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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.

Human activities pose a major threat to tropical forest biodiversity and ecosystem services. Although the impacts of deforestation are well studied, multiple land-use and land-cover transitions (LULCTs) occur in tropical landscapes, and we do not know how LULCTs differ in their rates or impacts on key ecosystem components. Here, we quantified the impacts of 18 LULCTs on three ecosystem components (biodiversity, carbon, and soil), based on 18 variables collected from 310 sites in the Brazilian Amazon. Across all LULCTs, biodiversity was the most affected ecosystem component, followed by carbon stocks, but the magnitude of change differed widely among LULCTs and individual variables. Forest clearance for pasture was the most prevalent and high-impact transition, but we also identified other LULCTs with high impact but lower prevalence (e.g., forest to agriculture). Our study demonstrates the importance of considering multiple ecosystem components and LULCTs to understand the consequences of human activities in tropical landscapes.

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

Silva et al. (Land Use Policy, 21 July 2020) offer an assessment of the links between deforestation, livestock production and exports in Brazil. Their analysis, based on relative changes in beef production and pasture area across the whole of Brazil, showed an “apparent decoupling of the link between beef production and deforestation in Brazil”. In reanalysing these links, we find that Silva et al. underestimate the strong, positive and significant associations between Brazilian livestock production and deforestation. Moreover, despite focusing the title, abstract and the beginning of their manuscript on the Amazon, their analyses are conducted at the national level, and fail to recognise marked differences in the development trajectories of Brazilian biomes, and that most of the recent pasture expansion in Brazil has replaced Amazonian forests. To progress any debate and aid decision-making regarding land-use changes in the Amazon, a region often in the spotlight and subjected to many debates that lack evidence, scientists must be open and scrupulous with their data sources and analyses. © 2020

Many countries allow lawsuits to hold responsible parties liable for the environmental harm they cause. Such litigation remains largely untested in most biodiversity hotspots and is rarely used in response to leading drivers of biodiversity loss, including illegal wildlife trade. Yet, liability litigation is a potentially ground-breaking conservation strategy to remedy harm to biodiversity by seeking legal remedies such as species rehabilitation, public apologies, habitat conservation and education, with the goal of making the injured parties ‘whole’. However, precedent cases, expert guidance, and experience to build such conservation lawsuits is nascent in most countries. We propose a simplified framework for developing conservation lawsuits across countries and conservation contexts. We explain liability litigation in terms of three dimensions: (1) defining the harm that occurred, (2) identifying appropriate remedies to that harm, and (3) understanding what remedies the law and courts will allow. We illustrate the framework via a hypothetical lawsuit against an illegal orangutan trader in Indonesia. We highlight that conservationists’ expertise is essential to characterizing harm and identifying remedies, and could more actively contribute to strategic, science-based litigation. This would identify priority contexts, target defendants responsible for egregious harm, propose novel and meaningful remedies, and build new transdisciplinary collaborations. © 2021 The Authors. Conservation Letters published by Wiley Periodicals LLC

Conservationists often assume that connection with and caring about nature's well-being is strongly linked to ecological knowledge. Existing evidence on the link between ecological knowledge and psychological nature connection is mixed, geographically limited to countries in the Global North, and does not scrutinise potential differences in determinants of ecological knowledge and nature connection. We investigate the relationship between psychological nature connection and ecological knowledge of local bird species, and assess their associations with potential drivers, including access to, contact with, and reliance on nature and socio-demographic characteristics. Our study is carried among a novel participant population of colonist farmers living along a major deforestation frontier in the Brazilian Amazon. Our study context has high conservation relevance and provides an ideal setting to assess the extent to which conservation psychology's insights from the Global North hold true elsewhere. Tropical farm-forest frontiers suffer from intense habitat and biodiversity loss, and farmers with migrant origins are important yet rarely studied conservation stakeholders. Importantly, farmers' experiences of nature are likely to vary considerably due to the wide range of socio-demographic, economic, geographical and cultural diversity. Interviewees scored highly on two indices of nature connection, but scores were higher among older people and those with greater contact with nature. Bird identification knowledge was generally low to moderate, and higher among men and younger people. Species more frequently recognised were regionally common, larger-bodied or associated with non-forest habitats. Ecological knowledge of birds and nature connection were not correlated, and they did not have any predictors in common. Our results indicate that colonist farmers are capable of forming strong connections with nature, even if they rarely possess detailed knowledge of local forest bi

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. © 2020 British Ecological Society

