Biodiversity informatics

Understanding how deforestation and changes in habitat boundaries affect biodiversity is essential for developing conservation solutions. These topics are central to biology and ecology programs, where students learn to apply their knowledge in real-world conservation efforts. Higher education plays a crucial role in strengthening this understanding, particularly in life sciences programs. Given the complexity of ecological processes in altered landscapes, agent-based modeling provides an interactive and engaging way to simplify and visualize the effects of land use changes. In this study, we integrate Amazonian anurans, highly sensitive to temperature and humidity fluctuations, with an Agent-based model to simulate the impacts of deforestation, habitat restoration, and land abandonment on species survival and movement. Their ectothermic nature and dependence on pulmocutaneous respiration make them especially vulnerable to the drier and more variable conditions caused by deforestation. Integrating this model into conservation biology courses has enhanced learning by encouraging independent exploration, both in and out of the classroom. This tool, an agent-based model, is particularly suited for university-level ecology and conservation courses, and can also serve as an effective awareness tool in environmental education and decision-making workshops, highlighting the negative effects of human-made habitat changes on biodiversity.

Martín Otálora-Low

Nicolás Urbina-Cardona

Mauricio González-Méndez

Biodiversity Informatics

Enhancing Ecological Education: Utilizing Agent-Based Modeling to Simplify the Impacts of Deforestation on Amphibians

增强生态教育：利用基于代理的建模简化森林砍伐对两栖动物的影响

Biodiversity inventory in Kenya has been ongoing for about a century and a half, coinciding with the arrival of naturalists from Europe, America, and elsewhere outside Africa. Since the first collections in the mid-to-late 1800s, there has been a steady increase of plant surveys, frequency of inventory, and discovery of new species that have considerably increased knowledge of faunal and floristic elements. However, as in all other countries, such historical biological collection activities are more often than not, ad hoc, resulting in gaps in knowledge of species and their habitats. While Kenya is relatively rich botanically, with a succulent flora of about 428 taxa, it is apparent that the list is understated owing to, among other factors, difficulty of preparing herbarium material and restricted access to some sites. This study investigated completeness of geographic knowledge of succulent plants in Kenya, with the aim of establishing species distribution patterns and identifying gaps that will guide and justify priority setting for future work on the group. Species data were filtered from the general BRAHMS database at the East African Herbarium and cleaned via an iterative series of inspections and visualizations designed to detect and document inconsistencies in taxonomic concepts, geographic coordinates, and dates of collection. Eight grid squares fulfilled criteria for completeness of inventory: one in the city of Mombasa, one in the Kulal–Nyiro complex, one in Garissa, one in Baringo, and four grid squares in the Nairobi–Nakuru–Laikipia area. Poorly-known areas, mostly in the west, north, and north-eastern regions of the country, were extremely isolated from well-known sites, both geographically and environmentally. These localities should be prioritised for future inventory as they are likely to yield species new to science, species new to the national flora, and/or contribute new knowledge on habitats. To avoid inconsistencies and data leakage, biodiversity inventory and documentation needs streamlining to generate standardised metadata that should be digitised to enhance access and synthesis.

Emily Wabuyele

Simon Kang’ethe

Leonard E. Newton

Digital Knowledge of Kenyan Succulent Flora and Priorities for Future Inventory and Documentation

肯尼亚多肉植物数字知识及未来编目与记录的优先事项

Discovery of and access to primary biodiversity data are critical components in informed decision-making regarding sustainable use of biological resources and conservation of biodiversity. Primary biodiversity data are increasingly available from Benin, but information about completeness of this information across the country is still lacking for most groups. This study analyzed the Digital Accessible Knowledge regarding the plants of Benin to identify gaps in both geographic and environmental dimensions. Many gaps exist in plant data for Benin, particularly in the northern most departments; central and southern Benin are better known, but some gaps remain even there. The resulting view of Beninese Digital Accessible Knowledge can guide future inventory and data discovery efforts.

