Effects of landscape configuration and composition on phylogenetic diversity of trees in a highly fragmented tropical forest

© 2016 British Ecological Society. Fragmentation of tropical forests is a major driver of the global extinction crisis. A key question is understanding how fragmentation impacts phylogenetic diversity, which summarizes the total evolutionary history shared across species within a community. Conserving phylogenetic diversity decreases the potential of losing unique ecological and phenotypic traits and plays important roles in maintaining ecosystem function and stability. Our study was conducted in landscapes within the highly fragmented Brazilian Atlantic forest. We sampled living trees with d.b.h. ≥ 4.8 cm in 0.1 ha plots within 28 fragment interiors and 12 fragment edges to evaluate the impacts of landscape configuration, composition and patch size, as well as edge effects, on phylogenetic diversity indices (PD, a measure of phylogenetic richness; MPD, phylogenetic distance between individuals in a community in deep evolutionary time; and MNTD, phylogenetic distance between each individual and its nearest phylogenetic neighbour). We found that PD and MPD were correlated with species richness, while MNTD was not. Best models suggest that MPD was positively related to edge density and negatively related to the number of forest patches, but that there was no effect of landscape configuration and composition metrics on PD or MNTD, or on standardized values of phylogenetic structure (sesPD, sesMPD and sesMNTD), which control for species richness. Considering all selected models for phylogenetic diversity and structure, edge density and number of forest patches were most frequently selected. With increasing patch size, we found lower PD in interiors but no change at edges and lower sesMNTD regardless of habitat type. Additionally, PD and sesMNTD were higher in interiors than at edges. Synthesis. Changes in MPD and sesMNTD suggest that extirpation of species at edges or in highly fragmented landscapes increases the dominance of species within a subset of clades (phylogenetic clustering), likely those adapted to disturbance. Smaller patch sizes are phylogenetically diverse and overdispersed, probably due to an invasion of edge-adapted species. Conservation must enhance patch area and connectivity via forest restoration; pivotally, even small forest patches are important reservoirs of phylogenetic diversity in the highly threatened Brazilian Atlantic forest

Combining Sentinel-1 and Landsat 8 does not improve classification accuracy of tropical selective logging

Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data

A machine learning approach to map tropical selective logging

Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m3 ha-1). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (< 15 m3 ha-1). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes

Precipitation gradients drive high tree species turnover in the woodlands of eastern and southern Africa

Savannas cover one-fifth of the Earth's surface, harbour substantial biodiversity, and provide a broad range of ecosystem services to hundreds of millions of people. The community composition of trees in tropical moist forests varies with climate, but whether the same processes structure communities in disturbance-driven savannas remains relatively unknown. We investigate how biodiversity is structured over large environmental and disturbance gradients in woodlands of eastern and southern Africa. We use tree inventory data from the Socio-Ecological Observatory for Studying African Woodlands (SEOSAW) network, covering 755 ha in a total of 6780 plots across nine countries of eastern and southern Africa, to investigate how alpha, beta, and phylogenetic diversity varies across environmental and disturbance gradients. We find strong climate-richness patterns, with precipitation playing a primary role in determining patterns of tree richness and high turnover across these savannas. Savannas with greater rainfall contain more tree species, suggesting that low water availability places distributional limits on species, creating the observed climate-richness patterns. Both fire and herbivory have minimal effects on tree diversity, despite their role in determining savanna distribution and structure. High turnover of tree species, genera, and families is similar to turnover in seasonally dry tropical forests of the Americas, suggesting this is a feature of semiarid tree floras. The greater richness and phylogenetic diversity of wetter plots shows that broad-scale ecological patterns apply to disturbance-driven savanna systems. High taxonomic turnover suggests that savannas from across the regional rainfall gradient should be protected if we are to maximise the conservation of unique tree communities

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost