Old-growth forest loss and secondary forest recovery across Amazonian countries

There is growing recognition of the potential of large-scale forest restoration in the Amazon as a 'nature-based solution' to climate change. However, our knowledge of forest loss and recovery beyond Brazil is limited, and carbon emissions and accumulation have not been estimated for the whole biome. Combining a 33 year land cover dataset with estimates of above-ground biomass and carbon sequestration rates, we evaluate forest loss and recovery across nine Amazonian countries and at a local scale. We also estimate the role of secondary forests in offsetting old-growth deforestation emissions and explore the temporal trends in forest loss and recovery. We find secondary forests across the biome to have offset just 9.7% of carbon emissions from old-growth deforestation, despite occupying 28.8% of deforested land. However, these numbers varied between countries ranging from 9.0% in Brazil to 23.8% in Guyana for carbon offsetting, and 24.8% in Brazil to 56.9% in Ecuador for forest area recovery. We reveal a strong, negative spatial relationship between old-growth forest loss and recovery by secondary forests, showing that regions with the greatest potential for large-scale restoration are also those that currently have the lowest recovery (e.g. Brazil dominates deforestation and emissions but has the lowest recovery). In addition, a temporal analysis of the regions that were >80% deforested in 1997 shows a continued decline in overall forest cover. Our findings identify three important challenges: (a) incentivising large-scale restoration in highly deforested regions, (b) protecting secondary forests without disadvantaging landowners who depend on farm-fallow systems, and (c) preventing further deforestation. Combatting all these successfully is essential to ensuring that the Amazon biome achieves its potential in mitigating anthropogenic climate change

Detecting and reducing heterogeneity of error in acoustic classification

Passive acoustic monitoring can be an effective method for monitoring species, allowing the assembly of large audio datasets, removing logistical constraints in data collection and reducing anthropogenic monitoring disturbances. However, the analysis of large acoustic datasets is challenging and fully automated machine learning processes are rarely developed or implemented in ecological field studies. One of the greatest uncertainties hindering the development of these methods is spatial generalisability—can an algorithm trained on data from one place be used elsewhere? We demonstrate that heterogeneity of error across space is a problem that could go undetected using common classification accuracy metrics. Second, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. Finally, we propose two complementary ways to reduce heterogeneity of error, by (i) accounting for it in the thresholding process and (ii) using a secondary classifier that uses contextual data. We found that using a thresholding approach that accounted for heterogeneity of precision error reduced the coefficient of variation of the precision score from a mean of 0.61 ± 0.17 (SD) to 0.41 ± 0.25 in comparison to the initial classification with threshold selection based on F-score. The use of a secondary, contextual classification with thresholding selection accounting for heterogeneity of precision reduced it further still, to 0.16 ± 0.13, and was significantly lower than the initial classification in all but one species. Mean average precision scores increased, from 0.66 ± 0.4 for the initial classification, to 0.95 ± 0.19, a significant improvement for all species. We recommend assessing—and if necessary correcting for—heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land‐use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less sensitive acoustic groups, or target taxa sonification is masked by non‐target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged‐and‐burned, and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0‐22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time‐frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape

Developing cost-effective field assessments of carbon stocks in human-modified tropical forests

Across the tropics, there is a growing financial investment in activities that aim to reduce emissions from deforestation and forest degradation, such as REDD+. However, most tropical countries lack on-the-ground capacity to conduct reliable and replicable assessments of forest carbon stocks, undermining their ability to secure long-term carbon finance for forest conservation programs. Clear guidance on how to reduce the monetary and time costs of field assessments of forest carbon can help tropical countries to overcome this capacity gap. Here we provide such guidance for cost-effective one-off field assessments of forest carbon stocks. We sampled a total of eight components from four different carbon pools (i.e. aboveground, dead wood, litter and soil) in 224 study plots distributed across two regions of eastern Amazon. For each component we estimated survey costs, contribution to total forest carbon stocks and sensitivity to disturbance. Sampling costs varied thirty-one-fold between the most expensive component, soil, and the least, leaf litter. Large live stems (≥10 cm DBH), which represented only 15% of the overall sampling costs, was by far the most important component to be assessed, as it stores the largest amount of carbon and is highly sensitive to disturbance. If large stems are not taxonomically identified, costs can be reduced by a further 51%, while incurring an error in aboveground carbon estimates of only 5% in primary forests, but 31% in secondary forests. For rapid assessments, necessary to help prioritize locations for carbon- conservation activities, sampling of stems ≥20cm DBH without taxonomic identification can predict with confidence (R2 = 0.85) whether an area is relatively carbon-rich or carbon-poor—an approach that is 74% cheaper than sampling and identifying all the stems ≥10cm DBH. We use these results to evaluate the reliability of forest carbon stock estimates provided by the IPCC and FAO when applied to human-modified forests, and to highlight areas where cost savings in carbon stock assessments could be most easily made

Optimizing tropical forest bird surveys using passive acoustic monitoring and high temporal resolution sampling

Estimation of avian biodiversity is a cornerstone measure of ecosystem condition. Surveys conducted using autonomous recorders are often more efficient at estimating diversity than traditional point-count surveys. However, there is limited research into the optimal temporal resolution for sampling—the trade-off between the number of samples and sample duration when sampling a survey window with a fixed survey effort—despite autonomous recorders allowing easy repeat sampling compared to traditional survey methods. We assess whether the additional temporal coverage from high temporal resolution (HTR) sampling, consisting of 240 15-s samples spread randomly across a survey window detects higher alpha and gamma diversity than low temporal resolution (LTR) sampling of four 15-min samples at the same locations. We do so using an acoustic dataset collected from 29 locations in a region of very high avian biodiversity—the eastern Brazilian Amazon. We find HTR sampling outperforms LTR sampling in every metric considered, with HTR sampling predicted to detect approximately 50% higher alpha diversity, and 10% higher gamma diversity. This effect is primarily driven by increased coverage of variation in detectability across the morning, with the earliest period containing a distinct community that is often under sampled using LTR sampling. LTR sampling produced almost four times as many false absences for species presence. Additionally, LTR sampling incorrectly found 70 species (34%) at only a single forest type when they were in fact present in multiple forest types, while the use of HTR sampling reduced this to just two species (0.9%). When considering multiple independent detections of species, HTR sampling detected three times more uncommon species than LTR sampling. We conclude that high temporal resolution sampling of passive-acoustic monitoring-based surveys should be considered the primary method for estimating the species richness of bird communities in tropical forests

Common Co-activation of AXL and CDCP1 in EGFR-mutation-positive Non-smallcell Lung Cancer Associated With Poor Prognosis.

Epidermal growth factor receptor (EGFR)-mutation-positive non-smallcell lung cancer (NSCLC) is incurable, despite high rates of response to EGFR tyrosine kinase inhibitors (TKIs). We investigated receptor tyrosine kinases (RTKs), Src family kinases and focal adhesion kinase (FAK) as genetic modifiers of innate resistance in EGFR-mutation-positive NSCLC. We performed gene expression analysis in two cohorts (Cohort 1 and Cohort 2) of EGFR-mutation-positive NSCLC patients treated with EGFR TKI. We evaluated the efficacy of gefitinib or osimertinib with the Src/FAK/Janus kinase 2 (JAK2) inhibitor, TPX0005 in vitro and in vivo. In Cohort 1, CUB domain-containing protein-1 (CDCP1) was an independent negative prognostic factor for progression-free survival (hazard ratio of 1.79, p=0.0407) and overall survival (hazard ratio of 2.23, p=0.0192). A two-gene model based on AXL and CDCP1 expression was strongly associated with the clinical outcome to EGFR TKIs, in both cohorts of patients. Our preclinical experiments revealed that several RTKs and non-RTKs, were up-regulated at baseline or after treatment with gefitinib or osimertinib. TPX-0005 plus EGFR TKI suppressed expression and activation of RTKs and downstream signaling intermediates. Co-expression of CDCP1 and AXL is often observed in EGFR-mutation-positive tumors, limiting the efficacy of EGFR TKIs. Co-treatment with EGFR TKI and TPX-0005 warrants testing

Biodiversity consequences of land-use change and forest disturbance in the Amazon:a multi-scale assessment using ant communities

