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The Intergovernmental Panel on Climate Change's (IPCC) special report on global warming of 1.5 degrees Celsius (degrees C) makes clear that most scenarios (90%) that hold warming to 1.5 degrees C by 2100 include an overshoot, or a period in which the temperature increase exceeds 1.5 degrees C before declining to the end-of-century 1.5 degrees C goal (IPCC 2018). An overshoot is also possible for 2 degrees C scenarios, given the lack of ambition in existing mitigation commitments. Current conservation policy and planning does not adequately account for the high likelihood of a temperature overshoot in a 1.5 degrees C scenario, but the impacts of an overshoot on conservation may be large. Efforts to avoid an overshoot must be increased through more ambitious mitigation commitments and a greater focus on peak warming rather than end-of-century outcomes. Simultaneously, conservation planning should account for such impacts by anticipating more dynamic systems that carry greater uncertainties and potentially irreversible changes that may persist even as temperatures peak and decline.

SummaryWildlife is an essential component of all ecosystems. Most places in the globe do not have local, timely information on which species are present or how their populations are changing. With the arrival of new technologies, camera traps have become a popular way to collect wildlife data. However, data collection has increased at a much faster rate than the development of tools to manage, process and analyse these data. Without these tools, wildlife managers and other stakeholders have little information to effectively manage, understand and monitor wildlife populations. We identify four barriers that are hindering the widespread use of camera trap data for conservation. We propose specific solutions to remove these barriers integrated in a modern technology platform called Wildlife Insights. We present an architecture for this platform and describe its main components. We recognize and discuss the potential risks of publishing shared biodiversity data and a framework to mitigate those risks. Finally, we discuss a strategy to ensure platforms like Wildlife Insights are sustainable and have an enduring impact on the conservation of wildlife.

Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services1. As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred1–6. However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats—one of the most extensive coastal ecosystems—remain unknown7. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation7, occupy at least 127,921 km2 (124,286–131,821 km2, 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development1, reduced sediment delivery from major rivers8,9, sinking of riverine deltas8,10, increased coastal erosion and sea-level rise11 signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems. Analyses of over 700,000 satellite images to map the global extent of tidal flats over the past thirty years, and enable assessments of the status and likely future trajectories of these coastal ecosystems.

This paper reports on recent improvements made to the FORMA ( Hammer et al., 2014a ) data product. The resulting system, FORMA250, is a 250-m alerting system updated daily. FORMA250 alerts are available through Global Forest Watch. These alerts can empower law enforcement officials, government agencies responsible for protecting forests, nongovernmental organizations, companies committed to sustainable forest management practices and supply chains, indigenous groups and forest-dependent communities. In addition, the alerts provide useful information for researchers who study temporal and spatial patterns of forest clearing.

Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.

We assess progress toward the protection of 50% of the terrestrial biosphere to address the species-extinction crisis and conserve a global ecological heritage for future generations. Using a map of Earth's 846 terrestrial ecoregions, we show that 98 ecoregions (12%) exceed Half Protected; 313 ecoregions (37%) fall short of Half Protected but have sufficient unaltered habitat remaining to reach the target; and 207 ecoregions (24%) are in peril, where an average of only 4% of natural habitat remains. We propose a Global Deal for Nature—a companion to the Paris Climate Deal—to promote increased habitat protection and restoration, national- and ecoregion-scale conservation strategies, and the empowerment of indigenous peoples to protect their sovereign lands. The goal of such an accord would be to protect half the terrestrial realm by 2050 to halt the extinction crisis while sustaining human livelihoods.

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~ 3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.

Boreal forests are globally extensive and store large amounts of carbon, but recent climate change has led to drier conditions and increasing fire activity. The objective of this study is to quantify trends in fire size and frequency using data spanning multiple scales in space and time. We use multi-temporal Landsat image compositing on Google Earth Engine and validate results with reference fire maps from the Canadian Park Service. We also interpret general fire trends through the concept of Self-Organized Criticality (SOC). Our study site is Wood Buffalo National Park, which is a fire hot spot in Canada due to frequent lightning ignitions. The relativize differenced normalized burn ratio (RdNBR) was the most accurate Landsat-based burn severity metric we evaluated (52.2% producer's accuracy, 87.6% user's accuracy). The Landsat-based burn severity maps provided a better fit for a linear relationship on the log-log scale of fire size and frequency than a manually drawn fire map. Landsat-based fire trends since 1990 conformed to a power-law distribution with a slope of 1.9, which is related to fractal dimensions of the satellite-based fire perimeter shapes. The unburned and low-severity patches within the burn severity mosaic influenced the power-law slope and associated fractal dimensionality. This study demonstrates a multi-scale and multi-dataset technique to quantify general fire trends and changing fire cycles in remote locations and establishes a baseline database for assessing future fire activity. Testing criticality by power laws helps to quantify emergent trends of contemporary fire regimes, which could inform the strategic application of prescribed fire and other management activities. Natural resource managers can utilize information from this study to understand local ecosystem adaptability to large fire events and ecosystem stability in the context of recent increasing fire activity.

Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services

New advances in satellite data acquisition and processing offer promise for monitoring agricultural lands globally. Using these data to estimate crop yields for individual fields would benefit both crop management and scientific research, especially for areas where reliable ground-based estimates are not currently made. Here we introduce a generalized approach for mapping crop yields with satellite data and test its predictions for yields across more than 17,000 maize fields and 11,000 soybean fields spanning multiple states and years in the Midwestern United States. The method, termed SCYM (a scalable satellite-based crop yield mapper), uses crop model simulations to train statistical models for different combinations of possible image acquisition dates, and these are then applied to Landsat and gridded weather data within the Google Earth Engine platform, where the Landsat is composited to find the “best” dates of observations on a pixel-by-pixel basis. SCYM estimates successfully captured a significant fraction of maize yield variation in all state-years, with a range of 14–58% and an average of 35% for this particular study region and crop. Similar results were observed for soybean, with an average of 32% of yield variation captured. The multi-year yield estimates were also used to examine the temporal persistence of yield advantages for the top yielding fields in different counties, which is one measure of how important factors such as farmer skill are in explaining yield gaps. The strength of the SCYM approach lies in its ability to leverage physiological knowledge embedded in crop models to interpret satellite observations in a scalable way, as it can be readily applied to new crops, regions, and types and timing of remote sensing observations without the need for ground calibration.

Forests in Flux Forests worldwide are in a state of flux, with accelerating losses in some regions and gains in others. Hansen et al. (p. 850 ) examined global Landsat data at a 30-meter spatial resolution to characterize forest extent, loss, and gain from 2000 to 2012. Globally, 2.3 million square kilometers of forest were lost during the 12-year study period and 0.8 million square kilometers of new forest were gained. The tropics exhibited both the greatest losses and the greatest gains (through regrowth and plantation), with losses outstripping gains.

Bonheur du citoyen, malheur de l’écrivain. Pauvreté du roman judéo-français, richesse du roman judéo-allemand dans l’entre-deux-guerres.

Across the globe, accurate national spatial datasets on cropland extent are lacking. These are necessary for a number of reasons, including accurately monitoring and predicting crop yield, land use, land acquisitions and food security. This study describes the use of crowdsourcing information retrieved over Ethiopia depicting the extent of cropland area. This information has been used to train a classification algorithm in Google Earth Engine to produce a continuous cropland extent map of Ethiopia. Preliminary results of this novel approach are encouraging, with an overall validity of 96%.

The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are reviewed. We contrast the common usage of a static 'snapshot' hierarchy in ontological representations of taxonomy with a more realistic situation that involves dynamic, piece-meal revisions of particular taxonomic groups and requires alignment with relevant preceding perspectives. Taxonomic practice is characterized by a range of phenomena that are orthogonal to the logical semantic background from which ontological entities and relationships originate, and therefore pose special challenges to ontological representation and reasoning. Among these phenomena are: (1) the notion that there is a single phylogenetic hierarchy in nature which taxonomy can only gradually approximate; (2) the evolvability of taxa which means that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life; (3) the hybrid approach of defining taxa both in reference to properties (intensional) and members (ostensive) which undermines the individual/class dichotomy sustaining conventional ontologies; (4) the idiosyncratic yet inferentially valuable usage of Linnaean ranks; (5) the indelible and semantically complex 250-year legacy of nomenclatural and taxonomic changes that characterizes the current system; (6) the insufficient taxonomic exploration of large portions of the tree of life; and the need to use a sophisticated terminology for aligning taxonomic entities in order to integrate both (7) single and (8) multiple hierarchies. We briefly such how such integration may proceed based on an initial expert alignment of concept relationship and subsequent use of first-order logic algorithms to maximize consistency, reveal implied relationships, and ultimately merge taxonomies. In light of the aforementioned obstacles, we suggest that research along the taxonomy/ontology interface should focus on either strictly nomenclatural entities or specialize in ontology-driven methods for producing alignments between multiple taxonomies. We furthermore suggest that the prospects of developing successful ontologies for taxonomy will largely depend on the ability of the taxonomic expert community to present their phylogenies and classifications in a way that is more compatible with ontological reasoning than concurrent practice. Minimally, this means (1) adopting rigorous standards for linking new core taxonomies to relevant peripheral taxonomies through comprehensive alignments so that their ontological/taxonomic connections are transparent; (2) using lineage-specific ontological standards for phenotype-based accounts of taxa while taking into account the phylogenetic contextuality of phenotypic descriptors; (3) presenting all nomenclatural and taxonomic novelties in an explicit, ontology-compatible format, including intensional and ostensive definitions; and (4) offering comprehensive intensional/ostensive alignments to entities in relevant preceding taxonomies.

