Importance of disturbance history on net primary productivity in the world's most productive forests and implications for the global carbon cycle

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Tree mortality and carbon emission as a function of wildfire severity in south-eastern Australian temperate forests

Disturbance trends over recent decades indicate that climate change is resulting in increased fire severity and extent in Australia's temperate Eucalyptus forests. As disturbance cycles become shorter and more severe, empirical measurements are required to identify potential change in forest carbon (C) stock and emissions. However, such estimates are rare in the literature. The 2019-2020 wildfires burnt through 6 to 7 million ha of mainly temperate open Eucalyptus forest in south-east Australia, with top down emission estimates ranging from 97 to 130 tonnes CO₂ ha^-1. Study sites that had been assessed for all aboveground C pools prior to the wildfires, were burnt in January 2020 by wildfire that varied in severity. Here we quantify the impact of high and low/moderate fire severities on tree mortality, C loss and C redistribution and assess implications for future C storage in these temperate Eucalyptus forests. Higher fire severity resulted in greater overstorey tree mortality but not understorey or loss of dead standing trees than in low/moderate severity fires. High severity fires combusted almost twice as much C from live trees (42 Mg C ha^-1) as low/moderate severity fires (25 Mg C ha^-1), while C loss from dead standing trees was similar among fire severity classes (average 17 Mg C ha^-1). Total aboveground C lost across study sites was 42 Mg C ha^-1 for high and 47 Mg C ha^-1 for low/moderate severity, with an average of 45 Mg C ha^-1 equivalent to 15 % (high severity) and 14 % (low/moderate severity) of AGC. Extrapolating our findings to other tall to medium open Eucalyptus forests across Victoria revealed that 37.33 ± 12.25 Tg C (mean ± s.e.) or 152 ± 50 Mg CO₂ ha^-1 was lost to the atmosphere from the 0.9 million ha of these productive forests, equating to about 20 % of Australia's total net annual emissions.

Forest disturbances and climate constrain carbon allocation dynamics in trees

Forest disturbances such as drought, fire, and logging affect the forest carbon dynamics and the terrestrial carbon sink. Forest mortality after disturbances creates uncertainties that need to be accounted for to understand forest dynamics and their associated C-sink. We combined data from permanent resampling plots and biomass oriented dendroecological plots to estimate time series of annual woody biomass growth (ABI) in several forests. ABI time series were used to benchmark a vegetation model to analyze dynamics in forest productivity and carbon allocation forced by environmental variability. The model implements source and sink limitations explicitly by dynamically constraining carbon allocation of assimilated photosynthates as a function of temperature and moisture. Bias in tree-ring reconstructed ABI increased back in time from data collection and with increasing disturbance intensity. ABI bias ranged from zero, in open stands without recorded mortality, to over 100% in stands with major disturbances such as thinning or snowstorms. Stand leaf area was still lower than in control plots decades after heavy thinning. Disturbances, species life-history strategy and climatic variability affected carbon-partitioning patterns in trees. Resprouting broadleaves reached maximum biomass growth at earlier ages than nonresprouting conifers. Environmental variability and leaf area explained much variability in woody biomass allocation. Effects of stand competition on C-allocation were mediated by changes in stand leaf area except after major disturbances. Divergence between tree-ring estimated and simulated ABI were caused by unaccounted changes in allocation or misrepresentation of some functional process independently of the model calibration approach. Higher disturbance intensity produced greater modifications of the C-allocation pattern, increasing error in reconstructed biomass dynamics. Legacy effects from disturbances decreased model performance and reduce the potential use of ABI as a proxy to net primary productivity. Trait-based dynamics of C-allocation in response to environmental variability need to be refined in vegetation models.

