Equivalence of citizen science and scientific data for modelling species distribution of birds from a tropical savanna

The Wallacean deficit continues to be a challenge to species distribution modelling. Although some authors have suggested that data collected by citizen scientists can be relevant for a better understanding of biodiversity, to our knowledge, no work has quantitatively tested the equivalence between scientific and citizen science data. Here, we investigate the hypothesis that data collected by citizen scientists can be equivalent to data collected by professional scientists when generating species spatial distribution models. For 42 bird species in the Cerrado region we generated and compared species distribution models based on three data sources: (1) scientific data, (2) citizen science data and (3) sample size corrected citizen science data. To test our hypothesis, we compared the equivalence of these datasets. We rejected the hypothesis of equivalence for about one-third (38%) of the evaluated species, revealing that, for most of the species considered, the models generated were equivalent irrespective of the data set used. The distances between centroids of the models that were equivalent were on average smaller than the distances between non-equivalent models. Also, the direction of change in the models showed no pattern, with no trend towards more populated regions. Our results show that the use of data collected by citizen scientists can be an ally in filling the Wallacean deficit gap. In fact, the lack of use of this wide range of data collected by citizen scientists seems to be an unjustified caution. We indicate the potential of using citizen science data for modelling the distribution of species, mainly due to the large set of data collected, which is impracticable for scientists alone to collect. Conservation measures will be favoured by the union of professional and amateur data, aiming for a better understanding of species distribution and, consequently, biodiversity conservation.     

Improving citizen science data for long-term monitoring of plant species in the Netherlands

Biodiversity and Conservation - In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied...     

How citizen science could improve species distribution models and their independent assessment

1. Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.
2. We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.
3. Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.
4. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer''s participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

     

Correcting for bias in distribution modelling for rare species using citizen science data

Aim

To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine‐learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data.

Innovation

Modelling rare species’ distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non‐detections (i.e. class imbalance). Citizen science data can provide a cost‐effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under‐sample the majority class (non‐detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under‐sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).

Main Conclusions

Spatial under‐sampling increased the accuracy of each model and outperformed the approach typically used to direct under‐sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

     

New conservation opportunities: Using citizen science in monitoring non-native species in Neotropical region

The combination of highly equipped smartphones, with the increased use of social media, has offered a wide database. Given this, citizen science can be used to record and monitor non-native fish fauna, target new samples and collaborate with monitoring occurrences in new areas. We aimed to demonstrate the efficiency of social media in citizen science as a tool to cooperate with monitoring studies of non-native species. Consequently, we determined the occurrence points of S. brasiliensis in the Iguaçu River basin, indicating sites of greatest occurrence and analyzing the impact of the invasion on the native fauna of the basin. Files and information available on the YouTube^® and Facebook^® media platforms were used as data, was carried out from April 2019 to April 2020. The results were 40 records, 22 videos obtained from Youtube, and seven videos and 11 photos from Facebook, the oldest record was from April 2013, while the largest number of posts was in 2016. Fish records available from online platforms can reveal the occurrence and progressive dispersion of species, in the context of biological invasions, these tools can be of great value in studies that aim to follow the progress of introduced species, contributing by helping to direct new sampling programs and corroborating the occurrence of species in new areas in conjunction with standard monitoring programs. Based on citizen science records, it was possible to update the range of occurrence of the non-native S. brasiliensis in the Iguaçu River basin, cooperating with scientific knowledge. Innovative monitoring and control measures are necessary to deal with invasive species, with citizen science proving to be competent for determining the occurrence of species and showing promise in the entire field of ichthyology.     

Using citizen science data to inform the relative sensitivity of waterbirds to natural versus human‐dominated landscapes in China

Habitat loss is widely regarded as one of the most destructive factors threatening native biodiversity. Because migratory waterbirds include some of the most globally endangered species, information on their sensitivity to landscape would benefit their conservation. While citizen science data on waterbird species occurrence are subjected to various biases, their appropriate interpretation can provide information of benefit to species conservation. We apply a bootstrapping procedure to citizen science data to reduce sampling biases and report the relative sensitivity of waterbird species to natural versus human‐dominated landscapes. Analyses are performed on 30,491 data records for 69 waterbird species referred to five functional groups observed in China between 2000 and 2018. Of these taxa, 30 species (43.5%) are significantly associated with natural landscapes, more so for cranes, geese, and ducks than for shorebirds and herons. The relationship between land association and the threat status of waterbirds is significant when the range size of species is considered as the mediator, and the higher the land association, the higher the threat status. Sensitive species significantly associated with natural landscapes are eight times more likely to be classified as National Protected Species (NPS) Classes I or II than less sensitive species significantly associated with human‐dominated landscapes. We demonstrate the potential for citizen science data to assist in conservation planning in the context of landscape changes. Our methods might assist others to obtain information to help relieve species decline and extinction.     

