Open research questions in Species Distribution and Climate Change

What the literature leaves open

• 5 1 9 4 / h e s s ‑ 1 0 ‑ 1 0 1 ‑ 2 0 0 6 Sztepanacz JL, Houle D (2024) Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data.

  Scaling the mountains: multiscale proxies derived from very high-resolution digital elevation models to characterize heterogeneous alpine landscapes and their local effects on plant adaptation · 2026 · DOI

• Indeed, in the case of uakari monkeys, an important limitation to dispersal is the influence of rivers on their geographic distribution. Although future scenarios may indicate ecologically suitable areas within the modeled region, the capacity of species to disperse to and establish in these areas remains uncertain.

  When suitable habitat is not enough: climate change, habitat loss, and dispersal limitation increase the vulnerability of bald-headed uakaris (Cacajao sp.) in the Amazon Rainforest · 2026 · DOI

• Land-use and land-cover (LULC) change is a dominant driver of biodiversity loss, yet its long-term role in restructuring species distribution remains poorly understood due to limited historical baselines and the scarcity of long-term analyses across multiple ecological transition zones.

  Legacy Effects of Land-Use Land-Cover Change on Species Distribution Dynamics: Evidence from a landscape of diverse biogeographic crossroads · 2026 · DOI

• While this study offers important insights regarding the existing and projected distribution of the Argun palm (Medemia argun), several limitations should be recognized. First, the relatively low number of recorded occurrences may reduce the precision of the species distribution models and increase uncertainty, especially in projections under future climate scenarios. Importantly, for rare species with nar- row geographic distributions, such as the Argun (Medemia argun), this number of validated records is both reasonable and consistent with the SDM literature. Previous studies have demonstrated that reliable SDMs can be developed with relatively small sample sizes (often fewer than 50 occur- rences), provided that data quality is high and appropriate modeling strategies such as ensemble approaches and careful evaluation are used (e.g., Hernandez et al. 2006; Pearson et al. 2007; van Proosdij et al. 2016). This is particularly true for species occupying environmentally distinct niches, as is the case for groundwater-dependent desert taxa. We fully agree that additional occurrence data from future field surveys or regional conservation organizations in Egypt and Sudan would further improve model refinement. However, given the rarity, restricted distribution, and logistical chal- lenges associated with surveying this species, the dataset used here represents a realistic and scientifically defensible balance between data availability and modeling rigor. Sec- ond, although ensemble SDMs improve predictive robust- ness, they do not account for dispersal limitations, biotic interactions, or landscape barriers, which could restrict natural colonization of newly suitable habitats. Third, the use of coarse-scale environmental layers may overlook fine- scale microhabitats or local hydrological features critical for this groundwater-dependent palm. Future research could address these limitations by incorporating spatially explicit dispersal models, higher-resolution environmental data, and field-based validation of projected suitable areas. Addition- ally, studies exploring the genetic diversity and adaptive capacity of existing populations could inform conservation strategies. Overall, combining modeling with empirical and mechanistic approaches will provide a more comprehensive basis for guiding conservation and management of this rare desert palm under ongoing climate change.

  Forecasting the future of a desert relict: current and future potential distribution of the rare Argun palm Medemia argun under climate change scenarios · 2026 · DOI

• Future research will focus on validating these 1 3Tree Genetics & Genomes (2026) 22:16 findings by establishing and evaluating breeding trials, aim- ing to confirm adaptability patterns and refine productivity predictions across contrasting environments.

  A new environmental-based tool to support forest breeders in selecting species adapted to current and near-term climate conditions · 2026 · DOI

• Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2026 npj Biodiversity | (2026) 5:17 12

