Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data

1. The accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording. 2. Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer‐led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross‐referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi‐input neural network models that synthesize metadata and images to identify records to species level. 3. We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image‐only baseline of 48.2%, we observe a 9.1 percentage‐point improvement in top‐1 accuracy with a multi‐input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data are being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilizing similar pieces of evidence as human naturalists to make identifications. 4. Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualization offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualizing images has the potential to lead to a step change in the accuracy of automated identification tools, with considerable benefits for large‐scale verification of submitted records

AI naturalists might hold the key to unlocking biodiversity data in social media imagery

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis

Recent trends in UK insects that inhabit early successional stages of ecosystems

Improved recording of less popular groups, combined with new statistical approaches that compensate for datasets that were hitherto too patchy for quantitative analysis, now make it possible to compare recent trends in the status of UK invertebrates other than butterflies. Using BRC datasets, we analysed changes in status between 1992 and 2012 for those invertebrates whose young stages exploit early seral stages within woodland, lowland heath and semi-natural grassland ecosystems, a habitat type that had declined during the 3 decades previous to 1990 alongside a disproportionally high number of Red Data Book species that were dependent on it. Two clear patterns emerged from a meta-analysis involving 299 classifiable species belonging to ten invertebrate taxa: (i) during the past 2 decades, most early seral species that are living near their northern climatic limits in the UK have increased relative to the more widespread members of these guilds whose distributions were not governed by a need for a warm micro-climate; and (ii) independent of climatic constraints, species that are restricted to the early stages of woodland regeneration have fared considerably less well than those breeding in the early seral stages of grasslands or, especially, heathland. The first trend is consistent with predicted benefits for northern edge-of-range species as a result of climate warming in recent decades. The second is consistent with our new assessment of the availability of early successional stages in these three ecosystems since c. 1990. Whereas the proportion and continuity of early seral patches has greatly increased within most semi-natural grasslands and lowland heaths, thanks respectively to agri-environmental schemes and conservation management, the representation of fresh clearings has continued to dwindle within UK woodlands, whose floors are increasingly shaded and ill-suited for this important guild of invertebrates

Statistics for citizen science: extracting signals of change from noisy ecological data

1. Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Long-term monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant timescales. 2. We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends. 3. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful. 4. We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and Occupancy-Detection models. 5. The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection

Citizen meets social science : Predicting volunteer involvement in a global freshwater monitoring experiment

FreshWater Watch is a global citizen science project that seeks to advance the understanding and stewardship of freshwater ecosystems across the globe through analysis of their physical and chemical properties by volunteers. To date, literature concerning citizen science has mainly focused on its potential to generate unprecedented volumes of data. In this paper, we focus instead on the data relating to the volunteer experience and ask key questions about volunteer engagement with the project. For example, we ask what factors influence: a) volunteer data submission following a training event and b) the number of water quality samples volunteers subsequently submit. We used a binomial model to identify the factors that influence the retention of volunteers after training. In addition, we used a generalized linear model (GLM) to examine the factors that affected the number of samples each citizen scientist submitted. In line with other citizen science projects, most people trained did not submit any data, and 1% of participants contributed 47% of the data. We found that the statistically significant factors associated with submission of data after training were: whether training was given on how to upload data, the number of volunteers that attended the training, whether the volunteer was assigned to a research team, the outside temperature, and the average engagement of others in the training group. The statistically significant factors associated with the quantity of data submitted were: the length of time volunteers were active in the project, whether training took place as part of a paid work day, the difficulty of the sampling procedure, how socially involved volunteers were in the project, average sampling group size, and engagement with online learning modules. Based on our results, we suggest that intrinsic motivation may be important for predicting volunteer retention after training and the number of samples collected subsequently. We suggest that, to maximize the contribution of citizen science to our understanding of the world around us, there is an urgent need to better understand the factors that drive volunteer retention and engagement

Microclimate affects landscape level persistence in the British Lepidoptera

Microclimate has been known to drive variation in the distribution and abundance of insects for some time. Until recently however, quantification of microclimatic effects has been limited by computing constraints and the availability of fine-scale biological data. Here, we tested fine-scale patterns of persistence/extinction in butterflies and moths against two computed indices of microclimate derived from Digital Elevation Models: a summer solar index, representing fine-scale variation in temperature, and a topographic wetness index, representing fine-scale variation in moisture availability. We found evidence of microclimate effects on persistence in each of four 20 × 20 km British landscapes selected for study (the Brecks, the Broads, Dartmoor, and Exmoor). Broadly, local extinctions occurred more frequently in areas with higher minimum or maximum solar radiation input, while responses to wetness varied with landscape context. This negative response to solar radiation is consistent with a response to climatic warming, wherein grid squares with particularly high minimum or maximum insolation values provided an increasingly adverse microclimate as the climate warmed. The variable response to wetness in different landscapes may have reflected spatially variable trends in precipitation. We suggest that locations in the landscape featuring cooler minimum and/or maximum temperatures could act as refugia from climatic warming, and may therefore have a valuable role in adapting conservation to climatic change

Using biological records to infer long-term occupancy trends of mammals in the UK

