Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.     

Design principles for engaging and retaining virtual citizen scientists

Citizen science initiatives encourage volunteer participants to collect and interpret data and contribute to formal scientific projects. The growth of virtual citizen science (VCS), facilitated through websites and mobile applications since the mid‐2000s, has been driven by a combination of software innovations and mobile technologies, growing scientific data flows without commensurate increases in resources to handle them, and the desire of internet‐connected participants to contribute to collective outputs. However, the increasing availability of internet‐based activities requires individual VCS projects to compete for the attention of volunteers and promote their long‐term retention. We examined program and platform design principles that might allow VCS initiatives to compete more effectively for volunteers, increase productivity of project participants, and retain contributors over time. We surveyed key personnel engaged in managing a sample of VCS projects to identify the principles and practices they pursued for these purposes and led a team in a heuristic evaluation of volunteer engagement, website or application usability, and participant retention. We received 40 completed survey responses (33% response rate) and completed a heuristic evaluation of 20 VCS program sites. The majority of the VCS programs focused on scientific outcomes, whereas the educational and social benefits of program participation, variables that are consistently ranked as important for volunteer engagement and retention, were incidental. Evaluators indicated usability, across most of the VCS program sites, was higher and less variable than the ratings for participant engagement and retention. In the context of growing competition for the attention of internet volunteers, increased attention to the motivations of virtual citizen scientists may help VCS programs sustain the necessary engagement and retention of their volunteers.     

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.     

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo‐based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short‐term (single session) and longer‐term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo‐based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long‐lived citizen science projects.     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

Estimating species richness and accumulation by modeling species occurrence and detectability

A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for imperfect detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nondetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for imperfect detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.     

Influence of volunteer and project characteristics on data quality of biological surveys

Volunteer involvement in biological surveys is becoming common in conservation and ecology, prompting questions on the quality of data collected in such surveys. In a systematic review of the peer‐reviewed literature on the quality of data collected by volunteers, we examined the characteristics of volunteers (e.g., age, prior knowledge) and projects (e.g., systematic vs. opportunistic monitoring schemes) that affect data quality with regards to standardization of sampling, accuracy and precision of data collection, spatial and temporal representation of data, and sample size. Most studies (70%, n = 71) focused on the act of data collection. The majority of assessments of volunteer characteristics (58%, n = 93) examined the effect of prior knowledge and experience on quality of the data collected, often by comparing volunteers with experts or professionals, who were usually assumed to collect higher quality data. However, when both groups’ data were compared with the same accuracy standard, professional data were more accurate in only 4 of 7 cases. The few studies that measured precision of volunteer and professional data did not conclusively show that professional data were less variable than volunteer data. To improve data quality, studies recommended changes to survey protocols, volunteer training, statistical analyses, and project structure (e.g., volunteer recruitment and retention).     

Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.     

Detecting Insect Pollinator Declines on Regional and Global Scales

Recently there has been considerable concern about declines in bee communities in agricultural and natural habitats. The value of pollination to agriculture, provided primarily by bees, is >$200 billion/year worldwide, and in natural ecosystems it is thought to be even greater. However, no monitoring program exists to accurately detect declines in abundance of insect pollinators; thus, it is difficult to quantify the status of bee communities or estimate the extent of declines. We used data from 11 multiyear studies of bee communities to devise a program to monitor pollinators at regional, national, or international scales. In these studies, 7 different methods for sampling bees were used and bees were sampled on 3 different continents. We estimated that a monitoring program with 200–250 sampling locations each sampled twice over 5 years would provide sufficient power to detect small (2–5%) annual declines in the number of species and in total abundance and would cost U.S.$2,000,000. To detect declines as small as 1% annually over the same period would require >300 sampling locations. Given the role of pollinators in food security and ecosystem function, we recommend establishment of integrated regional and international monitoring programs to detect changes in pollinator communities. Detección de Declinaciones de Insectos Polinizadores a Escalas Regional y Global     