Jean Cossi Ganglo

Sunday Berlioz Kakpo

Completeness of Digital Accessible Knowledge of Plants of Benin and Priorities for Future Inventory and Data Discovery

贝宁植物数字可访问知识的完整性及未来编目和数据发现优先事项

贝宁植物数字可访问知识的完整性及未来编目与数据发现优先事项

Research efforts that synthesize historical and contemporary ecological data with modeling approaches improve our understanding of the complex response of species, communities, and landscapes to changing biophysical conditions through time and in space. Historical ecological data are particularly important in this respect. There are remaining barriers that limit such data synthesis, and technological improvements that make multiple diverse datasets more readily available for integration and synthesis are needed. This paper presents one case study of the Wieslander Vegetation Type Mapping project in California and highlights the importance of rescuing, digitizing and sharing historical datasets. We review the varied ecological uses of the historical collection: the vegetation maps have been used to understand legacies of land use change and plan for the future; the plot data have been used to examine changes to chaparral and forest communities around the state and to predict community structure and shifts under a changing climate; the photographs have been used to understand changing vegetation structure; and the voucher specimens in combination with other specimen collections have been used for large scale distribution modeling efforts. The digitization and sharing of the data via the web has broadened the scope and scale of the types of analysis performed. Yet, additional research avenues can be pursued using multiple types of VTM data, and by linking VTM data with contemporary data. The digital VTM collection is an example of a data infrastructure that expands the potential of large scale research through the integration and synthesis of data drawn from numerous data sources; its journey from analog to digital is a cautionary tale of the importance of finding historical data, digitizing it with best practices, linking it with other datasets, and sharing it with the research community.

Maggi Kelly

Rescuing and Sharing Historical Vegetation Data for Ecological Analysis: The California Vegetation Type Mapping Project

拯救与共享历史植被数据用于生态分析：加利福尼亚植被类型制图项目

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias—taxonomic, spatial, and temporal—as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou’s index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

Elisa Marchetto

Martina Livornese

Francesco Maria Sabatini

Enrico Tordoni

Daniele Da Re

Jonathan Lenoir

Riccardo Testolin

Giovanni Bacaro

Roberto Cazzolla Gatti

Alessandro Chiarucci

Giles M. Foody

Lukáš Gábor

Quentin Groom

Jacopo Iaria

Marco Malavasi

V´ıtˇezslav Moudr´y

Diletta Santovito

Petra Šímová

Piero Zannini

Duccio Rocchini

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

解决大陆尺度生物多样性数据库中的多方面偏差和不确定性

Species distribution models (SDM) are widely used tools in ecology and conservation aimed at predicting the potential distribution of a species based on its environmental requirements and occurrence data. SDM face many challenges and uncertainties that influence their accuracy. Selecting the ideal calibration area is one of these difficulties. This study analyzes the influence of the extent of the calibration area on the accuracy of SDM through simulations with virtual species. Using bioclimatic variables, 100 virtual species were generated. Occurrence probabilities were determined based on environmental suitability, spatial sampling bias, and accessible areas. SDM were built using MaxEnt, varying size of calibration area, spatial filtering of occurrence records, predictor collinearity treatment, and regularization parameter. Model performance was assessed in terms of functional accuracy (true model accuracy) and discrimination accuracy (model ability to separate occurrence from random sites). Results show that the extent of the calibration area was the most influential factor (explaining 50% of the variance in functional accuracy), while regularization multiplier, predictor collinearity, and spatial thinning had minimal impact (about 4% of explained variance combined). Overall, larger calibration areas generally led to higher functional accuracy, although it varies across species. The correlation between functional and discrimination accuracy was relatively low, indicating that models performing well in one metric may not excel in the other. In conclusion, this research advances the discussion on calibration area selection, providing insights on its substantial effects on model accuracy. Our findings demonstrate that the size of the calibration area is one of the most critical factors affecting the accuracy of models, surpassing the influence of other factors. These insights highlight the importance of select appropriate calibration areas to improve model predictions and ensure more reliable applications of the models.

