Skip to contents

Where flexsdm was cited?

Source: vignettes/pkg_citation.Rmd
pkg_citation.Rmd

Here we provide a list of published peer-reviewed papers that cited flexsdm package. Some of the following research used flexsdm for the entire modeling protocol, others used a couple of flexsdm functions, and others just mentioned our package.

Cite our package as

Velazco, S.J.E., Rose, M.B., Andrade, A.F.A., Minoli, I., Franklin, J. (2022). flexsdm: An R package for supporting a comprehensive and flexible species distribution modelling workflow. Methods in Ecology and Evolution, 13(8) 1661–1669. https://doi.org/10.1111/2041-210X.13874

@article{velazco_flexsdm_2022,
    title = {flexsdm: An r package for supporting a comprehensive and flexible species distribution modelling workflow},
    volume = {13},
    rights = {© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
    issn = {2041-210X},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13874},
    doi = {10.1111/2041-210X.13874},
    pages = {1661--1669},
    number = {8},
    journaltitle = {Methods in Ecology and Evolution},
    author = {Velazco, Santiago José Elías and Rose, Miranda Brooke and de Andrade, André Felipe Alves and Minoli, Ignacio and Franklin, Janet},
    date = {2022},
    note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13874}
}

Thanks to the authors for citing our package.

List updated automatically on 2026-09-23 (152 publications; source: OpenAlex).

2026 (n = 42)

