Can opportunistically collected Citizen Science data fill a data gap for habitat suitability models of less common species?

Pollinator activity and flowering in agricultural weeds in Sweden

The extent to which weeds in arable land are useful to pollinators depends in part on the temporal pattern of flowering and insect flight activity. We compiled citizen science data on 54 bees and hoverflies typical of agricultural areas in southern Sweden, as well as 24 flowering weed species classified as pollinator-friendly in the sense that they provide nectar and/or pollen to pollinators. The flight periods of the bees and hoverflies varied greatly, but there were also some consistent differences between the four groups studied. The first group to fly were the early flying solitary bees (7 species), followed by the social bees (18 species). In contrast, other solitary bees (11 species) and hoverflies (22 species) flew later in the summer. Solitary bees had the shortest flight periods, while social bees and hoverflies had longer flight periods. Flowering of weed species also varied greatly between species, with weeds classified as winter annuals (e.g., germinating in autumn) starting early together with germination generalists (species that can germinate in both autumn and spring). Summer annuals (spring germinators) and perennials started flowering about a month later. Germination generalists had a much longer flowering period than the others. Weekly pollinator records were in most cases significantly explained by weed records. Apart from early flying solitary bees, all models showed strong positive relationships. The overall best explanatory variable was the total number of weeds, with a weight assigned to each species based on its potential as a nectar/pollen source. This suggests that agricultural weeds in Sweden provide a continuous potential supply of nectar and pollen throughout the flight season of most pollinators.

Illuminating patterns of firefly abundance using citizen science data and machine learning models

As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera: Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports suggest that some firefly populations have declined in recent decades. Researchers have hypothesized that North American firefly populations are most threatened by habitat loss, pesticide use, and light pollution, but the importance of these factors in shaping firefly populations has not been rigorously examined at broad spatial scales. Using data from >24,000 surveys (spanning 2008-16) from the citizen science program Firefly Watch, we trained machine learning models to evaluate the relative importance of a variety of factors on bioluminescent firefly populations: pesticides, artificial lights at night, land cover, soil/topography, short-term weather, and long-term climate. Our analyses revealed that firefly abundance was driven by complex interactions among soil conditions (e.g., percent sand composition), climate/weather (e.g., growing degree days), and land cover characteristics (e.g., percent agriculture and impervious cover). Given the significant impact that climactic and weather conditions have on firefly abundance, there is a strong likelihood that firefly populations will be influenced by climate change, with some regions becoming higher quality and supporting larger firefly populations, and others potentially losing populations altogether. Collectively, our results support hypotheses related to factors threatening firefly populations, especially habitat loss, and suggest that climate change may pose a greater threat than appreciated in previous assessments. Thus, future conservation of North American firefly populations will depend upon 1) consistent and continued monitoring of populations via programs like Firefly Watch, 2) efforts to mitigate the impacts of climate change, and 3) insect-friendly conservation practices.

A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring

To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.

Citizen Science Meet South American Tachinids: New Records of Feather-Legged Fly Trichopoda (Galactomyia) pictipennis Bigot (Diptera: Tachinidae) from Chile

The species of Gymnosomatini (Diptera: Tachinidae) are specialized to attack stink bugs (Hemiptera: Pentatomidae). Some of these flies, such as those in the genus Trichopoda Berthold, are conspicuous and represent target species to study with citizen science. Here we report for the first time the distribution range of T. (Galactomyia) pictipennis Bigot, a newly introduced biological control species of tachinid for use against stink bugs in Chile using data obtained through the citizen science method. Additionally, we update the distribution of T. arcuata (Bigot), a native biological control species of tachinid, and assess the grade of overlap between the flies and their main stink bug hosts. We obtained data regarding the occurrence of flies and stink bugs from the citizen science program "Moscas Florícolas de Chile," from Facebook groups, and from iNaturalist. We standardized our data to avoid temporal, spatial, and geographic biases. We report the extent of occurrence (EOO) and area of occupancy (AOO) for each fly and stink bug species. The EOOs for T. pictipennis and T. arcuata are 85,474 km² and 20,647 km² through central Chile ecosystems, respectively. The EOO of T. arcuata is overlapped by 53% with the introduced species. Trichopoda pictipennis has a high percentage of overlap with the EOO of Nezara viridula Linnaeus (61%), whereas T. arcuata has low percentages of overlap with different native Acledra species and with N. viridula. We discuss the potential of citizen science to detect (1) areas with higher diversity or gaps of occurrences, (2) new biological control agents, (3) prey records, (4) negative impacts on non-target species, (5) changes in composition at long-term, and (6) areas to promote conservation biological control in agricultural landscapes.

Joint survival modelling for multiple species exposed to toxicants

In environmental risk assessment (ERA), the multitude of compounds and taxa demands cross-species extrapolation to cover the variability in sensitivity to toxicants. However, only the impact of a single compound to a single species is addressed by the general unified threshold model of survival (GUTS). The reduced GUTS is the recommended model to analyse lethal toxic effects in regulatory aquatic ERA. GUTS considers toxicokinetics and toxicodynamics. Two toxicodynamic approaches are considered: Stochastic death (SD) assumes that survival decreases with an increasing internalized amount of the toxicant. Individual tolerance (IT) assumes that individuals vary in their tolerance to toxic exposure. Existing theory suggests that the product of the threshold z_w and killing rate b_w (both SD toxicodynamic parameters) are constant across species or compounds if receptors and target sites are shared. We extend that theory and show that the shape parameter β of the loglogistic threshold distribution in IT is also constant. To verify the predicted relationships, we conducted three tests using toxicity studies for eight arthropods exposed to the insecticide flupyradifurone. We confirmed previous verifications of the relation- between SD parameters, and the newly established relation for the IT parameter β. We enhanced GUTS to jointly model survival for multiple species with shared receptors and pathways by incorporating the relations among toxicodynamic parameters described above. The joint GUTS exploits the shared parameter relations and therefore constrains parameter uncertainty for each of the separate species. Particularly for IT, the joint GUTS more precisely predicted risk to the separate species than the standard single species GUTS under environmentally realistic exposure. We suggest that joint GUTS modelling can improve cross-species extrapolation in regulatory ERA by increasing the reliability of risk estimates and reducing animal testing. Furthermore, the shared toxicodynamic response provides potential to reduce complexity of ecosystem models.

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.

Size and degree of protection of native forest remnants drive the local occupancy of an endangered neotropical primate

Although the species-area relationship is well known, it may interact with and be augmented or cancelled out by other factors, such as local human disturbance. We used data on site occupancy of the Endangered blonde capuchin monkey (Sapajus flavius) based primarily on a standardized program of local interviews to model the influence of past human disturbance on the occurrence of this species across remaining forest patches of northeastern Brazil within the Atlantic Forest and Caatinga biomes. To do so, we assessed environmental covariates that best represent the history of human impacts. We then used single-species occupancy models to assess site occupancy, while controlling for detection error during sampling. Surprisingly, we obtained a higher occupancy rate in the more arid Caatinga remnants than in the more mesic Atlantic Forest. Habitat patch size, history of site protection, and annual precipitation were the best predictors of local occupancy. Historical human disturbance, including subsistence hunting, has exerted considerable impact on the modern distribution of the blonde capuchin, whose geographic range largely spans a region historically lacking any wildlife protection. Matrix vegetation structure across the Caatinga, which so far has averted large-scale mechanized agriculture, also creates a benign landscape that likely benefits contemporary capuchin occupancy. Local extinctions of this endangered primate will most likely continue unabated unless a ban on hunting in remaining Atlantic Forest and Caatinga fragments can be enforced.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.

An integrated high-resolution mapping shows congruent biodiversity patterns of Fagales and Pinales

The documentation of biodiversity distribution through species range identification is crucial for macroecology, biogeography, conservation, and restoration. However, for plants, species range maps remain scarce and often inaccurate. We present a novel approach to map species ranges at a global scale, integrating polygon mapping and species distribution modelling (SDM). We develop a polygon mapping algorithm by considering distances and nestedness of occurrences. We further apply an SDM approach considering multiple modelling algorithms, complexity levels, and pseudo-absence selections to map the species at a high spatial resolution and intersect it with the generated polygons. We use this approach to construct range maps for all 1957 species of Fagales and Pinales with data compilated from multiple sources. We construct high-resolution global species richness maps of these important plant clades, and document diversity hotspots for both clades in southern and south-western China, Central America, and Borneo. We validate the approach with two representative genera, Quercus and Pinus, using previously published coarser range maps, and find good agreement. By efficiently producing high-resolution range maps, our mapping approach offers a new tool in the field of macroecology for studying global species distribution patterns and supporting ongoing conservation efforts.