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• Davies, J. et al. Conserving Dryland Biodiversity (International Union 32. 33. 34. 35. 36. for Conservation of Nature and Natural Resources, 2012). Brito, J. C., Del Barrio, G., Stellmes, M., Pleguezuelos, J. M. & Saarinen, J. Drivers of change and conservation needs for vertebrates in drylands: an assessment from global scale to Sahara- Sahel wetlands. Eur. Zool. J. 88, 1103–1129 (2021). Brito, J. C. & Pleguezuelos, J. M. Desert biodiversity—world’s hot spots/globally outstanding biodiverse deserts (Elsevier, 2020). Farhadinia, M. S. et al. Wandering the barren deserts of Iran: illuminating high mobility of the Asiatic cheetah with sparse data. J. Arid Environ. 134, 145–149 (2016). Farhadinia, M. S. et al. Estimating the density of a small population of leopards (Panthera pardus) in central Iran using multi-session photographic-sampling data. Mamm. Biol. 101, 363–371 (2021). Finke, D. L. & Denno, R. F. Predator diversity dampens trophic cascades. Nature 429, 407–410 (2004). 37. Drake, K. K. et al. Negative impacts of invasive plants on conservation 38. 39. 40. 41. 42. of sensitive desert wildlife. Ecosphere 7, e01531 (2016). Rodríguez-Castañeda, G. The world and its shades of green: a metaanalysis on trophic cascades across temperature and precipitation gradients. Glob. Ecol. Biogeogr. 22, 118–130 (2013). Ripple, W. J. et al. What is a trophic cascade? Trends Ecol. Evol. 31, 842–849 (2016). Ehleringer, J. R. Productivity of deserts. in Terrestrial Global Productivity, 354–362 (Elsevier, 2001). Terborgh, J., Holt, R. D., Estes, J. A., Terborgh, J. & Estes, J. Trophic cascades: what they are, how they work, and why they matter. Trophic Cascades: Predators, Prey, and the Changing Dynamics of Nature 1–18 (Island Press, 2010). ENETWILD-consortium et al. Wild ungulate density data generated by camera trapping in 37 European areas: first output of the European Observatory of Wildlife (EOW). EFSA Support. Publ. 20, 7892E (2023). 43. Hickisch, R. et al. Effects of publication bias on conservation planning. Conserv. Biol. 33, 1151–1163 (2019). 45. 44. UN. 2010–2020: UN Decade for Deserts and the Fight against Desertification. https://www.un.org/en/events/desertification_ decade/whynow.shtml (2010). Legge, S. et al. The Arid Zone Monitoring Project: combining Indigenous ecological expertise with scientific data analysis to assess the potential of using sign-based surveys to monitor vertebrates in the Australian deserts. Wildl. Res. 51, WR24070 (2024). Lindsey, P. et al. Conserving Africa’s wildlife and wildlands through the COVID-19 crisis and beyond. Nat. Ecol. Evol. 4, 1300–1310 (2020). Vallejo-Vargas, A. F. et al. Consistent diel activity patterns of forest mammals among tropical regions. Nat. Commun. 13, 7102 (2022). 48. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). Lane, M. A. & Edwards, J. L. The Global Biodiversity Information Facility (GBIF). Syst.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• areas like Bajdah Wildlife Reserve, will facilitate the evaluation and com- parison of different methodologies and analytical tools. However, for these approaches to be viable, they must be applicable beyond fenced areas if rewilding is to take place on a large scale and fulfil the underlying ambitions of those implementing it. This will ultimately provide guidance on how to align diverse data with appropriate statistical frameworks, enabling accurate inferences about dryland wildlife (i.e. ecological communities composed of multiple species), the environment, and their responses to changes at scale. Currently, one of the main barriers to collaboration is that metadata describes study design, and deployments are not interoperable. Before establishing a network, dryland researchers already using any of these monitoring methods should strive to adopt rigorous and widely recognised metadata schemas, such as camtrapDP, for their ongoing work53. Ground- truthing outputs from new technology or methods, such as land use clas- sification products derived from remote sensing imagery64, would also benefit a global network by reducing delays caused by waiting for these steps to be completed. Initial steps for a network could include appointing a steering committee to set clear goals and communicate mission statements to the community. The committee should identify key stakeholders to gain insight into community needs, historical barriers to collaboration, and which data-sharing approaches resonate most with the community, such as WildObs, and facilitate pilot studies with multiple data streams from dif- ferent regions, similar to Bajdah Wildlife Reserve. This will help understand how the network can scale as engagement increases.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• species identification. Meanwhile, WildObs led the data curation, standar- disation, analysis, and report writing. At the time of the project (2023), the code base was still being developed, and the WildObs portion of the project took 10 weeks, while today, the same project has a 2-week turnaround time. This demonstrates that WildObs’ approach can go from images to infer- ences within time periods relevant to management.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• Wildlife Service), federal government representatives (DCCEW), and sev- eral universities7. The Wildlife Summit revealed an appetite for shared infrastructure, knowledge sharing, data sharing and collaboration. Next, to identify specific camera usage and user needs, they conducted a literature review and a questionnaire of over 150 camera users, which revealed that while small targeted camera trap surveys were prevalent across the con- tinent, there had been minimal expansion into multi-site, multi-year studies, partly due to limited human bandwidth and exposure to more efficient technologies7, such as AI-powered object detectors (e.g. MegaDetector) and computer vision classifiers for identifying species in images. There was also little use of detection-corrected hierarchical models to address slight var- iations in deployment methodology (e.g. occupancy models with covariates in the detection formula), thereby enabling multiple studies to be integrated into a monitoring framework. There were also friction points to colla- boration required for a large-scale, long-term wildlife monitoring com- munity in Australia. At the time of writing, WildObs has >100 different data contributors across all Australian states (Fig. 5). To ensure datasets are interoperable, there is a rigorous standardisation pipeline that includes sending providers detailed metadata questionnaires. The final output complies with camtrapDP’s metadata schema, making it suitable for other initiatives and meeting WildObs’ requirements53. As the use of wildlife cameras increases, WildObs considered scaling issues from the outset. This underpinned WildObs’ decision to build and host everything on federal digital infrastructure (the Nectar Research Cloud) to enable cloud storage and computing. For a dryland observatory spanning multiple countries, collaboration with a global provider such as Google, Microsoft or Amazon, similar to the Wildlife Insights platform, could be a viable alternative. However, this would need to be weighed against any prospective data-sharing agreements and privacy concerns.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• modelling for making robust inferences. Cumulatively, these challenges can result in information being generated but not utilised, as it takes too long to process or inappropriate metrics are used to infer abundance, for example. A collaborative network can address a small aspect of a much larger issue by providing analytical scripts and tutorials for managing datasets that comply with its metadata schema. These resources could address topics ranging from data manipulation to analysis and visualisation for reporting. Encouraging an inclusive network with membership across various organisations, including international and local universities and government agencies, can foster partnerships that support mentorship and collaborative programmes both domestically and internationally. Consequently, this can facilitate the adoption of modern methods, enhance local capacity, demonstrate the benefits of using contemporary techniques to other stakeholders and potentially help mitigate various socio-economic barriers. How would a network function at scale? The growth of scientific communities operating at global and continental levels, along with the scientific papers generated by standardised networks of remote sensors, demonstrates that an initiative like a drylands collaborative network is achievable42,47. Establishing a network to bridge the gap between conservation activities and their outcomes through large-scale monitoring presents an opportunity to integrate often overlooked groups and employ innovative methods that combine data streams from different technologies, ultimately leading to more effective long-term outcomes. Any proposed network would need to scale from individual sites to a global level. One strategy is to utilise horizontal integration, whereby each data provider is responsible for their sampling, allowing flexibility in the survey design. However, by opting into the network and adhering to the established minimum standards, they will gain access to shared knowledge, a community of collaborators, and resources and tools in their native language to maximise their data and monitoring efforts. There are barriers to collating data from different sites, including ensuring that the datasets generated by various providers are interoperable. The transition from local sites to global analyses can only occur by providing clear guidance on the metadata schema. Research conducted under non-disclosure agreements can limit timely access to data and hinder broader knowledge sharing. Adopting frameworks such as Findable, Accessible, Interoperable and Reusable (FAIR)48, or following the approaches of globally successful initiatives like the Global Biodiversity Information Facility (GBIF)49, can facilitate the sharing of data once embargo periods have ended. For instance, data contributors could retain ownership while releasing datasets under permissive licences such as CC-BY following agreed embargo periods, or restrict access to approved network members during sensitive phases, as adopted by platforms such as GBIF and Wildlife Insights. Finally, by offering workshops and training events and inviting practitioners to contribute to the minimum monitoring standards, it ensures that any offerings align with community needs and are applicable, increasing the likelihood of wide adoption. By promoting widespread adoption, a network is more likely to attract funding bodies to become involved, which could alleviate some of the financial challenges of conducting long-term monitoring for its members, facilitate workshops, and ensure the network’s longevity. We demonstrate how a network could function at the scale of an individual site and a whole country using two case studies outlined below. Case study 1: local scale—NEOM Saudi Arabia NEOM Nature Reserve is a newly-established protected area covering 25,000 km2 in northwest Saudi Arabia50. As part of the Kingdom’s Vision 2030, several larger natural landscapes are being protected and restored as sources for ecosystem restoration across the broader region. Within these areas, management interventions include regreening and wadi restoration, reducing human perturbation, and reintroducing ecologically functional communities of herbivores and carnivores. Bajdah Wildlife Reserve is one of the first ecological restoration sites established within NEOM Nature Reserve. It is currently an 85 km² fenced area, but plans are being established to expand it into a final designated reserve of 1086 km².

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• wildlife population dynamics and habitat associations with environmental and anthropogenic variables post-intervention. To address the current monitoring gap in drylands, we propose a collaborative network of scientists and practitioners, similar to other global initiatives such as the Tropical Ecology Assessment and Monitoring (TEAM) network, be established that would enhance understanding and drive progress in dryland conservation worldwide. Expanding existing networks is unlikely to be effective, as these platforms have seen limited adoption for reasons that remain unclear, potentially including inadequate technological infrastructure, data restric- tions imposed by non-disclosure agreements and general reluctance to collaborate. Moreover, dryland practitioners often rely on multiple data sources to account for low species densities and wide-ranging wildlife, which existing frameworks are not designed to accommodate. Finally, the relative paucity of research in dryland systems means that a dedicated, community- informed network would better align with local monitoring needs and promote broader participation. A bespoke network would facilitate stan- dardisation, scaling up and subsequent information sharing to assess changes in these systems at biogeographic scales and help achieve more effective and sustainable conservation outcomes.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• Bridging monitoring gaps in global drylands with big data and collaboration https://doi.org/10.1038/s44185-026-00129-6 Tom Bruce1, Rajan Amin1, Kausik Banerjee2, José Carlos Brito3,4, Bogdan Cristescu5,6, Mohammad S. Farhadinia7, Matthew Scott Luskin8,9, Brett Lyons10, David Olson10, Joaquín Vicente11 & Robert A.

