Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species' niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

World of Crayfish™: a web platform towards real-time global mapping of freshwater crayfish and their pathogens

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.

Using a cognitive model to understand crowdsourced data from citizen scientists

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources.

Drivers of support for citizen science across state wildlife management agencies in the United States

Citizen science (CS) is gaining recognition as a valuable approach to meet data needs for environmental projects while fostering collaboration between scientists and members of the public. Despite increasing implementation of CS by natural resource entities, organizations' motivations for engaging in CS remain poorly understood. We examined the utility of the theory of planned behavior (TPB) and social exchange theory (SET) in identifying factors influencing support of CS by scientific organizations. To test predictions of the TPB and SET theories, we surveyed (quantitative, web based) state wildlife agency staff in the United States on their perceptions of organizational engagement in CS. We divided questions that measured TPB items into individual and organizational components to address the influence of personal- and organization-level decision-making on staff perceptions and attitudes. We used structural equation modeling to identify key constructs that influence staff support of CS in state wildlife agencies. The survey yielded 627 responses across 44 states. Both TPB and SET constructs accurately predicted staff support of CS; however, measures from SET (e.g., public engagement benefits and costs of CS to scientific credibility) were most influential (i.e., TPB constructs had less impact). Our findings indicate that organizational support for CS is primarily influenced by assessment of trade-offs among perceived costs and benefits. Indicators of support for CS were further elucidated by including measures from the TPB model. Based on our results, we suggest that natural resource entities give careful consideration to CS project design, develop thorough communication and data management plans, and practice iterative evaluation of CS project productivity.

The Role of Community Science in Entomology

Community (or citizen) science, the involvement of volunteers in scientific endeavors, has a long history. Over the past few centuries, the contributions of volunteers to our understanding of patterns and processes in entomology have been inspiring. From the collation of large-scale and long-term data sets, which have been instrumental in underpinning our knowledge of the status and trends of many insect groups, to action, including species management, whether for conservation or control, community scientists have played pivotal roles. Contributions, such as pest monitoring by farmers and species discoveries by amateur naturalists, set foundations for the research engaging entomologists today. The next decades will undoubtedly bring new approaches, tools, and technologies to underpin community science. The potential to increase inclusion within community science is providing exciting opportunities within entomology. An increase in the diversity of community scientists, alongside an increasing taxonomic and geographic breadth of initiatives, will bring enormous benefits globally for people and nature.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

The Effect of Calcium Ions on Mechanosensation and Neuronal Activity in Proprioceptive Neurons

Proprioception of all animals is important in being able to have coordinated locomotion. Stretch activated ion channels (SACs) transduce the mechanical force into electrical signals in the proprioceptive sensory endings. The types of SACs vary among sensory neurons in animals as defined by pharmacological, physiological and molecular identification. The chordotonal organs within insects and crustaceans offer a unique ability to investigate proprioceptive function. The effects of the extracellular environment on neuronal activity, as well as the function of associated SACs are easily accessible and viable in minimal saline for ease in experimentation. The effect of extracellular [Ca2+] on membrane properties which affect voltage-sensitivity of ion channels, threshold of action potentials and SACs can be readily addressed in the chordotonal organ in crab limbs. It is of interest to understand how low extracellular [Ca2+] enhances neural activity considering the SACs in the sensory endings could possibly be Ca2+ channels and that all neural activity is blocked with Mn2+. It is suggested that axonal excitability might be affected independent from the SAC activity due to potential presence of calcium activated potassium channels (K(Ca)) and the ability of Ca2+ to block voltage gated Na+ channels in the axons. Separating the role of Ca2+ on the function of the SACs and the excitability of the axons in the nerves associated with chordotonal organs is addressed. These experiments may aid in understanding the mechanisms of neuronal hyperexcitability during hypocalcemia within mammals.