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species

The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modeling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, that is, 96 × 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting-habitat suitability modeling, that is, the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas (PAs) was considerable, but our analysis also revealed many potentially high-quality forest stands outside PAs. However, many of these sites are increasingly threatened by logging because of increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and a corresponding approach would also be feasible and recommendable elsewhere where similar data are available.

Accounting for geographic variation in species-habitat associations during habitat suitability modeling

Range-wide species conservation efforts are facilitated by spatially explicit estimates of habitat suitability. However, species-environment relationships often vary geographically and models assuming geographically constant relationships may result in misleading inferences. We present the first range-wide habitat suitability model (HSM) for the federally threatened eastern indigo snake (Drymarchon couperi) as a case study illustrating an approach to account for known latitudinal variation in habitat associations. Specifically, we modeled habitat suitability using interactive relationships between minimum winter temperature and several a priori environmental covariates and compared our results to those from models assuming geographically constant relationships. We found that multi-scale models including interactive effects with winter temperature outperformed single-scale models and models not including interactive effects with winter temperature. Our top-ranked model had suitable range-wide predictive performance and identified numerous large (i.e., ≥1000 ha) potential habitat patches throughout the indigo snake range. Predictive performance was greatest in southern Georgia and northern Florida likely reflecting more restrictive indigo snake habitat associations in these regions. This study illustrates how modeling interactive effects between temperature and environmental covariates can improve the performance of HSMs across geographically varying environmental gradients.

Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization

Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.

Citizen Science Data Collection for Integrated Wildlife Population Analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

Buzzing Homes: Using Citizen Science Data to Explore the Effects of Urbanization on Indoor Mosquito Communities

Urbanization has been associated with a loss of overall biodiversity and a simultaneous increase in the abundance of a few species that thrive in urban habitats, such as highly adaptable mosquito vectors. To better understand how mosquito communities differ between levels of urbanization, we analyzed mosquito samples from inside private homes submitted to the citizen science project 'Mückenatlas'. Applying two urbanization indicators based on soil sealing and human population density, we compared species composition and diversity at, and preferences towards, different urbanization levels. Species composition between groups of lowest and highest levels of urbanization differed significantly, which was presumably caused by reduced species richness and the dominance of synanthropic mosquito species in urban areas. The genus Anopheles was frequently submitted from areas with a low degree of urbanization, Aedes with a moderate degree, and Culex and Culiseta with a high degree of urbanization. Making use of citizen science data, this first study of indoor mosquito diversity in Germany demonstrated a simplification of communities with increasing urbanization. The dominance of vector-competent species in urban areas poses a potential risk of epidemics of mosquito-borne diseases that can only be contained by a permanent monitoring of mosquitoes and by acquiring a deeper knowledge about how anthropogenic activities affect vector ecology.

The role of passive surveillance and citizen science in plant health

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through "passive surveillance". These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these "messy" unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers' probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

Cannot see the diversity for all the species: Evaluating inclusion criteria for local species lists when using abundant citizen science data

Abundant citizen science data on species occurrences are becoming increasingly available and enable identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands, we investigated the sensitivity of α- and β-diversity estimates to the use raw versus detection-corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested seven inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence-only observation data are abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based α- and β-diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: The large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.

Effects of future agricultural change scenarios on beneficial insects

Insects provide vital ecosystem services to agricultural systems in the form of pollination and natural pest control. However, there are currently widespread declines in the beneficial insects which deliver these services (i.e. pollinators and 'natural enemies' such as predators and parasitoids). Two key drivers of these declines have been the expansion of agricultural land and intensification of agricultural production. With an increasing human population requiring additional sources of food, further changes in agricultural land use appear inevitable. Identifying likely trajectories of change and predicting their impacts on beneficial insects provides a scientific basis for making informed decisions on the policies and practices of sustainable agriculture. We created spatially explicit, exploratory scenarios of potential changes in the extent and intensity of agricultural land use across Great Britain (GB). Scenarios covered 52 possible combinations of change in agricultural land cover (i.e. agricultural expansion or grassland restoration) and intensity (i.e. crop type and diversity). We then used these scenarios to predict impacts on beneficial insect species richness and several metrics of functional diversity at a 10km (hectad) resolution. Predictions were based on species distribution models derived from biological records, comprising data on 116 bee species (pollinators) and 81 predatory beetle species (natural enemies). We identified a wide range of possible consequences for beneficial insect species richness and functional diversity as result of future changes in agricultural extent and intensity. Current policies aimed at restoring semi-natural grassland should result in increases in the richness and functional diversity of both pollinators and natural enemies, even if agricultural practices remain intensive on cropped land (i.e. land-sparing). In contrast, any expansion of arable land is likely to be accompanied by widespread declines in richness of beneficial insects, even if cropping practices become less intensive (i.e. land-sharing), although effects of functional diversity are more mixed.