Quantifying and understanding the main drivers of biodiversity responses to human disturbances at multiple scales is key to foster effective conservation plans and management systems. Here we report on a detailed regional assessment of the response of ant communities to land-use change and forest disturbance in the Brazilian Amazon. We aimed to explore the effects of land-use intensification at both site and landscape scales, examining variation in ant species richness and composition, and asking which set of environmental variables best predict observed patterns of diversity. We sampled 192 sites distributed across 18 landscapes (each 50 km2) in Paragominas, eastern Brazilian Amazon, covering ca. 20,000 km2. We sampled from undisturbed primary forest through varyingly disturbed primary forests, secondary forests, pastures and mechanised agriculture, following a gradient of decreasing total aboveground biomass. Irrespective of forest disturbance class, ant species richness was almost twice as high in forests when compared to production areas. In contrast, ant species composition showed continuous variation from primary forest to intensive agriculture, following a gradient of aboveground biomass. Ant species richness at all spatial scales increased with primary forest cover in the surrounding landscapes. We highlight the limited value of species richness as an indicator of changes in habitat quality, reinforcing calls to consider species composition in assessments of forest disturbance. Taken together, our results reveal the unique biodiversity value of undisturbed primary forests, but also show that disturbed primary forests and secondary forests have high conservation value, and thus play an important role in regional conservation planning

Rainforest-to-pasture conversion stimulates soil methanogenesis across the Brazilian Amazon

The Amazon rainforest is a biodiversity hotspot and large terrestrial carbon sink threatened by agricultural conversion. Rainforest-to-pasture conversion stimulates the release of methane, a potent greenhouse gas. The biotic methane cycle is driven by microorganisms; therefore, this study focused on active methane-cycling microorganisms and their functions across land-use types. We collected intact soil cores from three land use types (primary rainforest, pasture, and secondary rainforest) of two geographically distinct areas of the Brazilian Amazon (Santarém, Pará and Ariquemes, Rondônia) and performed DNA stable-isotope probing coupled with metagenomics to identify the active methanotrophs and methanogens. At both locations, we observed a significant change in the composition of the isotope-labeled methane-cycling microbial community across land use types, specifically an increase in the abundance and diversity of active methanogens in pastures. We conclude that a significant increase in the abundance and activity of methanogens in pasture soils could drive increased soil methane emissions. Furthermore, we found that secondary rainforests had decreased methanogenic activity similar to primary rainforests, and thus a potential to recover as methane sinks, making it conceivable for forest restoration to offset greenhouse gas emissions in the tropics. These findings are critical for informing land management practices and global tropical rainforest conservation

Listening to tropical forest soils

Acoustic monitoring has proven to be an effective tool for monitoring biotic soundscapes in the marine, terrestrial, and aquatic realms. Recently it has been suggested that it could also be an effective method for monitoring soil soundscapes, but has been used in very few studies, primarily in temperate and polar regions. We present the first study of soil soundscapes using passive acoustic monitoring in tropical forests, using a novel analytical pipeline allowing for the use of in-situ recording of soundscapes with minimal soil disturbance. We found significant differences in acoustic index values between burnt and unburnt forests and the first indications of a diel cycle in soil soundscapes. These promising results and methodological advances highlight the potential of passive acoustic monitoring for large-scale and long-term monitoring of soil biodiversity. We use the results to discuss research priorities, including relating soil biophony to community structure and ecosystem function, and the use of appropriate hardware and analytical techniques

Tree growth and stem carbon accumulation in human-modified Amazonian forests following drought and fire

Human-modified forests are an ever-increasing feature across the Amazon Basin, but little is known about how stem growth is influenced by extreme climatic events and the resulting wildfires. Here we assess for the first time the impacts of human-driven disturbance in combination with El Niño–mediated droughts and fires on tree growth and carbon accumulation. We found that after 2.5 years of continuous measurements, there was no difference in stem carbon accumulation between undisturbed and human-modified forests. Furthermore, the extreme drought caused by the El Niño did not affect carbon accumulation rates in surviving trees. In recently burned forests, trees grew significantly more than in unburned ones, regardless of their history of previous human disturbance. Wood density was the only significant factor that helped explain the difference in growth between trees in burned and unburned forests, with low wood–density trees growing significantly more in burned sites. Our results suggest stem carbon accumulation is resistant to human disturbance and one-off extreme drought events, and it is stimulated immediately after wildfires. However, these results should be seen with caution—without accounting for carbon losses, recruitment and longer-term changes in species composition, we cannot fully understand the impacts of drought and fire in the carbon balance of human-modified forests. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Nino on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’