Temporal patterns of wildlife roadkill in the UK

Wildlife-vehicle collisions are one of the main causes of mortality for wild mammals and birds in the UK. Here, using a dataset of 54,000+ records collated by a citizen science roadkill recording scheme between 2014-2019, we analyse and present temporal patterns of wildlife roadkill of the 19 most commonly reported taxa in the UK (84% of all reported roadkill). Most taxa (13 out of 19) showed significant and consistent seasonal variations in road mortality and fitted one of two seasonal patterns; bimodal or unimodal: only three species (red fox Vulpes vulpes, European polecat Mustela putorius and Reeves' muntjac deer Muntiacus reevesi) showed no significant seasonality. Species that increase movement in spring and autumn potentially have bimodal patterns in roadkill due to the increase in mate-searching and juvenile dispersal during these respective time periods (e.g. European badger Meles meles). Unimodal patterns likely represent increased mortality due to a single short pulse in activity associated with breeding (e.g. birds) or foraging (e.g. grey squirrels Sciurus carolinensis in autumn). Importantly, these patterns also indicate periods of increased risk for drivers, potentially posing a greater threat to human welfare. In addition to behaviour-driven annual patterns, abiotic factors (temperature and rainfall) explained some variance in roadkill. Notably, high rainfall was associated with decreased observations of two bird taxa (gulls and Eurasian magpies Pica pica) and European rabbit Oryctolagus cuniculus. By quantifying seasonal patterns in roadkill, we highlight a significant anthropogenic impact on wild species, which is important in relation to conservation, animal welfare, and human safety.

Opportunities and limitations: A comparative analysis of citizen science and expert recordings for bioacoustic research

Citizen science is an approach that has become increasingly popular in recent years. Despite this growing popularity, there still is widespread scepticism in the academic world about the validity and quality of data from citizen science projects. And although there might be great potential, citizen science is a rarely used approach in the field of bioacoustics. To better understand the possibilities, but also the limitations, we here evaluated data generated in a citizen science project on nightingale song as a case study. We analysed the quantity and quality of song recordings made in a non-standardized way with a smartphone app by citizen scientists and the standardized recordings made with professional equipment by academic researchers. We made comparisons between the recordings of the two approaches and among the user types of the app to gain insights into the temporal recording patterns, the quantity and quality of the data. To compare the deviation of the acoustic parameters in the recordings with smartphones and professional devices from the original song recordings, we conducted a playback test. Our results showed that depending on the user group, citizen scientists produced many to a lot of recordings of valid quality for further bioacoustic research. Differences between the recordings provided by the citizen and the expert group were mainly caused by the technical quality of the devices used—and to a lesser extent by the citizen scientists themselves. Especially when differences in spectral parameters are to be investigated, our results demonstrate that the use of the same high-quality recording devices and calibrated external microphones would most likely improve data quality. We conclude that many bioacoustic research questions may be carried out with the recordings of citizen scientists. We want to encourage academic researchers to get more involved in participatory projects to harness the potential of citizen science—and to share scientific curiosity and discoveries more directly with society.

Assessing the potential for deep learning and computer vision to identify bumble bee species from images

Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.

Generalized model-based solutions to false-positive error in species detection/nondetection data

Detection/nondetection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often imperfect. Recent model development has focused on accounting for both false-positive and false-negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false-positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false-positive error in detection/nondetection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false-positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false-positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false-positive error following the site-confirmation or observation-confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false-positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) abundance in Wisconsin, USA reinforce concerns that biases induced by false-positive error can also distort spatial predictions often used to guide decision making. Model sensitivity to false-positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

The effects of tricaine mesylate on arthropods: crayfish, crab and Drosophila

Tricaine mesylate, also known as MS-222, was investigated to characterize its effects on sensory neurons, synaptic transmission at the neuromuscular junction, and heart rate in invertebrates. Three species were examined: Drosophila melanogaster, blue crab (Callinectes sapidus), and red swamp crayfish (Procambarus clarkii). Intracellular measures of action potentials in motor neurons of the crayfish demonstrated that MS-222 dampened the amplitude, suggesting that voltage-gated Na + channels are blocked by MS-222. This is likely the mechanism behind the reduced activity measured in sensory neurons and depressed synaptic transmission in all three species as well as reduced cardiac function in the larval Drosophila. To address public access to data, a group effort was used for analysis of given data sets, blind to the experimental design, to gauge analytical accuracy. The determination of a threshold in analysis for measuring extracellular recorded sensory events is critical and is not easily performed with commercial software.