Expert-Informed Habitat Suitability Analysis for At-Risk Species Assessment and Conservation Planning

The U.S. Fish and Wildlife Service (USFWS) is responsible for reviewing the biological status of hundreds of species to determine federal status designations under the Endangered Species Act. The longleaf pine Pinus palustris ecological system supports many priority at-risk species designated for review, including five species of herpetofauna: gopher tortoise Gopherus polyphemus, southern hognose snake Heterodon simus, Florida pine snake Pituophis melanoleucus mugitus, gopher frog Lithobates (Rana) capito, and striped newt Notophthalmus perstriatus. To inform status decisions and conservation planning, we developed habitat suitability models to 1) identify habitat features that best predict species presence and 2) estimate the amount and distribution of suitable habitat across each species' range under current conditions. We incorporated expert judgment from federal, state, and other partners to capture variation in ecological settings across species' ranges, prioritize predictor variables to test in models, mitigate data limitations by informing the selection of pseudoabsence points, qualitatively evaluate model estimates, and improve the likelihood that experts will trust and use model predictions for conservation. Soil characteristics, land cover, and fire interval strongly influenced habitat suitability for all species. Suitable habitat was distributed on known species strongholds, as well as private lands without known species records. Between 4.7% (gopher frog) and 14.6% (gopher tortoise) of the area in a species' range was classified as suitable habitat, and between 28.1% (southern hognose snake) and 47.5% (gopher frog) of suitable habitat was located in patches larger than 1 km2 (100 ha) on publicly owned lands. By overlaying predictions for each species, we identified areas of suitable habitat for multiple species on protected and unprotected lands. These results have direct applications to management and conservation planning: partners can tailor site-level management based on attributes associated with high habitat suitability for species of concern; allocate survey effort in areas with suitable habitat but no known species records; and identify priority areas for management, land acquisitions, or other strategies based on the distribution of species records, suitable habitat, and land protection status. These results can aid regional partners in implementing effective conservation strategies and inform status designation decisions of the USFWS.

Remotely sensed forest understory density and nest predator occurrence interact to predict suitable breeding habitat and the occurrence of a resident boreal bird species

Habitat suitability models (HSM) based on remotely sensed data are useful tools in conservation work. However, they typically use species occurrence data rather than robust demographic variables, and their predictive power is rarely evaluated. These shortcomings can result in misleading guidance for conservation. Here, we develop and evaluate a HSM based on correlates of long-term breeding success of an open nest building boreal forest bird, the Siberian jay. In our study site in northern Sweden, nest failure of this permanent resident species is driven mainly by visually hunting corvids that are associated with human settlements. Parents rely on understory nesting cover as protection against these predators. Accordingly, our HSM includes a light detection and ranging (LiDAR) based metric of understory density around the nest and the distance of the nest to the closest human settlement to predict breeding success. It reveals that a high understory density 15-80 m around nests is associated with increased breeding success in territories close to settlements (<1.5 km). Farther away from human settlements breeding success is highest at nest sites with a more open understory providing a favorable warmer microclimate. We validated this HSM by comparing the predicted breeding success with landscape-wide census data on Siberian jay occurrence. The correlation between breeding success and occurrence was strong up to 40 km around the study site. However, the HSM appears to overestimate breeding success in regions with a milder climate and therefore higher corvid numbers. Our findings suggest that maintaining patches of small diameter trees may provide a cost-effective way to restore the breeding habitat for Siberian jays up to 1.5 km from human settlements. This distance is expected to increase in the warmer, southern, and coastal range of the Siberian jay where the presence of other corvids is to a lesser extent restricted to settlements.

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

A practical guide for combining data to model species distributions

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.