Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research.

The snow leopard Panthera uncia is an elusive species inhabiting some of the most remote and inaccessible tracts of Central and South Asia. It is difficult to determine its distribution and density pattern, which are crucial for developing conservation strategies. Several techniques for species detection combining camera traps with remote sensing and geographic information systems have been developed to model the habitat of such cryptic and low-density species in challenging terrains. Utilising presence-only data from camera traps and direct observations, alongside six environmental variables (elevation, aspect, ruggedness, distance to water, land cover, and prey habitat suitability), we assessed snow leopard habitat suitability across Ladakh in northern India. This is the first study to model snow leopard distribution both in India and utilising direct observation data. Results suggested that elevation and ruggedness are the two most influential environmental variables for snow leopard habitat suitability, with highly suitable habitat having an elevation range of 2,800 m to 4,600 m and ruggedness of 450 m to 1,800 m. Our habitat suitability map estimated approximately 12% of Ladakh's geographical area (c. 90,000 km2) as highly suitable and 18% as medium suitability. We found that 62.5% of recorded livestock depredation along with over half of all livestock corrals (54%) and homestays (58%) occurred within highly suitable snow leopard habitat. Our habitat suitability model can be used to assist in allocation of conservation resources by targeting construction of livestock corrals to areas of high habitat suitability and promoting ecotourism programs in villages in highly suitable snow leopard habitat.

/pdf/modelling-potential-habitat-for-snow-leopards-panthera-uncia-53oran6ws9.pdf

Modelling potential habitat for snow leopards (Panthera uncia) in Ladakh, India

https://dspace.stir.ac.uk/bitstream/1893/29181/1/Improving%20reintroduction%20success%20in%20large%20carnivores%20through%20individual-based%20modelling-%20how%20to%20reintroduce%20Eurasian%20lynx%20%28Lynx%20lynx%29%20to%20Scotland.pdf

Improving reintroduction success in large carnivores through individual-based modelling: how to reintroduce Eurasian lynx (Lynx lynx) to Scotland

Habitat selection models for woodland species usually consider forest attributes derived from land cover maps, such as an area covered by forests or distance to forest edge, but canopy cover is less frequently used as a predictor. We evaluated the effect of including forest canopy cover data in habitat selection models for woodland species using the endangered Iberian lynx as a case study. We considered four canopy cover classes for both conifer and broadleaved forests, together with other land cover predictors, and analyzed a large telemetry (GPS-GSM collars) dataset. The model that included forest canopy cover outperformed, both in predictive power and in conservation management insights, the model that only used forest area. The effect of forest canopy cover on lynx habitat selection was especially strong for broadleaved forests; an increased tree cover in these forests consistently translated into significantly lower selection probabilities. Open broadleaved forests were found to clearly be the most preferred habitat. By contrast, the habitat model without canopy cover data indicated that broadleaved forests were significantly less preferred than shrublands and mountain olive groves, which may mislead conservation management actions. We conclude that habitat selection models for woodland species should consider forest canopy cover as a relevant candidate predictor as it can capture important factors influencing habitat use, such as understory vegetation abundance or prey availability. Recent developments on remote sensing technologies will increase the availability of spatially continuous information on canopy cover, facilitating a more widespread use of vegetation structural variables in habitat modeling and hence improving the conservation planning value of these models.

The role of forest canopy cover in habitat selection: insights from the Iberian lynx

The estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient non-invasive sampling methods and robust statistical tools to estimate abundance, maintenance of field sampling is still subject to economic and logistic constraints. These can result in intentional or unintentional interruptions in sampling and cause gaps in data time series, posing a challenge to abundance estimation, and ultimately conservation and management decisions. We applied an open population spatial capture-recapture (OPSCR) model to simulations and a real case study to test the reliability of abundance inferences models to interruption in data collection. Using individual detections occurring over consecutive sampling occasions, OPSCR models allow the estimation of abundance from individual detection data while accounting for lack of demographic and geographic closure between occasions. First, we simulated sampling data with interruptions in field sampling of different lengths and timing. We checked the performance of an OPSCR model in deriving abundance for species with slow and intermediate life history strategies. Finally, we introduced artificial sampling interruptions of various magnitudes and timing to a five-year non-invasive monitoring data set of wolverines (Gulo gulo) in Norway and quantified the consequences for OPSCR model predictions. Inferences from OPSCR models were reliable even with temporal interruptions in monitoring. Interruption did not cause any systematic bias, but increased uncertainty. Interruptions occurring at occasions towards the beginning and the end of the sampling caused higher uncertainty. The loss in precision was more severe for species with a faster life history strategy. We provide a reliable framework to estimate abundance even in the presence of sampling interruptions. OPSCR allows monitoring studies to provide contiguous abundance estimates to managers, stakeholders, and policy makers even when data are non-contiguous. OPSCR models do not only help cope with unintentional interruptions during sampling but also offer opportunities for using intentional sampling interruptions during the design of cost-effective population surveys.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.3172

Estimating abundance with interruptions in data collection using open population spatial capture-recapture models

The Eurasian lynx is of special conservation concern based on the European Union's Habitat Directive and its populations need to be maintained or restored at favourable conservation status. To evaluate lynx population status, appropriate monitoring needs to be in place. We modelled the distribution dynamics of lynx in the Alps (200 000 km2) during 1994–2014 at a resolution of 100 km2. Lynx distribution and detection probability varied by year, country, forest cover, elevation and distance to the nearest release site. Occupancy of neighbouring quadrats had a strong positive effect on colonization and persistence rates. Our analyses demonstrate the importance of accounting for imperfect detection: the raw data underestimated the lynx range by 55% on average, depending on country and winter. Over the past 20 years the Alpine lynx range has expanded at an average rate of 4% per year, which was partly due to the lynx translocations to new areas. Our approach to large-scale distribution modelling and analysing trends using site occupancy models can be applied retrospectively and is useful in many cases where a network of trained people is established to report the presence of target species, for example, in Europe where member states of the European Union have to report conservation status of species of community interest. Hence, dynamic occupancy models are an appealing framework for inference about the large-scale range dynamics based on opportunistic data and a useful tool for large-scale management and conservation programmes.

https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1111/acv.12369

O. Müller

Papers

Mapping range dynamics from opportunistic data: spatiotemporal modelling of the lynx distribution in the Alps over 21 years