  Bridging monitoring gaps in global drylands with big data and collaboration · 2026 · DOI

• Preliminary HDS results showed 65% of species were previously underestimated, but this analysis is based on approximately 27,000 complete lists from 4,000 sites, indicating need for expanded validation across broader geographic and temporal scales.

  Ornitho Family: two decades of citizen science in biodiversity monitoring · 2026 · DOI

• An automatic archiving strategy should be implemented, as it is currently handled manually by depositing a CSV dump and herbarium labels images in Zenodo.

  herbUA Collectors: An open-source framework for online publication of the herbarium collector-centric data · 2026 · DOI

• Curating protected area-level species lists requires integration of diverse and dynamic data sources, but standardized curation methods across different data sources remain an open challenge (Wenk et al., 2024).

  Guidelines and best practices for the scientific use of global iNaturalist data · 2026 · DOI

• The reproducibility and FAIR data principles in plant ecology and evolution require pressing improvements, particularly regarding data accessibility and standardization (Manzano & Julier, 2021).

  Guidelines and best practices for the scientific use of global iNaturalist data · 2026 · DOI

• Data obscuration practices in iNaturalist impact species distribution models, but the full extent and best practices for handling obscured data remain unclear (Koo et al., 2025).

  Guidelines and best practices for the scientific use of global iNaturalist data · 2026 · DOI

• Open Science instructions targeted to ecologists and evolutionary biologists may be insufficient, as indicated by literature reviews of guidelines and journal data policies (Koivisto & Mäntylä, 2024).

  Guidelines and best practices for the scientific use of global iNaturalist data · 2026 · DOI

• Guidelines and best practices exist for eBird data (Strimas-Mackey et al. 2023), but equivalent comprehensive guidelines for iNaturalist data use in scientific research need to be developed and standardized.

  Guidelines and best practices for the scientific use of global iNaturalist data · 2026 · DOI

• The fundamental technical infrastructure required for the proposed framework already exists. The main obstacles that must be addressed before the framework can achieve widespread adoption are: 1. Data availability – Important resources (e.g. country‑level checklists) are still missing from name‑matching services, at least in some cases caused by access restrictions imposed by data contributors. This is limiting the guide’s relevance for many users; A user-centric framework for harmonizing scientific name usage 21 2. 3. 4. Standardised metadata – No community‑agreed schema presently captures both the scope of a dataset and the characteristics of the associated matching service. Without such a standard, automatic aggregation and comparison of services remain difficult; Ongoing curation – Keeping metadata up to date demands a modest, but continuous editorial effort and close coordination with data providers; User awareness and uptake – Tools that are not widely known or perceived as useful are unlikely to be sustained. Addressing these points will require: 1. 2. 3. 4. Outreach to national biodiversity agencies and other data owners to encourage deposition of their checklists as open data in platforms such as Catalogue of Life's ChecklistBank or Global Names; Development and endorsement of a community‑driven metadata standard (building on existing TDWG/GBIF specifications); Implementation of a simple, web‑based interface for community contributions and routine updates; Targeted promotion (e.g. tutorials, webinars, case‑study papers) to demonstrate the guide’s value to the different user communities.