  1. Anselmo, L., Caprio, E., Regaiolo, I., et al. (2026). From research to conservation: Site selection for habitat restoration of a narrowly distributed and critically endangered butterfly. Conservation Science and Practice, 8(8). https://doi.org/10.1111/csp2.70343
  2. Barbosa, R.V., Schuster, J., Godwin, S., et al. (2026). Substrate limitation and environmental heterogeneity shape kelp habitat distribution in a complex coastal landscape. Marine Ecology Progress Series, 793, 1–21. https://doi.org/10.3354/meps15224
  3. Bedrij, N.A., Montti, L.F., Keller, H.A., et al. (2026). Beyond protected areas: The synergistic role of forest territorial planning in safeguarding tree diversity. Biological Conservation, 321, 111982. https://doi.org/10.1016/j.biocon.2026.111982
  4. Bomfim, F.D.S., Diele‐Viegas, L.M., Almeida, T.S., et al. (2026). Predicting the future of the Caatinga endemic Melocactus pachyacanthus under climate and anthropogenic landscape changes. Discover Ecology, 2(1). https://doi.org/10.1007/s44396-026-00042-z
  5. Buck, R.C., Butterfield, H.S., Hiroyasu, E.H.T., et al. (2026). Applying Both Landscape Genomic and Ecological Niche Model Predictions to Inform Conservation Strategies of a California Foundational Oak Species. Molecular Ecology, 35(7), e70322. https://doi.org/10.1111/mec.70322
  6. Carvalho, R.G.G.D., Fraga, C.N., Moura, M.R., et al. (2026). Environmental variation is related to morphotype differentiation in a Brunfelsia (Solanaceae) complex. Annals of Botany. https://doi.org/10.1093/aob/mcag271
  7. Chávez-Hernández, M.G., Barreiro, P.G., White, J.D., et al. (2026). Prioritising native flora and geographic areas for ex situ conservation in the Sonoran Desert. Botanical Sciences. https://doi.org/10.17129/botsci.3874
  8. Coelho, F.D.A., Amaro, G.C., Batista, A.C., et al. (2026). Predicting global habitat suitability and invasion risks of the red gum lerp psyllid Glycaspis brimblecombei under current and future climates. Agricultural and Forest Entomology. https://doi.org/10.1111/afe.70076
  9. Copoț, O., Lõhmus, A. (2026). Assessment of multiple outcomes of habitat models can significantly affect conservation decisions for threatened species. Scientific Reports, 16(1), 5860. https://doi.org/10.1038/s41598-026-35987-4
  10. Dajil, J.E., Block, C., Vega, L.E., et al. (2026). Vulnerability of Pampean Coastal Lizards to Global Change: Divergent Responses of Endemic Specialists and Widespread Generalists. Biology, 15(14), 1152. https://doi.org/10.3390/biology15141152
  11. Demır, M.A., Kabalak, M. (2026). Predicting habitat suitability of selected Meloidae species and future potential refugia: A case study from inner Western Anatolia. Insect Conservation and Diversity, 19(5), 1185–1202. https://doi.org/10.1111/icad.70092
  12. Eisawi, K.A.E., Rija, A.A. (2026). The influence of climate change on bird distribution patterns and conservation priorities. Environmental Research, 308(Pt 1), 125552. https://doi.org/10.1016/j.envres.2026.125552
  13. Faggion, S., Marco, P.D., Machado-Filho, C., et al. (2026). Protected areas may fail in maintaining suitable ranges of threatened species: A habitat-association approach for Brazilian Savanna bird species. Basic and Applied Ecology, 94, 34–43. https://doi.org/10.1016/j.baae.2026.05.007
  14. Ferreira, M.L., Oliveira, G.S.D., Andrade, A.M.D., et al. (2026). Modeling the current and future potential distribution of Leptoglossus zonatus (Hemiptera: Coreidae) under climate change scenarios. International Journal of Tropical Insect Science. https://doi.org/10.1007/s42690-026-01997-y
  15. Ferreiro, A.M., Poljak, S., Plum, L., et al. (2026). At the edge of the burrow: The mitochondrial genetic structure and range dynamics of the yellow armadillo Euphractus sexcinctus in southern South America. Journal of Mammalian Evolution, 33(1). https://doi.org/10.1007/s10914-026-09812-8
  16. Giri, S., Pradhan, P. (2026). Beyond the obvious: insights into the diversity and ecological niche of the Myxomycetes in temperate forests of Eastern Himalaya. Studies in Fungi, 11(1), 0. https://doi.org/10.48130/sif-0026-0007
  17. hesabi, A., Alavi, S.J., Esmailzadeh, O. (2026). Machine learning meets ecology: XGBoost-based prediction of endangered species habitats using multi-source environmental data. Environmental Monitoring and Assessment, 198(8). https://doi.org/10.1007/s10661-026-15694-3
  18. Holcomb, K.M., Dunford, J.C., Connelly, C.R. (2026). Estimated suitability distribution for Culicoides (Haematomyidium) paraensis (Diptera: Ceratopogonidae) in the contiguous United States and associated Caribbean territories. Journal of Medical Entomology, 63(2). https://doi.org/10.1093/jme/tjag038
  19. Huber, B.A., Meng, G., Král, J., et al. (2026). Ninetine spiders in Brazilian Caatinga and Cerrado: revision of Kambiwa and description of Sertana gen. nov. (Araneae, Pholcidae), with analyses of predicted range shifts due to climate change. European Journal of Taxonomy, 1054. https://doi.org/10.5852/ejt.2026.1054.3276
