To model the distribution of ectomycorrhizal (ECM) fungi to (i) analyse climate change impacts on their future distribution areas and centroids and (ii) analyse their distribution changes by ECM fungal host specificity. Location: Europe. Time period: 2041-2100. Major taxa studied: Ectomycorrhizal fungi. We modelled the distributions of 60 common ECM fungal species in European forests and projected their future distributions under three different shared socioeconomic pathways (SSP126, SSP370 and SSP585) for 2041-2070 and 2071-2100. Both abiotic and biotic (host tree distribution) variables were included in the modelling, with ECM fungal species classified into broadleaf specialists (19), conifer specialists (22) and generalists (19). We estimated changes in both the areas and geographic centroids between the projected future and current distributions for each species and for each ECM fungal host specificity group. We found that host tree distributions make strong contributions to ECM fungal distribution models, but their influence varied with ECM fungal host specificity. The distributions of most ECM fungal species are projected to decline (ranging from 0.2% to 64%) and shift northward under the three climate scenarios in both 2041-2070 and 2071-2100, and most ECM fungal conifer specialists are projected to lose more of their current distribution compared to broadleaf specialists and generalists. Substantial decline of studied ECM fungal co-occurrence is projected in southern England, central Europe, Finland and Sweden. Our results evidence ECM fungi will be mostly negatively affected by climate change, but this will vary with host specificity. Thus, conservation actions need tailored actions for the different groups. Conifer specialists need special attention, either through targeted monitoring or by assessing their conservation status. Overall, a conservation plan for fungi is needed under climate change scenarios.

Modeling complex, nonlinear ecosystem processes across different timescales presents a significant challenge. We identify two key issues: selecting a representative timestep that captures interconnected processes across various timescales, and simulating these processes in an appropriate sequence. By synthesizing existing ecosystem frameworks, we find shared compromises between biological realism and computational performance. For the representative timestep, these include 'selective elimination of timescales', 'biting the bullet', 'each in their own time', and 'capture the unseen'. For processing order, we identify hierarchical, logical, iterative, and random approaches. Similar challenges exist in other disciplines, and we show how transferring methods from multiple fields, along with smarter computing, can improve timescale integration. Overcoming these challenges requires innovative transdisciplinary solutions, and we outline directions for future research.

African Swine Fever (ASF) reached the island of Borneo at the end of 2020. The first mortalities were recorded in wild bearded pigs (Sus barbatus) in Sabah, north-east Borneo. The virus then began to spread across the island but due to COVID-19 lockdowns the spread was difficult to monitor on the ground. With the urgent need to track this epidemic, and in the absence of traditional monitoring, the Babi Hutan Project was launched in April 2021 to gather data on pig sightings using citizen science. Any sightings of bearded pigs were requested via the website, social media and a WhatsApp hotline. Here we bring together the data from this project and other online sources to show how the virus spread across almost the entire island within a one-year period. The speed of spread appeared to increase with time following an exponential model: we estimate an average speed of spread of 0.89 km/day after 100 days since the first observation and at 4.28 km/day after 400 days. Our key recommendations are: that existing hunting bans on bearded pigs remain in place; that urgent biosecurity measures should be put in place if outbreaks occur in areas with backyard (domestic) pigs; that surviving pigs are tested for resistance; that the disease dynamics are modelled and that the International Union for Conservation of Nature (IUCN) urgently re-evaluates the bearded pig’s status.

The functional stability of ecosystems depends greatly on interspecific differences in responses to environmental perturbation. However, responses to perturbation are not necessarily invariant among populations of the same species, so intraspecific variation in responses might also contribute. Such inter-population response diversity has recently been shown to occur spatially across species ranges, but we lack estimates of the extent to which individual populations across an entire community might have perturbation responses that vary through time. We assess this using 524 taxa that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in Sabah, Malaysia. Just 39 % of taxa – all with non-significant responses to forest degradation – had invariant responses. All other taxa (61 %) showed significantly different responses to the same forest degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of logging disturbance increased the prevalence of intra-population response diversity, while uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated variable responses in at least 44 % of taxa. Our results show that the responses of individual species to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to the stability of disturbed habitats such as logged tropical forests.### Competing Interest StatementThe authors have declared no competing interest.