  A user-centric framework for harmonizing scientific name usage · 2026 · DOI

• Our objectives include gaining a deeper understanding of the effects of localised environmental conditions and their change in time on biodiversity, providing critical data for an under-researched Mediterranean Biodiversity Hotspot region, and examining the dynamics of small-sized species, particularly plants and invertebrates.

  BASS - Biodiversity Assessments at Small Scales · 2024 · DOI

• Implications for Conservation The use of a taxonomic perspective focused on the phylogenetic relationship of the different populations would directly impact the definition of risk categories, particularly for those endemic or restricted distribution evolutionary units for which there is usually very little information available and which, undoubtedly, are the ones that most need to be studied and, particularly, protected.

  Implications on the Use of the Phylogenetic Species Concept in the Risk Categories Assignment: The Case of the Birds of Mexico · 2022 · DOI

• The paper mentions that naming trends forecast an 'asymptote within the next half-century at which all new taxonomic names will be whimsical,' but does not provide the mathematical model, historical naming rate data, or predictive parameters (saturation curves, diversity of remaining unnamed species) underlying this forecasting claim.

  Taxonomic punchlines: metadata in biology · 2021 · DOI

• The paper documents specific instances where editorial gatekeeping (e.g., C.T. Brues rejecting T. nippontucki for containing 'political satire') influenced whimsical naming acceptance, but provides no systematic analysis of editorial policies, journal guidelines, or institutional acceptance thresholds for unconventional nomenclature across natural history journals and taxonomic publishing venues.

  Taxonomic punchlines: metadata in biology · 2021 · DOI

Most-cited papers in Species Distribution and Climate Change

• Defaunation in the Anthropocene · Science · 2014 · 3,516 citations
• Collinearity in ecological niche modeling: Confusions and challenges · Ecology and Evolution · 2019 · 372 citations
• Ecological Impacts of Alien Species: Quantification, Scope, Caveats, and Recommendations · BioScience · 2014 · 365 citations
• Observing the Observers: How Participants Contribute Data to iNaturalist and Implications for Biodiversity Science · BioScience · 2021 · 294 citations
• Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change · Global Change Biology · 2019 · 248 citations
• Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science · BioScience · 2016 · 238 citations
• Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity · BioScience · 2019 · 233 citations
• Crossing Frontiers in Tackling Pathways of Biological Invasions · BioScience · 2015 · 227 citations
• Hybridization in a warmer world · Ecology and Evolution · 2014 · 207 citations
• A Conceptual Framework for Range-Expanding Species that Track Human-Induced Environmental Change · BioScience · 2019 · 199 citations

Most recent work

• High risk of extinction across the flowering plant tree of life · Science · 2026
• A comprehensive trait dataset for Terrestrial Arthropods of the Azores: insights for conservation, island ecology and species invasion · Biodiversity Data Journal · 2026
• Guidelines and best practices for the scientific use of global iNaturalist data · 2026
• herbUA Collectors: An open-source framework for online publication of the herbarium collector-centric data · Biodiversity Data Journal · 2026
• Extreme Heat as the New Normal: A Methodological Roadmap Incorporating Behavior, Physiology, and Species Distributions · Integrative And Comparative Biology · 2026
• The AvianMetaNetwork: biotic interactions among birds of the continental United States and Canada · bioRxiv · 2026
• Testing genomic offset with common gardens in genetically structured black spruce (Picea mariana) · bioRxiv · 2026
• Projecting Current and Future Habitat for the European Ground Squirrel Using MaxEnt Modeling under Climate Change · Theoretical and Natural Science · 2026
• Citizen science yield different results compared to traditional databases regarding species distribution analysis · Mammalian Biology · 2026
• Ornitho Family: two decades of citizen science in biodiversity monitoring · Environmental and Experimental Biology · 2026

Related topics in Environmental Science