Effects of prescribed fire frequency on wildfire emissions and carbon sequestration in a fire adapted ecosystem using a comprehensive carbon model

Prescribed fire to reduce forest fuels has been routinely applied to reduce wildfire risk in many parts of the world. It has also been proposed that prescribed fire can be used to mitigate greenhouse gas (GHG) emissions. Although prescribed fire creates emissions, if the treatment also decreases the incidence of subsequent wildfires, it is possible for the net outcome to be an emissions decline. Previous studies have suggested prescribed fire, at the frequencies required to materially impact wildfire occurrence, generally leads to net emissions increases. A focus on emissions means any change in carbon storage within the ecosystem remains unaccounted for; because living, dead, and soil carbon pools are characterized by different residence times, a re-distribution of carbon amongst these pools may either reduce or increase long-term ecosystem carbon stores. A full ecosystem carbon model has been developed to investigate the implications of prescribed fire management on total Net Ecosystem Carbon Balance (NECB), inclusive of both emissions and carbon storage. Consistent with previous work, the results suggested limited potential for reducing net GHG emissions through applying prescribed fire, with higher emissions from prescribed fire approximately offset by lower emissions and avoided carbon losses from the subsequent reduction in wildfire frequency. For example, shortening the prescribed fire interval from 25 to 10 years resulted in a NECB sequestration that was typically less than ±0.4 Mg C ha^-1 yr^-1, or less than approximately 0.1% of the total ecosystem carbon storage. Hence, whilst there was limited opportunity for achieving emission abatement outcomes from changing prescribed fire management, there were no significant emission penalties for doing so. These results suggest land managers should be free to adopt prescribed fire regimes to target specific management outcomes, without significantly impacting net emissions or total ecosystem carbon storage over the long term.

How disturbance history alters invasion success: biotic legacies and regime change

Disturbance is a key factor shaping ecological communities, but little is understood about how the effects of disturbance processes accumulate over time. When disturbance regimes change, historical processes may influence future community structure, for example, by altering invasibility compared to communities with stable regimes. Here, we use an annual plant model to investigate how the history of disturbance alters invasion success. In particular, we show how two communities can have different outcomes from species introduction, solely due to past differences in disturbance regimes that generated different biotic legacies. We demonstrate that historical differences can enhance or suppress the persistence of introduced species, and that biotic legacies generated by stable disturbance history decay over time, though legacies can persist for unexpectedly long durations. This establishes a formal theoretical foundation for disturbance legacies having profound effects on communities, and highlights the value of further research on the biotic legacies of disturbance.

Multiscale assessment of the impact on air quality of an intense wildfire season in southern Italy

The summer of 2017 in the Calabria Region (South Italy) was an exceptional wildfire season with the largest area burned by wildfires in the last 11 years (2008-2019). The equivalent black carbon (EBC) and carbon monoxide (CO) measurements, recorded at the high-altitude Global Atmosphere Watch (GAW) Monte Curcio (MCU) regional station, were analyzed to establish the wildfires' impact on air quality, human health, and the ecosystem. A method was applied to identify the possible wildfires that influenced the air quality based on the integration of fire data (both satellite and ground-based) and the high-resolution WRF-HYSPLIT trajectories. The satellite-based fires applied to WRF-HYSPLIT with 10 km of spatial resolution allowed us to establish that for 52.5% of total cases, wildfires were located outside the Calabria Region, and they were influenced by long-range transport. Nonetheless, the impact on human health, qualitatively evaluated in terms of passively smoked cigarettes (PSC) corresponding to the EBC, was greater when wildfires were local. Indeed, for wildfires located mainly in Calabria, the equivalent PSC ranged from 2.75 to 11.08. This maximum PSC value was close to the daily number of smoked cigarettes in Calabria (approximately 12.4). Even if this analogy does not imply a proportional effect between the estimated number of cigarettes smoked and the effective wildfire EBC exposure, this result suggests that wildfire emissions may have negative effects on people's health. Moreover, a focus on the Calabria Region was conducted using high-resolution ground-based GPS and higher resolution WRF-HYSPLIT back-trajectories (2 km) to measure wildfires. The validity of the methodology was confirmed by the EBC and CO positive correlation with the ratio between the identified ground-based burned areas and the distance from the sampling station. Moreover, the impact on the ecosystem was studied by analyzing the land vegetation loss due to the wildfires that contributed to air quality reduction at the MCU station. A total of more than 1679 ha of vegetation burned, the main losses comprising forests and shrubland.

Understanding forest dynamics by integrating age and environmental change

How much carbon a forest ecosystem can sequester is determined by both postdisturbance regrowth and environmentally modified growth. Disturbance causes sharp declines in the short term and is followed by regrowth in the long term. Environmental change may alter carbon accumulation through increasing CO₂ , nitrogen deposition and climate change. Regrowth and modified growth occur simultaneously, yet they are usually studied separately and assessed using an additive approach. Alternatively, an interactive approach using hierarchical models can address their concurrent nature and evaluate their joint effects. Hierarchical models are informed by forest age data, which have recently become available at global scales. The age-based hierarchical framework provides a coherent and feasible way to integrate regrowth and modified growth in understanding forest dynamics.

Models ignoring spatial heterogeneities of forest age will significantly overestimate the climate effects on litterfall in China

Litterfall is an important process that links vegetation and soil pools and plays an important role in the maintenance of soil fertility. Although studies indicated that climate will significantly affect forest litterfall, the role of biotic factors such as the spatial heterogeneity of forest age, remains unclear. In this study, we built an updated dataset of litterfall in China and explored the key drivers affecting forest litterfall by establishing optimal linear mixed models (OLMMs). The potential bias of models and their spatial patterns were then evaluated based on the OLMMs and remotely sensed and China's forest inventory data. The results showed the mean annual temperature (MAT) and forest age were the key drivers affecting forest litterfall. Abiotic factors and forest age and height together accounted for 77.5% of the variation in observed litterfall. Although forest age and height did not apparently enhance the coefficient of determination (R²), these factors significantly decreased spatial errors. Therefore, if the model contains only climate factors and the spatial patterns of biotic factors are ignored, it will produce high spatial errors (-52% to 92%). In addition, when forest age and height were not considered, variation of litterfall explained by forest age was inappropriately attributed to MAT, which significantly overestimated the importance of climate factors on forest litterfall. Specifically, litterfall was overestimated for young forests and underestimated for old forests if the model did not contain forest age in China. Models that ignored forest age significantly overestimated the contribution of climatic factors on forest litterfall and produced high spatially specific errors. The comparison of the litterfall modeled by OLMMs and the remote sensing-based net primary production (NPP) indicated that litterfall and NPP are strongly dependent, and the ratio of litterfall to NPP linearly increased with forest age.

Sustained Biomass Carbon Sequestration by China’s Forests from 2010 to 2050

China’s forests have functioned as important carbon sinks. They are expected to have substantial future potential for biomass carbon sequestration (BCS) resulting from afforestation and reforestation. However, previous estimates of forest BCS have included large uncertainties due to the limitations of sample size, multiple data sources, and inconsistent methodologies. This study refined the BCS estimation of China’s forests from 2010 to 2050 using the national forest inventory data (FID) of 2009−2013, as well as the relationships between forest biomass and stand age retrieved from field observations for major forest types in different regions of China. The results showed that biomass–age relationships were well-fitted using field data, with respective R2 values more than 0.70 (p < 0.01) for most forest types, indicating the applicability of these relationships developed for BCS estimation in China. National BCS would increase from 130.90 to 159.94 Tg C year−1 during the period of 2010−2050 because of increases in forest area and biomass carbon density, with a maximum of 230.15 Tg C year−1 around 2030. BCS for young and middle-aged forests would increase by 65.35 and 15.38 Tg C year−1, respectively. 187.8% of this increase would be offset by premature, mature, and overmature forests. During the study period, forest BCS would increase in all but the northern region. The largest contributor to the increment would be the southern region (52.5%), followed by the southwest, northeast, northwest, and east regions. Their BCS would be primarily driven by the area expansion and forest growth of young and middle-aged forests as a result of afforestation and reforestation. In the northern region, BCS reduction would occur mainly in the Inner Mongolia province (6.38 Tg C year−1) and be caused predominantly by a slowdown in the increases of forest area and biomass carbon density for different age–class forests. Our findings are in broader agreement with other studies, which provide valuable references for the validation and parameterization of carbon models and climate-change mitigation policies in China.