AimAnthropogenic climate change is predicted to drive unprecedented increases in the frequency and intensity of extreme climatic events, such as drought and cyclones. The impacts of these events on fully migratory species could be particularly severe and have cascading effects on the functioning of many ecosystems. We explore the relationships between geography, taxonomy, extinction risk and the exposure of fully migratory birds to drought and cyclones.LocationGlobal.Time Period1985-2014.Major Taxa Studied383 fully migratory bird species.MethodsWe assessed the exposure of fully migratory birds to cyclones and droughts, quantifying exposure by calculating the percentage of spatial overlap between a species' range and the extent of an extreme event within a given time series. We compared the level of cumulative exposure sustained by species among different taxonomic groups and within their breeding and wintering ranges; we also assessed whether species currently classed as 'threatened' are more cumulatively exposed than 'non-threatened' species.ResultsWe identified fully migratory bird species highly exposed to extreme climatic events and global geographic hotspots of species exposure. 4% of species were found to be highly exposed to cyclones and droughts in both their wintering and breeding ranges. Wintering ranges were, on average, more cumulatively exposed to cyclones than breeding ranges; there was no discernible difference in drought exposure between ranges. Species currently classed as threatened were shown to experience higher exposure to droughts than non-threatened ones in both ranges.Main ConclusionsThis exposure analysis provides the first step to a full global assessment of fully migratory bird species' vulnerability to extreme climatic events. Many species are at least as exposed to extreme events within their wintering ranges as in their breeding ranges, supporting calls for 'full cycle' assessment of migratory species' vulnerability to climate change. Our identification of hotspots of exposure may help to guide further monitoring, research and management.

Aim: Ecoregions and the distance decay in community similarity are fundamental concepts in biogeography and conservation biology that are well supported across plants and animals, but not fungi. Here we test the relevance of these concepts for ectomycorrhizal (ECM) fungi in temperate and boreal regions. Location: Europe. Time Period: 2008-2015. Major Taxa Studied: Ectomycorrhizal fungi. Methods: We used a large dataset of similar to 24,000 ectomycorrhizas, assigned to 1350 operational taxonomic units, collected from 129 forest plots via a standardized protocol. We investigated the relevance of ecoregion delimitations for ECM fungi through complementary methodological approaches based on distance decay models, multivariate analyses and indicator species analyses. We then evaluated the effects of host tree and climate on the observed biogeographical distributions. Results: Ecoregions predict large-scale ECM fungal biodiversity patterns. This is partly explained by climate differences between ecoregions but independent from host tree distribution. Basidiomycetes in the orders Russulales and Atheliales and producing epigeous fruiting bodies, with potentially short-distance dispersal, show the best agreement with ecoregion boundaries. Host tree distribution and fungal abundance (as opposed to presence/absence only) are important to uncover biogeographical patterns in mycorrhizas. Main Conclusions: Ecoregions are useful units to investigate eco-evolutionary processes in mycorrhizal fungal communities and for conservation decision-making that includes fungi.

Abstract Multi‐microphone recording adds spatial information to recorded audio with emerging applications in ecosystem monitoring. Specifically placing sounds in space can improve animal count accuracy, locate illegal activity like logging and poaching, track animals to monitor behaviour and habitat use and allow for ‘beamforming’ to amplify sounds from target directions for downstream classification. Studies have shown many advantages of spatial acoustics, but uptake remains limited as the equipment is often expensive, complicated, inaccessible or only suitable for short‐term deployments. With an emphasis on enhanced uptake and usability, we present a low‐cost, open‐source, six‐channel recorder built entirely from commercially available components which can be integrated into a solar‐powered, online system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works as an independent node in long‐term autonomous, passive and/or short‐term deployments. Here, we introduce MAARU's hardware and software and present the results of lab and field tests investigating the device's durability and usability. MAARU records multichannel audio with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the United Kingdom and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6 kHz and bird calls as far as 8 m away (±10° range, 100% and >60% of signals, respectively). Louder calls may have even further detection radii. We also show how beamforming can be used with MAARUs to improve species ID confidence scores. MAARU is an accessible, low‐cost option for those looking to explore spatial acoustics accurately and easily with a single device, and without the formidable expenses and processing complications associated with establishing arrays. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for enhanced recording of sonic environments, further enabling a potential step change in the uptake of spatial acoustics in the wider field.