Spatially Explicit Power Analyses for Occupancy‐Based Monitoring of Wolverine in the U.S. Rocky Mountains

Conservation scientists and resource managers often have to design monitoring programs for species that are rare or patchily distributed across large landscapes. Such programs are frequently expensive and seldom can be conducted by one entity. It is essential that a prospective power analysis be undertaken to ensure stated monitoring goals are feasible. We developed a spatially based simulation program that accounts for natural history, habitat use, and sampling scheme to investigate the power of monitoring protocols to detect trends in population abundance over time with occupancy‐based methods. We analyzed monitoring schemes with different sampling efforts for wolverine (Gulo gulo) populations in 2 areas of the U.S. Rocky Mountains. The relation between occupancy and abundance was nonlinear and depended on landscape, population size, and movement parameters. With current estimates for population size and detection probability in the northern U.S. Rockies, most sampling schemes were only able to detect large declines in abundance in the simulations (i.e., 50% decline over 10 years). For small populations reestablishing in the Southern Rockies, occupancy‐based methods had enough power to detect population trends only when populations were increasing dramatically (e.g., doubling or tripling in 10 years), regardless of sampling effort. In general, increasing the number of cells sampled or the per‐visit detection probability had a much greater effect on power than the number of visits conducted during a survey. Although our results are specific to wolverines, this approach could easily be adapted to other territorial species. Poder de Análisis Espacialmente Explícito para el Monitoreo Basado en Ocupación del Glotón (Gulo gulo) en las Montañas Rocallosas de Estados Unidos     

Advantages of Volunteer-Based Biodiversity Monitoring in Europe

Abstract:  Without robust and unbiased systems for monitoring, changes in natural systems will remain enigmatic for policy makers, leaving them without a clear idea of the consequences of any environmental policies they might adopt. Generally, biodiversity-monitoring activities are not integrated or evaluated across any large geographic region. The EuMon project conducted the first large-scale evaluation of monitoring practices in Europe through an on-line questionnaire and is reporting on the results of this survey. In September 2007 the EuMon project had documented 395 monitoring schemes for species, which represents a total annual cost of about €4 million, involving more than 46,000 persons devoting over 148,000 person-days/year to biodiversity-monitoring activities. Here we focused on the analysis of variations of monitoring practices across a set of taxonomic groups (birds, amphibians and reptiles, mammals, butterflies, plants, and other insects) and across 5 European countries (France, Germany, Hungary, Lithuania, and Poland). Our results suggest that the overall sampling effort of a scheme is linked with the proportion of volunteers involved in that scheme. Because precision is a function of the number of monitored sites and the number of sites is maximized by volunteer involvement, our results do not support the common belief that volunteer-based schemes are too noisy to be informative. Just the opposite, we believe volunteer-based schemes provide relatively reliable data, with state-of-the-art survey designs or data-analysis methods, and consequently can yield unbiased results. Quality of data collected by volunteers is more likely determined by survey design, analytical methodology, and communication skills within the schemes rather than by volunteer involvement per se.     

Techniques and Guidelines for Monitoring Neotropical Butterflies

Long-term monitoring of selected species can identify changes in biological diversity, permitting the timely adjustment of management activities to reverse or avoid undesired trends. This paper addresses several related issues bearing on the development of inexpensive and easily implemented monitoring programs for tropical butterflies. First, we discuss the use of butterflies as ecological indicators. Next, we present field evaluations of butterfly sampling techniques, indicating that: (1) light-gap size greatly affects sampling results in forests and should be of critical concern in site selection and sampling design; (2) baited traps and visual censuses provide complementary data on butterfly abundances; (3) monitoring a subset of locally common butterfly species can provide data for comparing community composition and relative abundance of species in areas where species inventories are incomplete. Drawing on these results, we develop guidelines for designing monitoring programs. These address the formulation of explicit questions to be addressed through monitoring and the selection of appropriate study sites, study species, sampling techniques, and sampling frequency. A protocol for the ongoing butterfly monitoring program that emerged from these studies is appended. The techniques and guidelines presented here are intended to serve as an adaptable model for biologists designing monitoring projects to help guide applied conservation efforts in the tropics.     