We consider the task of merging datasets that have been organized using different, but aligned taxonomies. We assume such a merge is intended to create a single dataset that unambiguously describes the information in the source datasets using the alignment. We also assume that the merged result should reflect the observations of the datasets as specifically as possible. Typically, there will be no single merge result that is both unambiguous and maximally specific. In this case, a user may be provided with a set of possible merged datasets. If the user requires a single dataset, that dataset loses specificity. Here we examine whether the data exchange setting can provide a way to derive a “best-effort” merge. We find that the data exchange setting might be a good candidate for providing the merge, but further research is needed.

Collaborative environmental in situ data collection occurs when a team of investigators goes into the field together to collect environmental data. These data might be necessary, e.g., for a biodiversity inventory, compilation of a soil density map, or to estimate above-ground forest carbon stocks. Investigators will often arrive at a location and disperse, collecting data, and then compiling it either in the field, or at a later time. Typically, an area will be divided into a set of plots, and within those, subplots. Teams of investigators will visit each of these plots with standardized forms and specialized equipment for collecting the data of interest. For example, in a forest inventory, investigators might collect data about the diameter and species of the trees in the forest, the trees’ health, fire damage and soil quality at the plot, proximity to roads, and whether any logging has taken place.

Biodiversity informatics (BDI) information is both highly localized and highly distributed. The temporal and spatial contexts of data collection events are generally of primary importance in BDI studies, and most studies are focused around specific localities. At the same time, data are collected by many groups working independently, but often at the same sites, leading to a distribution of data. BDI data are also distributed over time, due to protracted longitudinal studies, and the continuously evolving meanings of taxonomic names. Ambient data integration provides new opportunities for collecting, sharing, and analyzing BDI data, and the nature of BDI data poses interesting challenges for applications of ADI. This paper surveys recent work on utilization of BDI data in the context of ADI. Topics covered include applying ADI to species identification, data security, annotation and provenance sharing, and coping with multiple competing classification ontologies. We conclude with a summary of requirements for applying ADI to biodiversity informatics.

We present a method for using aligned ontologies to merge taxonomically organized data sets that have apparently compatible schemas, but potentially different semantics for corresponding domains. We restrict the relationships involved in the alignment to basic set relations and disjunctions of these relations. A merged data set combines the domains of the source data set attributes, conforms to the observations reported in both data sets, and minimizes uncertainty introduced by ontology alignments. We find that even in very simple cases, merging data sets under this scenario is non-trivial. Reducing uncertainty introduced by the ontology alignments in combination with the data set observations often results in many possible merged data sets, which are managed using a possible worlds semantics. The primary contributions of this paper are a framework for representing aligned data sets and algorithms for merging data sets that report the presence and absence of taxonomically organized entities, including an efficient algorithm for a common data set merging scenario.

Taxonomies are widely used to classify information, and multiple (possibly competing) taxonomies often exist for the same domain. Given a set of correspondences between two taxonomies, it is often necessary to "merge" the taxonomies, thereby creating a unified taxonomy (e.g., that can then be used by data integration and discovery applications). We present an algorithm for merging taxonomies that have been related using articulations given as RCC-5 constraints. Two taxa N and M can be related using (disjunctions of) the five base relations in RCC-5: N a M; N -- M; N -- M; N -- M (partial overlap of N and M); and N ! M (disjointness: N ) M = O). RCC-5 is increasingly being adopted by scientists to specify mappings between large species taxonomies. We discuss the properties of the proposed merge algorithm and evaluate our approach using real-world biological taxonomies.

Taxonomically organized data pervade science, business and everyday life. Unfortunately, taxonomies are often underspecified, limiting their utility in contexts such as data integration, information navigation and autonomous agent communication. This work formalizes taxonomies and relationships between them as formulas in logic. This formalization concretizes notions such as consistency and inconsistency of taxonomies and articulations (inter-taxonomic relations) between them, enables the derivation of new articulations based on a given set of taxonomies and articulations and provides a framework for testing assumptions about underspecified taxonomies.Given the typical intractability of reasoning with taxonomies and articulations, this research investigates many optimizations: from those that reduce the search space, to those that leverage parallel processing, to those investigating logics more tractable than first-order logic (e.g., monadic first-order logic, propositional logic, description logics, and subsets of the RCC-5 spatial algebra). Finally, in addition to reasoning with taxonomies and articulations, this research investigates how to repair inconsistent taxonomies and articulations, how to explain inconsistencies and discovered relations, and how to merge taxonomies given articulations. Critical to this research is the development of a framework for testing logics and support for the development of taxonomies and articulations. This framework, CleanTax is already well under way and has been used to study articulations between two large-scale biological taxonomies.

Similar to content on the web, scientific data is highly heterogeneous and can benefit from rich semantic descriptions. We are particularly interested in developing an infrastructure for expressing explicit semantic descriptions of ecological data (and life-sciences data in general), and exploiting these descriptions to provide support for automated data integration and transformation within scientific workflows [2]. Using semantic descriptions, our goal is to provide scientists with: (1) tools to easily search for and retrieve datasets relevant to their study (i.e., data procurement), (2) the ability to select a subset of returned datasets as input to a scientific workflow, and (3) automated integration and restructuring of the selected datasets for seamless workflow execution.