Biodiversity data obsolescence and land uses changes

Open Data Practices among Users of Primary Biodiversity Data

Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.

Estimates of Species Richness and Composition Depend on Detection Method in Assemblages of Terrestrial Mammals

Detecting rapid changes in mammal composition at large spatial scales requires efficient detection methods. Many studies estimate species composition with a single survey method without asking whether that particular method optimises detection for all occurring species and yields reliable community-level indices. We explore the implications of between-method differences in efficiency, consistency, and sampling effort for the basic characterisation of assemblages of medium to large mammals in a region with three contrasted Mediterranean landscapes. We assessed differences between camera traps, scent stations, scat surveys, and track surveys. Using track surveys, we detected all species present in the regional pool (13) and obtained the most accurate description of local species richness and composition with the lowest sampling effort (16 sampling units and 2 survey sessions at most). Had we chosen camera traps, scent stations, or scat surveys as the only survey method, we would have underestimated species richness (9, 11, and 12 species, respectively) and misrepresented species composition in varying degrees. Preliminary studies of method performance inform whether single or multiple survey methods are needed and eventually which single method might be most appropriate. Without such a formal assessment current practices may produce unreliable and incomplete species inventories, ultimately leading to incorrect conclusions about the impact of human activity on mammal communities.

Research applications of primary biodiversity databases in the digital age

Our world is in the midst of unprecedented change-climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.

Tyto furcata (Tytonidae: Strigiformes) pellets: tools to access the richness of small mammals of a poorly known Caatinga area in northeast Brazil

Owls are efficient predators and are widely distributed around the globe. Remains of undigested prey is regurgitated by these birds in the form of pellets, and these are a valuable source of information about prey communities and the diet of owls. In this study, the composition of mammals present in the diet of owls that inhabit different caves was evaluated through the analysis of their pellets. We found 373 pellets from seven caves, and small mammals composed at least 80% of the diet of these birds in all caves. The mammal composition found in the pellets showed a richness of 26 distinct taxa including 12 rodents, three marsupials and 11 bats. In this work, we highlight the richness of small mammals in a poorly known Caatinga area. We also highlight the importance of morphology and taxonomy in supporting this kind of research, which relies upon vertebrate parts as its source of information for identification. Finally, we reiterate the efficiency of the study of owl pellets as a rapid approach for assessing local mammal richness and as a complementary method in studies of diversity and conservation.

Completeness of Digital Accessible Knowledge (DAK) about terrestrial mammals in the Iberian Peninsula

The advent of online data aggregator infrastructures has facilitated the accumulation of Digital Accessible Knowledge (DAK) about biodiversity. Despite the vast amount of freely available data records, their usefulness for research depends on completeness of each body of data regarding their spatial, temporal and taxonomic coverage. In this paper, we assess the completeness of DAK about terrestrial mammals distributed across the Iberian Peninsula. We compiled a dataset with all records about mammals occurring in the Iberian Peninsula available in the Global Biodiversity Information Facility and in the national atlases from Portugal and Spain. After cleaning the dataset of errors as well as records lacking collection dates or not determined to species level, we assigned all occurrences to a 10-km grid. We assessed inventory completeness by calculating the ratio between observed and expected richness (based on the Chao2 richness index) in each grid cell and classified cells as well-sampled or under-sampled. We evaluated survey coverage of well-sampled cells along four environmental gradients and temporal coverage. Out of 796,283 retrieved records, quality issues led us to remove 616,141 records unfit for this use. The main reason for discarding records was missing collection dates. Only 25.95% cells contained enough records to robustly estimate completeness. The DAK about terrestrial mammals from the Iberian Peninsula was low, and spatially and temporally biased. Out of 5,874 cells holding data, only 620 (9.95%) were classified as well-sampled. Moreover, well-sampled cells were geographically aggregated and reached inventory completeness over the same temporal range. Despite the increasing availability of DAK, its usefulness is still compromised by quality issues and gaps in data. Future work should therefore focus on increasing data quality, in addition to mobilizing unpublished data.

Data Leakage and Loss in Biodiversity Informatics

The field of biodiversity informatics is in a massive, "grow-out" phase of creating and enabling large-scale biodiversity data resources. Because perhaps 90% of existing biodiversity data nonetheless remains unavailable for science and policy applications, the question arises as to how these existing and available data records can be mobilized most efficiently and effectively. This situation led to our analysis of several large-scale biodiversity datasets regarding birds and plants, detecting information gaps and documenting data "leakage" or attrition, in terms of data on taxon, time, and place, in each data record. We documented significant data leakage in each data dimension in each dataset. That is, significant numbers of data records are lacking crucial information in terms of taxon, time, and/or place; information on place was consistently the least complete, such that geographic referencing presently represents the most significant factor in degradation of usability of information from biodiversity information resources. Although the full process of digital capture, quality control, and enrichment is important to developing a complete digital record of existing biodiversity information, payoffs in terms of immediate data usability will be greatest with attention paid to the georeferencing challenge.

Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential

Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms-neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist-for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.

Taxonomic bias in biodiversity data and societal preferences

Studying and protecting each and every living species on Earth is a major challenge of the 21^st century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic bias continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrences from the Global Biodiversity Information Facility (GBIF), the biggest biodiversity data portal, to characterize the taxonomic bias in biodiversity data. We also investigated how societal preferences and taxonomic research relate to biodiversity data gathering. For each species belonging to 24 taxonomic classes, we used the number of publications from Web of Science and the number of web pages from Bing searches to approximate research activity and societal preferences. Our results show that societal preferences, rather than research activity, strongly correlate with taxonomic bias, which lead us to assert that scientists should advertise less charismatic species and develop societal initiatives (e.g. citizen science) that specifically target neglected organisms. Ensuring that biodiversity is representatively sampled while this is still possible is an urgent prerequisite for achieving efficient conservation plans and a global understanding of our surrounding environment.

Temporal degradation of data limits biodiversity research

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems—such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets.