Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño

Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests

Habitat fragmentation could potentially affect tree architecture and allometry. Here, we use ground surveys of terrestrial LiDAR in Central Amazonia to explore the influence of forest edge effects on tree architecture and allometry, as well as forest biomass, 40 years after fragmentation. We find that young trees colonising the forest fragments have thicker branches and architectural traits that optimise for light capture, which result in 50% more woody volume than their counterparts of similar stem size and height in the forest interior. However, we observe a disproportionately lower height in some large trees, leading to a 30% decline in their woody volume. Despite the substantial wood production of colonising trees, the lower height of some large trees has resulted in a net loss of 6.0 Mg ha-1 of aboveground biomass - representing 2.3% of the aboveground biomass of edge forests. Our findings indicate a strong influence of edge effects on tree architecture and allometry, and uncover an overlooked factor that likely exacerbates carbon losses in fragmented forests.

Increased impact of the El Niño-Southern Oscillation on global vegetation under future warming environment

There are broad effects of vegetation changes on regional climate, carbon budget, the water cycle, and ecosystems' productivity. Therefore, further knowledge of the drivers of future vegetation changes is critical to mitigate the influences of global warming. The El Niño-Southern Oscillation (ENSO) is a major mode of interannual climate variability and is likely to affect vegetation on the global scale. Nonetheless, little is known about the causal impacts of ENSO on future vegetation cover with changes in land use and a warming environment. Here, we examined the connections between ENSO and vegetation using leaf area index (LAI) data over the period 2015-2100 from Coupled Modeling Intercomparison Project Phase 6. Our findings indicate that, compared with the historical period 1915-2000, the vegetated areas influenced by ENSO are projected to rise by approximately 55.2% and 20.7% during the twenty-first century of the scenarios SSP2-4.5 and SSP5-8.5, respectively. Though uncertainty for the causal link between ENSO and vegetation changes remains in several regions (i.e., parts of North America, southern Australia, and western Asia), ENSO signature on LAI variations is robust over northern Australia, Amazonia, and parts of Southeast Asia. These results indicate that the influences of ENSO on global vegetation may strengthen in the future.

Deforestation for oil palm increases microclimate suitability for the development of the disease vector Aedes albopictus

A major trade-off of land-use change is the potential for increased risk of infectious diseases, a.o. through impacting disease vector life-cycles. Evaluating the public health implications of land-use conversions requires spatially detailed modelling linking land-use to vector ecology. Here, we estimate the impact of deforestation for oil palm cultivation on the number of life-cycle completions of Aedes albopictus via its impact on local microclimates. We apply a recently developed mechanistic phenology model to a fine-scaled (50-m resolution) microclimate dataset that includes daily temperature, rainfall and evaporation. Results of this combined model indicate that the conversion from lowland rainforest to plantations increases suitability for A. albopictus development by 10.8%, moderated to 4.7% with oil palm growth to maturity. Deforestation followed by typical plantation planting-maturation-clearance-replanting cycles is predicted to create pulses of high development suitability. Our results highlight the need to explore sustainable land-use scenarios that resolve conflicts between agricultural and human health objectives.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Even evergreen tropical forests can have seasonal dynamics, which may be sensitive to disturbance. Here, the authors combine high-resolution remote sensing observations and microclimate data to show that forest fragmentation impacts canopy phenology dynamics in the Amazon forest.

A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.

Deciphering the fingerprint of disturbance on the three-dimensional structure of the world's forests

Canopy gaps and the processes that generate them play an integral role in shaping the structure and dynamics of forests. However, it is only with recent advances in remote sensing technologies such as airborne laser scanning that studying canopy gaps at scale has become a reality. Consequently, we still lack an understanding of how the size distribution and spatial organization of canopy gaps varies among forests ecosystems, nor have we determined whether these emergent properties can be reconciled with existing theories of forest dynamics. Here, I outline a roadmap for integrating remote sensing with field data and individual-based models to build a comprehensive picture of how environmental constraints and disturbance regimes shape the three-dimensional structure of the world's forests.

Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

(1) Background: Forests throughout the world are managed to fulfil a range of commercial and ecosystem services. The same applies to managed areas of the Amazon forest. We explore a method of sustainable forest management (SFM) which anticipates the result of processes of natural mortality of large, mature trees that could fall and damage their neighbors. Collecting all the information required for planning logging in the Brazilian Amazon is, currently, a hard, time-consuming and expensive task. (2) Methods: This information can be obtained more quickly, accurately and objectively by including airborne laser scanning (ALS) products in the operational plan. We used ALS point clouds to isolate emergent crowns from the canopy height model. Then, we performed field work to validate the existence of these trees, and to understand how many commercial trees (tree diameter ≥ 50 cm) we identified by orienting the trees search through the emergent canopy model. (3) Results: We were able to detect 184 (54.4%) trees from 338 field-recorded individuals in 20 plots (totaling 8 ha). Of the detected trees, 66 individuals were classified as having potential for commerce. Furthermore, 58 individuals presented the best stem quality for logging, which represents more than seven high quality commercial trees per hectare. The logistic regression showed that the effects that positively influence the emergent crown formation are strongly presented in the commercial species. (4) Conclusions: Using airborne laser scanning can improve the SFM planning in a structurally complex, dense and mixed composition tropical forest by reducing field work in the initial stages of management. Therefore, we propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census.

Upscaling tropical restoration to deliver environmental benefits and socially equitable outcomes

The UN Decade on Ecosystem Restoration offers immense potential to return hundreds of millions of hectares of degraded tropical landscapes to functioning ecosystems. Well-designed restoration can tackle multiple Sustainable Development Goals, driving synergistic benefits for biodiversity, ecosystem services, agricultural and timber production, and local livelihoods at large spatial scales. To deliver on this potential, restoration efforts must recognise and reduce trade-offs among objectives, and minimize competition with food production and conservation of native ecosystems. Restoration initiatives also need to confront core environmental challenges of climate change and inappropriate planting in savanna biomes, be robustly funded over the long term, and address issues of poor governance, inadequate land tenure, and socio-cultural disparities in benefits and costs. Tackling these issues using the landscape approach is vital to realising the potential for restoration to break the cycle of land degradation and poverty, and deliver on its core environmental and social promises.