species composition combined with highly varied mortality-environment correlations within species. The results imply that variation in mortality is a crucial part of variation in the forest carbon cycle, such that including this variation in models of the global carbon cycle could significantly narrow uncertainty in climate change predictions.