Diffuse migratory connectivity in two species of shrubland birds: evidence from stable isotopes

Connecting seasonal ranges of migratory birds is important for understanding the annual template of stressors that influence their populations. Brewer’s sparrows (Spizella breweri) and sagebrush sparrows (Artemisiospiza nevadensis) share similar sagebrush (Artemisia spp.) habitats for breeding but have different population trends that might be related to winter location. To link breeding and winter ranges, we created isoscapes of deuterium [stable isotope ratio (δ) of deuterium; δ ²H] and nitrogen (δ ¹⁵N) for each species modeled from isotope ratios measured in feathers of 264 Brewer’s and 82 sagebrush sparrows and environmental characteristics at capture locations across their breeding range. We then used feather $\delta^{2} {\text{H}}_{\text{f}}$ and $\delta^{15} {\text{N}}_{\text{f}}$ measured in 1,029 Brewer’s and 527 sagebrush sparrows captured on winter locations in southwestern United States to assign probable breeding ranges. Intraspecies population mixing from across the breeding range was strong for both Brewer’s and sagebrush sparrows on winter ranges. Brewer’s sparrows but not sagebrush sparrows were linked to more northerly breeding locations in the eastern part of their winter range. Winter location was not related to breeding population trends estimated from US Geological Survey Breeding Bird Survey routes for either Brewer’s or sagebrush sparrows. Primary drivers of population dynamics are likely independent for each species; Brewer’s and sagebrush sparrows captured at the same winter location did not share predicted breeding locations or population trends. The diffuse migratory connectivity displayed by Brewer’s and sagebrush sparrows measured at the coarse spatial resolution in our analysis also suggests that local environments rather than broad regional characteristics are primary drivers of annual population trends.     

Evaluating citizen science data for forecasting species responses to national forest management

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.     

Climate effects on the distribution of wetland habitats and connectivity in networks of migratory waterbirds

The establishment and maintenance of conservation areas are among the most common measures to mitigate the loss of biodiversity. However, recent advances in conservation biology have challenged the reliability of such areas to cope with variation in climate conditions. Climate change can reshuffle the geographic distribution of species, but in many cases suitable habitats become scarce or unavailable, limiting the ability to migrate or adapt in response to modified environments. In this respect, the extent to which existing protected areas are able to compensate changes in habitat conditions to ensure the persistence of species still remains unclear. We used a spatially explicit model to measure the effects of climate change on the potential distribution of wetland habitats and connectivity of Natura 2000 sites in Italy. The effects of climate change were measured on the potential for water accumulation in a given site, as a surrogate measure for the persistence of aquatic ecosystems and their associated migratory waterbirds. Climate impacts followed a geographic trend, changing the distribution of suitable habitats for migrants and highlighting a latitudinal threshold beyond which the connectivity reaches a sudden collapse. Our findings show the relative poor reliability of most sites in dealing with changing habitat conditions and ensure the long-term connectivity, with possible consequences for the persistence of species. Although alterations of climate suitability and habitat destruction could impact critical areas for migratory waterbirds, more research is needed to evaluate all possible long-term effects on the connectivity of migratory networks.     

Using species niche models to inform strategic management of weeds in a changing climate

The expansion of the global area planted in fast-growing forest species seems likely as a means of offsetting carbon dioxide emissions and developing a sustainable bio-energy resource. Selecting appropriate sites for these plantations will require consideration of the effect of climate change on plantation growth and risks from abiotic and biotic factors. Buddleja davidii has been identified as a weed that has a major impact on plantation forest production in New Zealand. While it is at present restricted mainly to the North Island, a large proportion of the area identified for forest expansion is in eastern and southern regions of the South Island where the weed is presently relatively scarce. In this study we use a process-oriented climatic niche model (CLIMEX) to identify climatically suitable areas for B. davidii under current climate and future climate during the 2080s. This analysis indicates areas most at risk from invasion by B. davidii are in eastern and southern regions of the South Island. As B. davidii predominantly colonises disturbed areas, the likely increases in plantation forest area within this region can be expected to promote the spread of B. davidii. Strategies that could be implemented to manage B. davidii in this region are discussed. This study highlights the general utility of process-oriented niche models in identifying possible threats to planned primary production activities from invasive weed species. This type of knowledge is invaluable in planning and allocation of often scarce resources to most effectively control high impact weeds. Without the synoptic view of the invasion and the assets at risk, there is a strong potential for regional pest management to be parochial, and consequently less effective at all scales.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.     

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.     

Using retrospective health data from the Gombe chimpanzee study to inform future monitoring efforts

Disease outbreaks, either in isolation or in concert with other risk factors, can pose serious threats to the long-term persistence of mammal populations, and these risks become elevated as population size decreases and/or population isolation increases. Many chimpanzee study sites are increasingly isolated by loss of habitat due to human encroachment, and managers of parks that contain chimpanzees perceive that disease outbreaks have been and continue to be significant causes of mortality for chimpanzees. Major epidemics at Gombe National Park include suspected polio in 1966; respiratory diseases in 1968, 1987, 1996, 2000, and 2002; and sarcoptic mange in 1997. These outbreaks have led park managers and researchers working in Gombe to conclude that disease poses a substantial risk to the long-term survival of Gombe's chimpanzee population. We surveyed behavioral data records spanning 44 years for health-related data and found a combination of standardized and nonstandardized data for the entire period. Here we present the types of data found during the survey, discuss the usefulness of these data in the context of risk assessment, and describe how our current monitoring effort at Gombe was designed based on our findings.