AimWe used two fungal data sources for occurrence records (fruitbodies and roots) to (1) test the influence of data source on estimating the environmental niche of ectomycorrhizal (ECM) fungi and (2) compare the differences in estimated niche area and density for ECM fungal species with conspicuous (easily observed, i.e. mushrooms) versus inconspicuous (difficult to observe and/or usually overlooked, i.e. crusts and truffles) fruitbodies.LocationEurope.TaxonSixty-six ectomycorrhizal fungi.MethodsWe used fungal records obtained from fruitbody and root data of 66 common ECM fungal species in European forests to estimate their environmental niches. The fruitbody data were extracted from public databases (GBIF, UNITE), while the root data (from individual ectomycorrhizas) were obtained from a dataset of 136 ICP Forests long-term intensive monitoring plots. We estimated the niches for combined data sources (fruitbody and root data) and for each individual data source using six key environmental variables for ECM fungal community composition. We then examined how estimated niche overlap and area (number of cells in niche grid) varied for the two data sources between conspicuous and inconspicuous species.ResultsWe found that although the niches estimated using combined data from the two data sources had high overlap with the niches estimated from fruitbody data, the niches estimated from fruitbody data had low or medium overlap with the niches estimated using root data for most ECM fungi. The overlap between the two data sources for conspicuous species was significantly larger than that for inconspicuous species. Root data were important for estimating the niche of inconspicuous species, which had a high ratio of root data to fruitbody data.Main ConclusionOur results indicate that although fruitbody data suffice for estimating the environmental niche for most conspicuous ECM fungi, combined datasets including fruitbody and root data can improve the accuracy of estimated niches and should be used. Root data for inconspicuous species are particularly useful, and thus, adopting root data in niche estimation will better infer the niches of ECM fungi. Inferring niches along environmental variables can guide future sampling and conservation of fungi.

Logged and disturbed forests are often viewed as degraded and depauperate environments compared with primary forest. However, they are dynamic ecosystems(1) that provide refugia for large amounts of biodiversity2,3, so we cannot afford to underestimate their conservation value4. Here we present empirically defined thresholds for categorizing the conservation value of logged forests, using one of the most comprehensive assessments of taxon responses to habitat degradation in any tropical forest environment. We analysed the impact of logging intensity on the individual occurrence patterns of 1,681 taxa belonging to 86 taxonomic orders and 126 functional groups in Sabah, Malaysia. Our results demonstrate the existence of two conservation-relevant thresholds. First, lightly logged forests (<29% biomass removal) retain high conservation value and a largely intact functional composition, and are therefore likely to recover their pre-logging values if allowed to undergo natural regeneration. Second, the most extreme impacts occur in heavily degraded forests with more than two-thirds (>68%) of their biomass removed, and these are likely to require more expensive measures to recover their biodiversity value. Overall, our data confirm that primary forests are irreplaceable5, but they also reinforce the message that logged forests retain considerable conservation value that should not be overlooked.

The myriad interactions among individual plants, animals, microbes and their abiotic environment generate emergent phenomena that will determine the future of life on Earth. Here, we argue that holistic ecosystem models – incorporating key biological domains and feedbacks between biotic and abiotic processes – capable of predicting emergent phenomena are required if we are to understand the functioning of complex, terrestrial ecosystems in a rapidly changing planet. We argue that holistic ecosystem models will provide a framework for integrating the many approaches used to study ecosystems, including biodiversity science, population and community ecology, soil science, biogeochemistry, hydrology and climatology. Holistic models will provide new insights into the nature and importance of feedbacks that cut across scales of space and time, and that connect ecosystem domains such as microbes and animals or above and below ground. They will allow us to critically examine the origins and maintenance of ecosystem stability, resilience and sustainability through the lens of systems theory, and provide a much-needed boost for conservation and the management of natural environments. We outline our approach to developing a holistic ecosystem model – the Virtual Ecosystem – and argue that while the construction of such complex models is obviously ambitious, it is both feasible and necessary.