Predicting species distributions from samples collected along roadsides

Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.     

Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species’ sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human‐dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.     

Contributions of paraecologists and parataxonomists to research,conservation, and social development

Citizen science has been gaining momentum in the United States and Europe, where citizens are literate and often interested in science. However, in developing countries, which have a dire need for environmental data, such programs are slow to emerge, despite the large and untapped human resources in close proximity to areas of high biodiversity and poorly known floras and faunas. Thus, we propose that the parataxonomist and paraecologist approach, which originates from citizen‐based science, is well suited to rural areas in developing countries. Being a paraecologist or a parataxonomist is a vocation and entails full‐time employment underpinned by extensive training, whereas citizen science involves the temporary engagement of volunteers. Both approaches have their merits depending on the context and objectives of the research. We examined 4 ongoing paraecologist or parataxonomist programs in Costa Rica, India, Papua New Guinea, and southern Africa and compared their origins, long‐term objectives, implementation strategies, activities, key challenges, achievements, and implications for resident communities. The programs supported ongoing research on biodiversity assessment, monitoring, and management, and participants engaged in non‐academic capacity development in these fields. The programs in Southern Africa related to specific projects, whereas the programs in Costa Rica, India, and Papua New Guinea were designed for the long term, provided sufficient funding was available. The main focus of the paraecologists’ and parataxonomists’ activities ranged from collection and processing of specimens (Costa Rica and Papua New Guinea) or of socioeconomic and natural science data (India and Southern Africa) to communication between scientists and residents (India and Southern Africa). As members of both the local land user and research communities, paraecologists and parataxonomists can greatly improve the flow of biodiversity information to all users, from local stakeholders to international academia.     

A quantitative global review of species population monitoring

Species monitoring, defined here as the repeated, systematic collection of data to detect long-term changes in the populations of wild species, is a vital component of conservation practice and policy. We created a database of nearly 1200 schemes, ranging in start date from 1800 to 2018, to review spatial, temporal, taxonomic, and methodological patterns in global species monitoring. We identified monitoring schemes through standardized web searches, an online survey of stakeholders, in-depth national searches in a sample of countries, and a review of global biodiversity databases. We estimated the total global number of monitoring schemes operating at 3300–15,000. Since 2000, there has been a sharp increase in the number of new schemes being initiated in lower- and middle-income countries and in megadiverse countries, but a decrease in high-income countries. The total number of monitoring schemes in a country and its per capita gross domestic product were strongly, positively correlated. Schemes that were active in 2018 had been running for an average of 21 years in high-income countries, compared with 13 years in middle-income countries and 10 years in low-income countries. In high-income countries, over one-half of monitoring schemes received government funding, but this was less than one-quarter in low-income countries. Data collection was undertaken partly or wholly by volunteers in 37% of schemes, and such schemes covered significantly more sites and species than those undertaken by professionals alone. Birds were by far the most widely monitored taxonomic group, accounting for around half of all schemes, but this bias declined over time. Monitoring in most taxonomic groups remains sparse and uncoordinated, and most of the data generated are elusive and unlikely to feed into wider biodiversity conservation processes. These shortcomings could be addressed by, for example, creating an open global meta-database of biodiversity monitoring schemes and enhancing capacity for species monitoring in countries with high biodiversity. Article impact statement: Species population monitoring for conservation purposes remains strongly biased toward a few vertebrate taxa in wealthier countries.     

Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data

The European Union's Natura 2000 (N2000) is among the largest international networks of protected areas. One of its aims is to secure the status of a predetermined set of (targeted) bird and butterfly species. However, nontarget species may also benefit from N2000. We evaluated how the terrestrial component of this network affects the abundance of nontargeted, more common bird and butterfly species based on data from long-term volunteer-based monitoring programs in 9602 sites for birds and 2001 sites for butterflies. In almost half of the 155 bird species assessed, and particularly among woodland specialists, abundance increased (slope estimates ranged from 0.101 [SD 0.042] to 3.51 [SD 1.30]) as the proportion of landscape covered by N2000 sites increased. This positive relationship existed for 27 of the 104 butterfly species (estimates ranged from 0.382 [SD 0.163] to 4.28 [SD 0.768]), although most butterflies were generalists. For most species, when land-cover covariates were accounted for these positive relationships were not evident, meaning land cover may be a determinant of positive effects of the N2000 network. The increase in abundance as N2000 coverage increased correlated with the specialization index for birds, but not for butterflies. Although the N2000 network supports high abundance of a large spectrum of species, the low number of specialist butterflies with a positive association with the N2000 network shows the need to improve the habitat quality of N2000 sites that could harbor open-land butterfly specialists. For a better understanding of the processes involved, we advocate for standardized collection of data at N2000 sites.     

Use of Large‐Scale Acoustic Monitoring to Assess Anthropogenic Pressures on Orthoptera Communities

Biodiversity monitoring at large spatial and temporal scales is greatly needed in the context of global changes. Although insects are a species‐rich group and are important for ecosystem functioning, they have been largely neglected in conservation studies and policies, mainly due to technical and methodological constraints. Sound detection, a nondestructive method, is easily applied within a citizen‐science framework and could be an interesting solution for insect monitoring. However, it has not yet been tested at a large scale. We assessed the value of a citizen‐science program in which Orthoptera species (Tettigoniidae) were monitored acoustically along roads. We used Bayesian model‐averaging analyses to test whether we could detect widely known patterns of anthropogenic effects on insects, such as the negative effects of urbanization or intensive agriculture on Orthoptera populations and communities. We also examined site‐abundance correlations between years and estimated the biases in species detection to evaluate and improve the protocol. Urbanization and intensive agricultural landscapes negatively affected Orthoptera species richness, diversity, and abundance. This finding is consistent with results of previous studies of Orthoptera, vertebrates, carabids, and butterflies. The average mass of communities decreased as urbanization increased. The dispersal ability of communities increased as the percentage of agricultural land and, to a lesser extent, urban area increased. Despite changes in abundances over time, we found significant correlations between yearly abundances. We identified biases linked to the protocol (e.g., car speed or temperature) that can be accounted for ease in analyses. We argue that acoustic monitoring of Orthoptera along roads offers several advantages for assessing Orthoptera biodiversity at large spatial and temporal extents, particularly in a citizen science framework. El Uso de Monitoreos Acústicos a Gran Escala para Estudiar las Presiones Antropogénicas sobre Comunidades de Orthoptera     

Conservation and monitoring of a persecuted African lion population by Maasai warriors

Although Africa has many threatened species and biological hot spots, there are few citizen science schemes, particularly in rural communities, and there has been limited evaluation of existing programs. We engaged traditional Maasai warriors (pastoralist men aged 15 to 35) in community‐based conservation and demographic monitoring of a persecuted African lion (Panthera leo) population. Through direct engagement, we investigated whether a citizen science approach employing local warriors, who had no formal education, could produce reliable data on the demographics, predation, and movements of a species with which their communities have been in conflict for generations. Warriors were given benefits such as literacy training and skill enhancement and engaged in the monitoring of the lions. The trained warriors reported on lion sign across an area nearly 4000 km². Scientists worked together with the warriors to verify their reports and gather observations on the lion population. Using the verified reports and collected observations, we examined our scientific knowledge relative to the lion population preceding and during the citizen science program. Our observations showed that data quality and quantity improved with the involvement and training of the participants. Furthermore, because they engaged in conservation and gained personal benefits, the participants came to appreciate a species that was traditionally their foe. We believe engaging other local communities in biodiversity conservation and monitoring may be an effective conservation approach in rural Africa.     

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.