Sergio Luna

Alexander Peña-Peniche

Roberto Mendoza-Alfaro

Species distribution model accuracy is strongly influenced by the choice of calibration area

校准区域选择对物种分布模型精度具有重要影响

Here, we present the new R package “enmpa,” which includes a range of tools for modeling ecological niches using presence-absence data via logistic generalized linear models. The package allows users to calibrate, select, project, and evaluate models using independent data. We have emphasized a comprehensive search for ideal predictor combinations, including linear, quadratic, and two-way interaction responses, to provide more detailed and robust model calibration processes. We demonstrate the use of the package with an example of a simulated pathogen and its niche. Since enmpa is designed specifically to work with presence-absence data, our tools are particularly useful for studies with data derived from a detection or non-detection sampling universe, such as pathogen testing results. enmpa can be downloaded from CRAN, and the source code is freely available on GitHub.

Luis F. Arias-Giraldo

Marlon E. Cobos

enmpa: An R package for ecological niche modeling using presence-absence data and generalized linear models

enmpa：一个使用存在-缺失数据和广义线性模型进行生态位建模的R包

Genetic diversity is fundamental to biological diversity, vital for species’ health and adaptation to environmental change. Under the recently adopted Kunming-Montreal Global Biodiversity Framework (GBF), 196 Parties committed to report the status of genetic diversity for both wild and domesticated species. For this, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: ensuring that populations are large enough to maintain genetic diversity (effective population size Ne 500 indicator) and maintaining genetically distinct populations (populations maintained, PM indicator). A third indicator focuses on the number of species being monitored using DNA-based methods. Adopted by 196 CBD Parties in December 2022, GBF integrated Ne 500 and PM as headline and complementary indicators, respectively. To aid nations in quantifying these indicators, a detailed set of guideline materials was developed, encompassing species selection, data compilation, and indicator computation. These guidelines draw from the collaborative efforts of the first multinational assessment of genetic diversity indicators that was recently completed and that will be refined continually through a versioning system, as more experience is gained and shared. The materials aim to support the global monitoring framework established by the CBD and are accessible online for utilization and updates. The guidelines are available at this link.

Alicia Mastretta-Yanes

Sofía Suárez

Rebecca Jordan

Sean Hoban

Jessica M. Da Silva

Luis Castillo-Reina

Myriam Heuertz

Fumiko Ishihama

Viktoria Köppä

Linda Laikre

Anna J. MacDonald

Joachim Mergeay

Ivan Paz-Vinas

Gernot Segelbacher

Alicia Knapps

Henry Rakoczy

Amelie Weiler

Angelica Atsaves

Kira Cullmann

Simone Bagnato

Brenna R. Forester

Guideline Materials and Documentation for the Genetic Diversity Indicators of the Monitoring Framework for the Kunming-Montreal Global Biodiversity Framework

《昆明-蒙特利尔全球生物多样性框架监测框架遗传多样性指标的指南材料和文件》

短名	Biodiv. Inf.
Journal Impact	2.52
国际分区	BIODIVERSITY CONSERVATION(Q2)

出版信息	出版商: The University of Kansas，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: ，原创研究文献占比: ，自引率:， Gold OA占比: 0.00

Biodiversity informatics

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Enhancing Ecological Education: Utilizing Agent-Based Modeling to Simplify the Impacts of Deforestation on Amphibians

Digital Knowledge of Kenyan Succulent Flora and Priorities for Future Inventory and Documentation

Completeness of Digital Accessible Knowledge of Plants of Benin and Priorities for Future Inventory and Data Discovery

Completeness of Digital Accessible Knowledge of Plants of Benin and Priorities for Future Inventory and Data Discovery

Rescuing and Sharing Historical Vegetation Data for Ecological Analysis: The California Vegetation Type Mapping Project

Enhancing Ecological Education: Utilizing Agent-Based Modeling to Simplify the Impacts of Deforestation on Amphibians

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

Species distribution model accuracy is strongly influenced by the choice of calibration area

enmpa: An R package for ecological niche modeling using presence-absence data and generalized linear models

Guideline Materials and Documentation for the Genetic Diversity Indicators of the Monitoring Framework for the Kunming-Montreal Global Biodiversity Framework