1 – Acoustic localisation, which relies on simultaneous multi-microphone recording, adds spatial information to recorded audio and has been used in ecosystem monitoring to count individuals to improve abundance estimates, locate illegal activities such as logging/poaching, and monitor behaviour such as habitat use or species interactions. Studies have shown many advantages of acoustic localisation, but uptake remains limited as the equipment is often expensive, inaccessible, or only suitable for short-term deployments. 2 –Here, we present a low-cost, open-source, 6-channel recorder built entirely from commercially available components which can be integrated into a solar-powered, networked system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works in long-term autonomous, passive, and ad-hoc deployments. We introduce MAARU’s hardware and software and present the results of lab and field tests investigating the device’s durability, localisation accuracy, and other applications. 3 –MAARU provides multichannel data with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the UK and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6kHz and 65dB bird calls as far as 8m away (±10° range, 100% and >60% of signals respectively), louder calls may have even further detection radii. We also show how beamforming can be used on MAARU devices to improve species ID confidence scores by 20%+ and recall by 10%+ when using the BirdNET automated bird identification algorithms. 4 –MAARU is an accessible, low-cost option for those looking to explore spatial soundscape ecology accurately and easily. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for a new type of exploration into sonic environments. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

There has been limited characterisation of bat-borne coronaviruses in Europe. Here, we screened for coronaviruses in 48 faecal samples from 16 of the 17 bat species breeding in the UK, collected through a bat rehabilitation and conservationist network. We recovered nine (two novel) complete genomes across six bat species: four alphacoronaviruses, a MERS-related betacoronavirus, and four closely related sarbecoviruses. We demonstrate that at least one of these sarbecoviruses can bind and use the human ACE2 receptor for infecting human cells, albeit suboptimally. Additionally, the spike proteins of these sarbecoviruses possess an R-A-K-Q motif, which lies only one nucleotide mutation away from a furin cleavage site (FCS) that enhances infectivity in other coronaviruses, including SARS-CoV-2. However, mutating this motif to an FCS does not enable spike cleavage. Overall, while UK sarbecoviruses would require further molecular adaptations to infect humans, their zoonotic risk is unknown and warrants closer surveillance.

The relative effects of habitat loss and fragmentation on biodiversity have been a topic of discussion for decades. While it is acknowledged that habitat amount can mediate the effects of habitat fragmentation, it is unclear what other factors may drive inter-and intraspecific variation in fragmentation effects and their implications for conservation. We tested whether the effects of forest fragmentation on 362 bird species' occurrence in the Atlantic Forest of Brazil are mediated by distance to geographic range edge and habitat amount, and whether these effects explain intraspecific variation across populations. Using a single binomial linear mixed effects model, we found that fragmentation had mostly negative effects on occurrence probability up to 1080 km from the species' range edge, independent of habitat amount. We also show that above this distance, fragmentation has predominantly positive effects, more accentuated in deforested landscapes. We demonstrate that fragmen-tation effects can be both positive and negative, indicating that different populations of the same species can respond differently depending on distance to range edge and local forest cover. Our results help clarify one of the drivers of contradictory results found in the fragmentation literature and highlight the importance of preventing habitat fragmentation for the conservation of endangered populations. Conservation initiatives should focus on minimising fragmentation closer to range edges of target species and in regions where species range edges overlap.