Conservation action is usually triggered by detecting trends in species’ population size, geographical range, or occupancy (proportion of sites occupied). Robust estimates of these metrics are often required by policy makers and practitioners, yet many species lack dedicated monitoring schemes. An alternative source of data for trend estimation is provided by biological records, i.e., species presence information. In the UK, there are millions of such records, but biological trend assessments are often hindered by biases caused by the unstructured way in which they are collected. Recent advances in occupancy modelling that account for changes in survey effort and detectability over time mean that robust occupancy trends can now be estimated from these records. By grouping mammal species into survey assemblages — species likely to be recorded at the same time — and applying occupancy models, this study provides estimates of long-term (1970 to 2016) occupancy trends for 37 terrestrial mammal species from the UK. The inter-annual occupancy growth rates for these species ranged from -4.26% to 11.25%. This information was used to classify two species as strongly decreasing, five as decreasing, 12 as no change, 11 as increasing and seven as strongly increasing. Viewing the survey assemblages as a whole, the occupancy growth rates for small mammals were, on average, decreasing (-0.8% SD 1.57), whereas bats and deer (0.9% SD 1.30) were increasing (3.8% SD 3.25; 0.9% SD 1.30 respectively), and mid-sized mammals were stable (-0.3 SD 1.72). These results contribute much-needed information on a number of data deficient species, and provide evidence for prioritising conservation action

Sympatric woodland Myotis bats form tight-knit social groups with exclusive roost home ranges

Background: The structuring of wild animal populations can influence population dynamics, disease spread, and information transfer. Social network analysis potentially offers insights into these processes but is rarely, if ever, used to investigate more than one species in a community. We therefore compared the social, temporal and spatial networks of sympatric Myotis bats (M. nattereri (Natterer's bats) and M. daubentonii (Daubenton's bats)), and asked: (1) are there long-lasting social associations within species? (2) do the ranges occupied by roosting social groups overlap within or between species? (3) are M. daubentonii bachelor colonies excluded from roosting in areas used by maternity groups? Results: Using data on 490 ringed M. nattereri and 978 M. daubentonii from 379 colonies, we found that both species formed stable social groups encompassing multiple colonies. M. nattereri formed 11 mixed-sex social groups with few (4.3%) inter-group associations. Approximately half of all M. nattereri were associated with the same individuals when recaptured, with many associations being long-term (>100 days). In contrast, M. daubentonii were sexually segregated; only a quarter of pairs were associated at recapture after a few days, and inter-sex associations were not long-lasting. Social groups of M. nattereri and female M. daubentonii had small roost home ranges (mean 0.2 km2 in each case). Intra-specific overlap was low, but inter-specific overlap was high, suggesting territoriality within but not between species. M. daubentonii bachelor colonies did not appear to be excluded from roosting areas used by females. Conclusions: Our data suggest marked species- and sex-specific patterns of disease and information transmission are likely between bats of the same genus despite sharing a common habitat. The clear partitioning of the woodland amongst social groups, and their apparent reliance on small patches of habitat for roosting, means that localised woodland management may be more important to bat conservation than previously recognised

A national-scale assessment of climate change impacts on species: assessing the balance of risks and opportunities for multiple taxa

It is important for conservationists to be able to assess the risks that climate change poses to species, in order to inform decision making. Using standardised and repeatable methods, we present a national-scale assessment of the risks of range loss and opportunities for range expansion, that climate change could pose for over 3,000 plants and animals that occur in England. A basic risk assessment that compared projected future changes in potential range with recently observed changes classified 21% of species as being at high risk and 6% at medium risk of range loss under a B1 climate change scenario. A greater number of species were classified as having a medium (16%) or high (38%) opportunity to potentially expand their distribution. A more comprehensive assessment, incorporating additional ecological information, including potentially confounding and exacerbating factors, was applied to 402 species, of which 35 % were at risk of range loss and 42 % may expand their range extent. This study covers a temperate region with a significant proportion of species at their poleward range limit. The balance of risks and opportunities from climate change may be different elsewhere. The outcome of both risk assessments varied between taxonomic groups, with bryophytes and vascular plants containing the greatest proportion of species at risk from climate change. Upland habitats contained more species at risk than other habitats. Whilst the overall pattern was clear, confidence was generally low for individual assessments, with the exception of well-studied taxa such as birds. In response to climate change, nature conservation needs to plan for changing species distributions and increasing uncertainty of the future

Prototype Testing Results of Charged Particle Detectors and Critical Subsystems for the ESRA Mission to GTO

The Experiment for Space Radiation Analysis (ESRA) is the latest of a series of Demonstration and Validation (DemVal) missions built by the Los Alamos National Laboratory, with the focus on testing a new generation of plasma and energetic paritcle sensors along with critical subsystems. The primary motivation for the ESRA payloads is to minimize size, weight, power, and cost while still providing necessary mission data. These new instruments will be demonstrated by ESRA through ground-based testing and on-orbit operations to increase their technology readiness level such that they can support the evolution of technology and mission objectives. This project will leverage a commercial off-the-shelf CubeSat avionics bus and commercial satellite ground networks to reduce the cost and timeline associated with traditional DemVal missions. The system will launch as a ride share with the DoD Space Test Program to be inserted in Geosynchronous Transfer Orbit (GTO) and allow observations of the Earth\u27s radiation belts. The ESRA CubeSat consists of two science payloads and several subsystems: the Wide field-of-view Plasma Spectrometer, the Energetic Charged Particle telescope, high voltage power supply, payload processor, flight software architecture, and distributed processor module. The ESRA CubeSat will provide measurements of the plasma and energetic charged particle populations in the GTO environment for ions ranging from ~100 eV to ~1000 MeV and electrons with energy ranging from 100 keV to 20 MeV. ESRA will utilize a commercial 12U bus and demonstrate a low-cost, rapidly deployable spaceflight platform with sufficient SWAP to enable efficient measurements of the charged particle populations in the dynamic radiation belts