Silva et al. (Land Use Policy, 21 July 2020) offer an assessment of the links between deforestation, livestock production and exports in Brazil. Their analysis, based on relative changes in beef production and pasture area across the whole of Brazil, showed an “apparent decoupling of the link between beef production and deforestation in Brazil”. In reanalysing these links, we find that Silva et al. underestimate the strong, positive and significant associations between Brazilian livestock production and deforestation. Moreover, despite focusing the title, abstract and the beginning of their manuscript on the Amazon, their analyses are conducted at the national level, and fail to recognise marked differences in the development trajectories of Brazilian biomes, and that most of the recent pasture expansion in Brazil has replaced Amazonian forests. To progress any debate and aid decision-making regarding land-use changes in the Amazon, a region often in the spotlight and subjected to many debates that lack evidence, scientists must be open and scrupulous with their data sources and analyses. © 2020

Aichi Target 12 of the Convention on Biological Diversity (CBD) contains the aim to ‘prevent extinctions of known threatened species’. To measure the degree to which this was achieved, we used expert elicitation to estimate the number of bird and mammal species whose extinctions were prevented by conservation action in 1993–2020 (the lifetime of the CBD) and 2010–2020 (the timing of Aichi Target 12). We found that conservation action prevented 21–32 bird and 7–16 mammal extinctions since 1993, and 9–18 bird and two to seven mammal extinctions since 2010. Many remain highly threatened and may still become extinct. Considering that 10 bird and five mammal species did go extinct (or are strongly suspected to) since 1993, extinction rates would have been 2.9–4.2 times greater without conservation action. While policy commitments have fostered significant conservation achievements, future biodiversity action needs to be scaled up to avert additional extinctions.

Quantifying the impact of habitat disturbance on ecosystem function is critical to understanding and predicting the future of tropical forests. Many studies have examined post‐disturbance changes in animal traits related to mutualistic interactions with plants, but the effect of disturbance on plant traits in diverse forests has received much less attention. Focusing on two study regions in the eastern Brazilian Amazon, we used a trait‐based approach to examine how seed dispersal functionality within tropical plant communities changes across a landscape‐scale gradient of human modification, including both regenerating secondary forests and primary forests disturbed by burning and selective logging. Surveys of 230 forest plots recorded 26,533 live stems from 846 tree species. Using herbarium material and literature, we compiled trait information for each tree species, focusing on dispersal mode and seed size. Disturbance reduced tree diversity and increased the proportion of lower wood density and small‐seeded tree species in study plots. Disturbance also increased the proportion of stems with seeds that are ingested by animals and reduced those dispersed by other mechanisms (e.g. wind). Older secondary forests had functionally similar plant communities to the most heavily disturbed primary forests. Mean seed size and wood density per plot were positively correlated for plant species with seeds ingested by animals. Synthesis. Anthropogenic disturbance has major effects on the seed traits of tree communities, with implications for mutualistic interactions with animals. The important role of animal‐mediated seed dispersal in disturbed and recovering forests highlights the need to avoid defaunation or promote faunal recovery. The changes in mean seed width suggest larger vertebrates hold especially important functional roles in these human‐modified forests. Monitoring fruit and seed traits can provide a valuable indicator of ecosystem condition, emphasizing the importan

Tropical forests and coral reefs host a disproportionately large share of global biodiversity and provide ecosystem functions and services used by millions of people. Yet, ongoing climate change is leading to an increase in frequency and magnitude of extreme climatic events in the tropics, which, in combination with other local human disturbances, is leading to unprecedented negative ecological consequences for tropical forests and coral reefs. Here, we provide an overview of how and where climate extremes are affecting the most biodiverse ecosystems on Earth and summarize how interactions between global, regional and local stressors are affecting tropical forest and coral reef systems through impacts on biodiversity and ecosystem resilience. We also discuss some key challenges and opportunities to promote mitigation and adaptation to a changing climate at local and global scales. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data.

Efforts by conservation scientists to draw public attention to the biodiversity crisis are increasingly met with denialist rhetoric. We summarize some of the methods used by denialists to undermine scientific evidence on biodiversity loss, and outline pathways forward for the scientific community to counter misinformation.

Conservation initiatives overwhelmingly focus on terrestrial biodiversity, and little is known about the freshwater cobenefits of terrestrial conservation actions. We sampled more than 1500 terrestrial and freshwater species in the Amazon and simulated conservation for species from both realms. Prioritizations based on terrestrial species yielded on average just 22% of the freshwater benefits achieved through freshwater-focused conservation. However, by using integrated cross-realm planning, freshwater benefits could be increased by up to 600% for a 1% reduction in terrestrial benefits. Where freshwater biodiversity data are unavailable but aquatic connectivity is accounted for, freshwater benefits could still be doubled for negligible losses of terrestrial coverage. Conservation actions are urgently needed to improve the status of freshwater species globally. Our results suggest that such gains can be achieved without compromising terrestrial conservation goals.

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data.