One of landscape ecology's main goals is to unveil how biodiversity is impacted by habitat transformation. However, the discipline suffers from significant context dependency in observed spatial and temporal trends, hindering progress towards understanding the mechanisms driving species declines and preventing the development of accurate estimates of future biodiversity change. Here, we discuss recent evidence that populations' and species' responses to habitat change at the landscape scale are modulated by factors and processes occurring at macroecological scales, such as historical disturbance rates, distance to geographic range edges, and climatic suitability. We suggest that placing landscape ecology studies in a macroecological lens will help to explain seemingly inconsistent results and will ultimately create better predictive models to help mitigate the biodiversity crisis.

Description: Metadata of the raw audio files used to investigate how variation in study design affects how indices are quantified. (All audio recorded at SAFE project) Project: This dataset was collected as part of the following SAFE research project: 3D Acoustics for Audio Monitoring of Rainforest Biodiversity Funding: These data were collected as part of research funded by: NERC (NERC QMEE CDT Studentship, NE/P012345/1, http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A)This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.XML metadata: GEMINI compliant metadata for this dataset is available hereFiles: This dataset consists of 4 files: Audio_Data_Info.xlsx, Matrix.7z, Logged.7z, Primary.7zAudio_Data_Info.xlsxThis file contains dataset metadata and 1 data tables:Metadata (described in worksheet Metadata)Description: This worksheet describes the properties of all the acoustic files linked to this DOINumber of fields: 9Number of data rows: 640Fields: root.file.name: External File Name (Field type: file)file.name: Root Audio ID (Field type: id)format: Description of file type (Field type: comments)compression: Compression level (Mp3) (Field type: ordered categorical)frame.size: Frame size (recording length) (Field type: ordered categorical)site: Field Site Location (VJR is in primary forest, E is in logged forest and D is in Matrix) (Field type: location)req.freq: Recording Frequency (Field type: numeric)date: Date (Field type: date)time: Time of Recording (Field type: time)Matrix.7zDescription: 7zip file containing 223 .flac audio recordingsLogged.7zDescription: 7zip file containing 212 .flac audio recordingsPrimary.7zDescription: 7zip file containing 205 .flac audio recordingsDate range: 2019-02-26 to 2019-06-02Latitudinal extent: 4.6644 to 4.7027Longitudinal extent: 117.5351 to 117.5914

Abstract Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down‐sampling are sometimes employed to reduce data storage and transmission costs. These parameters vary widely between experiments, with the consequences of this variation remaining mostly unknown. We analyse field recordings from North‐Eastern Borneo across a gradient of historical land use. We quantify the impact of experimental parameters (MP3 compression, recording length and temporal subsetting) on soundscape descriptors (Analytical Indices and a convolutional neural net derived AudioSet Fingerprint). Both descriptor types were tested for their robustness to parameter alteration and their usability in a soundscape classification task. We find that compression and recording length both drive considerable variation in calculated index values. However, we find that the effects of this variation and temporal subsetting on the performance of classification models is minor: performance is much more strongly determined by acoustic index choice, with Audioset fingerprinting offering substantially greater (12%–16%) levels of classifier accuracy, precision and recall. We advise using the AudioSet Fingerprint in soundscape analysis, finding superior and consistent performance even on small pools of data. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies.

Description: A series of 20 minute avifaunal and herpetofaunal point counts conducted throughout the SAFE landscape across a land degradation gradient. Point counts were spread evenly throughout the 24 hours of the day. Associated with each point count is an audio recording file, so (theoretically) this could be used as a training dataset for automated bioacoustic studies. Jani Sleutel was responsible for avifaunal surveys and Adi Shabrani / Nursyamin Zulkifli for herpetofaunal data. This experiment was primarily designed by Sarab Sethi and Rob Ewers as part of the WWF Biome Health project. Full acoustic data is hosted elsewhere, contact Sarab for more information.Project: This dataset was collected as part of the following SAFE research project: Continuous bio-acoustic monitoring (2020 extension)Funding: These data were collected as part of research funded by: NERC (NERC SSCP DTP Studentship, https://www.imperial.ac.uk/grantham/education/science-and-solutions-for-a-changing-planet-dtp/)WWF (WWF biome health project, https://www.biomehealthproject.com/)This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.Permits: These data were collected under permit from the following authorities:Sabah research council (Research licence JKM/MBS.1000-2/2 JLD.8 (63))XML metadata: GEMINI compliant metadata for this dataset is available hereFiles: This consists of 1 file: Sarab_point_count_data_SAFE_formatted_aug_2020.xlsxSarab_point_count_data_SAFE_formatted_aug_2020.xlsxThis file contains dataset metadata and 3 data tables:Point count recordings (described in worksheet Point_count_recordings)Description: Audio files and collectionNumber of fields: 9Number of data rows: 1482Fields: Point_count_ID: Point count location (Field type: id)Audio_file: Audio file ID (Field type: id)Site: Location in the SAFE landscape (Field type: location)Date: Date of recording (Field type: date)Time: Time of recording (Field type: time)Weather: Weather conditions of recording (Field type: comments)Adi_Syamin: Research assistant (Field type: numeric)Jani: Research assistant (Field type: numeric)Notes: Notes (Field type: comments)Point count data (described in worksheet Point_count_data)Description: Animals visually and audially observedNumber of fields: 8Number of data rows: 12985Fields: Point_count_ID: Point count location (Field type: id)Site: Location in the SAFE landscape (Field type: location)Species_common_name: Common name of species seen and/or seen (Field type: taxa)Est_distance: Estimated distance of species (Field type: numeric)PC_time: Time within the Point Count (0-20mins) (Field type: time)Time_of_day: The time of the observation (Field type: time)Audio_visual: Visual or Audio sighting (Field type: categorical)Notes: Notes (Field type: comments)Audio moths (described in worksheet Audio_moths)Description: Location and collection of recorder Number of fields: 9Number of data rows: 81Fields: Audio_file: Point count location (Field type: id)Site: Location in the SAFE landscape (Field type: location)Audio_moth_ID: Audio file ID (Field type: id)Tree: Location of recorder (Field type: categorical)Setup_date: Date the recorder was placed in forest (Field type: date)Setup_time: Time the recorder was set up in forest (Field type: time)Collect_date: Date the recorder was collected (Field type: date)Collect_time: Time the recorder was collected (Field type: time)Notes: Notes (Field type: comments)Date range: 2018-03-06 to 2020-03-01Latitudinal extent: 4.5000 to 5.0700Longitudinal extent: 116.7500 to 117.8200Taxonomic coverage: All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets. - Animalia - - Chordata - - - Aves - - - - Passeriformes - - - - - Leiothrichidae - - - - - Eurylaimidae - - - - - - Eurylaimus - - - - - - - Eurylaimus javanicus - - - - - - - Eurylaimus ochromalus - - - - - - Calyptomena - - - - - - - Calyptomena viridis - - - - - Pycnonotidae - - - - - - Pycnonotus - - - - - - - Pycnonotus brunneus - - - - - - - Pycnonotus melanoleucos - - - - - - - Pycnonotus atriceps - - - - - - - Pycnonotus simplex - - - - - - - Pycnonotus plumosus - - - - - - - Pycnonotus eutilotus - - - - - - - Pycnonotus erythropthalmos - - - - - - - Pycnonotus goiavier - - - - - - Alophoixus - - - - - - - Alophoixus finschii - - - - - - - Alophoixus bres - - - - - - - Alophoixus phaeocephalus - - - - - - Tricholestes - - - - - - - Tricholestes criniger - - - - - - Iole - - - - - - - Iole crypta - - - - - Pycnonotidae - - - - - - Pycnonotus - - - - - - - Pycnonotus brunneus - - - - - - - Pycnonotus melanoleucos - - - - - - - Pycnonotus atriceps - - - - - - - Pycnonotus simplex - - - - - - - Pycnonotus plumosus - - - - - - - Pycnonotus eutilotus - - - - - - - Pycnonotus erythropthalmos - - - - - - - Pycnonotus goiavier - - - - - - Alophoixus - - - - - - - Alophoixus finschii - - - - - - - Alophoixus bres - - - - - - - Alophoixus phaeocephalus - - - - - - Tricholestes - - - - - - - Tricholestes criniger - - - - - - Iole - - - - - - - Iole crypta - - - - - Chloropseidae - - - - - - Chloropsis - - - - - - - Chloropsis sonnerati - - - - - - - Chloropsis cyanopogon - - - - - Dicaeidae - - - - - - Prionochilus - - - - - - - Prionochilus maculatus - - - - - - - Prionochilus xanthopygius - - - - - - Dicaeum - - - - - - - Dicaeum trigonostigma - - - - - - - Dicaeum agile - - - - - - - Dicaeum chrysorrheum - - - - - Dicaeidae - - - - - - Prionochilus - - - - - - - Prionochilus maculatus - - - - - - - Prionochilus xanthopygius - - - - - - Dicaeum - - - - - - - Dicaeum trigonostigma - - - - - - - Dicaeum agile - - - - - - - Dicaeum chrysorrheum - - - - - Muscicapidae - - - - - - Trichixos - - - - - - - Trichixos pyrropygus (as homotypic_synonym: Copsychus pyrropygus) - - - - - - Ficedula - - - - - - - Ficedula narcissina - - - - - - Enicurus - - - - - - - Enicurus ruficapillus - - - - - - - Enicurus leschenaulti - - - - - - Muscicapa - - - - - - - Muscicapa sibirica - - - - - - - Muscicapa griseisticta - - - - - - Cyornis - - - - - - - Cyornis superbus - - - - - - - Cyornis umbratilis - - - - - - - Cyornis caerulatus - - - - - - Copsychus - - - - - - - Copsychus saularis - - - - - - - Copsychus malabaricus - - - - - - - Copsychus stricklandii - - - - - Chloropseidae - - - - - - Chloropsis - - - - - - - Chloropsis sonnerati - - - - - - - Chloropsis cyanopogon - - - - - Nectariniidae - - - - - - Arachnothera - - - - - - - Arachnothera everetti - - - - - - - Arachnothera longirostra - - - - - - Arachnothera - - - - - - - Arachnothera everetti - - - - - - - Arachnothera longirostra - - - - - - Anthreptes - - - - - - - Anthreptes simplex - - - - - - - Anthreptes rhodolaemus - - - - - - Aethopyga - - - - - - - Aethopyga siparaja - - - - - - Cinnyris - - - - - - - Cinnyris jugularis - - - - - - Leptocoma - - - - - - - Leptocoma brasiliana - - - - - - Chalcoparia - - - - - - - Chalcoparia singalensis - - - - - Monarchidae - - - - - - Hypothymis - - - - - - - Hypothymis azurea - - - - - - Terpsiphone - - - - - - - Terpsiphone incei - - - - - - - Terpsiphone affinis - - - - - Nectariniidae - - - - - - Arachnothera - - - - - - - Arachnothera everetti - - - - - - - Arachnothera longirostra - - - - - - Arachnothera - - - - - - - Arachnothera everetti - - - - - - - Arachnothera longirostra - - - - - - Anthreptes - - - - - - - Anthreptes simplex - - - - - - - Anthreptes rhodolaemus - - - - - - Aethopyga - - - - - - - Aethopyga siparaja - - - - - - Cinnyris - - - - - - - Cinnyris jugularis - - - - - - Leptocoma - - - - - - - Leptocoma brasiliana - - - - - - Chalcoparia - - - - - - - Chalcoparia singalensis - - - - &ensp

Automated monitoring approaches offer an avenue to deep, large-scale insight into how ecosystems respond to human pressures. Since sensor technology and data analyses are often treated independantly, there are no open-source examples of end-to-end, real-time ecological monitoring networks. Here, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real-time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public-facing website. We provide the open-source code and design of our monitoring devices, the central web2py database and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real-time analyses of the eco-acoustic data. By detailing a fully functional, open-source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology.

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often re-quires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to em-bed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identi-fication of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular envi-ronmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global col-laborative autonomous ecosystem monitoring efforts.