  20. Issaly, E.A., Ferreiro, A.M., Baranzelli, M.C., et al. (2026). Climate-driven potential distribution of the invasive tree tobacco and its overlap with biodiversity conservation areas worldwide. Perspectives in Ecology and Conservation, 24(2), 175–181. https://doi.org/10.1016/j.pecon.2026.02.002
  21. Lima, M.D.C., Peres, C.A., Araujo, H.F.P. (2026). Endemic bird species are far more threatened than previously thought across the semiarid Caatinga dry forest domain. Biological Conservation, 315, 111690. https://doi.org/10.1016/j.biocon.2025.111690
  22. Liu, X., Jiang, X., Chen, C., et al. (2026). The invasive woodwasp Sirex noctilio Fabricius threatens pine forest carbon storage in China under climate change. Trees Forests and People, 26, 101337. https://doi.org/10.1016/j.tfp.2026.101337
  23. Madhavan, A., Bhat, K., Kasinathan, S., et al. (2026). Distribution models predict climate-related range alteration or extinction of eleven threatened tropical rainforest trees in the Western Ghats. Journal of Biosciences, 51(4). https://doi.org/10.1007/s12038-026-00622-x
  24. Magee, C., Rose, M.B., Franklin, J., et al. (2026). Supporting Tribal resilience in Southern California through plant vulnerability assessment. Earth stewardship., 3(2). https://doi.org/10.1002/eas2.70044
  25. Marques, T.M.S., Freitas‐Oliveira, R., Jardim, L.L.C.Z., et al. (2026). Regions of Climatic Stability for Neotropical Primates. Mammal Review, 56(1). https://doi.org/10.1111/mam.70025
  26. Omer, A., Dullinger, S., Wessely, J., et al. (2026). The global geography of plant invasion risk under future climate and land-use changes. Nature Ecology & Evolution, 10(5), 952–960. https://doi.org/10.1038/s41559-026-03040-2
  27. Pasipanodya, E.V., Zvidzai, M., Mawere, K.K., et al. (2026). Spatio-temporal variation in habitat suitability of Southern giraffe (Giraffa giraffa) under long-term environmental change in Hwange National Park, Zimbabwe. Environmental Monitoring and Assessment, 198(2), 116. https://doi.org/10.1007/s10661-025-14938-y
  28. Rahimi, E., Jung, C. (2026). How can we incorporate species interactions into SDM-based climate change modelling? Community Ecology. https://doi.org/10.1007/s42974-026-00330-4
  29. Rahimi, E., Jung, C. (2026). Climate Change May Expand Geographic Distribution of Asian Butterflies Despite Climatic Niche Contraction. Insects, 17(7), 683. https://doi.org/10.3390/insects17070683
  30. Rahimi, E. (2026). What are the key bioclimatic variables shaping bees’ distribution globally? Community Ecology. https://doi.org/10.1007/s42974-026-00343-z
  31. Rangel, C.B.A., Weber, M.M. (2026). Improving the Prediction of Treefrog Species Vulnerability to Climate Change: The Role of Abundance. Journal of Biogeography, 53(7). https://doi.org/10.1111/jbi.70289
  32. Romera-Romera, D., Berjano, R., Estrella, M.D.L., et al. (2026). Ecological differentiation in Pinus nigra subspecies predicts differential responses to climate change. European Journal of Forest Research, 145(2). https://doi.org/10.1007/s10342-025-01868-0
  33. Rose, M.B., Velazco, S.J.E., Regan, H.M., et al. (2026). Species‐Specific Responses to Multiple Climatic Variables Predict Diverging Locations of Future Climate Change Refugia. Diversity and Distributions, 32(2). https://doi.org/10.1111/ddi.70158
  34. Salariato, D.L., Zuloaga, F.O. (2026). Uneven impacts of climate change on angiosperm diversity across the South American Temperate Grasslands biome. Perspectives in Plant Ecology Evolution and Systematics, 71, 125935. https://doi.org/10.1016/j.ppees.2026.125935
  35. Santos, A., Payne, R., Branco, M., et al. (2026). Differing effects of climate change on wild bee nesting groups across Europe. Ecological Informatics, 97, 103902. https://doi.org/10.1016/j.ecoinf.2026.103902
  36. Soni, P., Hendy, A., Bottjer, D.J. (2026). ‘EcoCleanR’: enhancing data quality of biogeographic ranges with application for marine invertebrates. Ecography, 2026(3). https://doi.org/10.1002/ecog.08203
  37. Sorboni, S.G., Ghahremaninejad, F., Sunway, M.H., et al. (2026). Projecting future climatic refugia for Fagus orientalis in the Hyrcanian ecoregion. Journal for Nature Conservation, 95, 127444. https://doi.org/10.1016/j.jnc.2026.127444
  38. Syphard, A.D., Rustigian-Romsos, H., Franco, D., et al. (2026). Lessons learned using species’ distribution models for conservation planning in the Golden Gate Biosphere reserve. PLoS ONE, 21(3), e0343037. https://doi.org/10.1371/journal.pone.0343037
  39. Szinwelski, N., Prasniewski, V.M., Vendruscolo, L.S.N., et al. (2026). Spatial distribution, habitat suitability, and threat status of Diponthus crassus (Orthoptera: Romaleidae: Romaleini). Journal of Insect Conservation, 30(4). https://doi.org/10.1007/s10841-026-00790-z
  40. Tytar, V. (2026). Range expansion of the golden jackal (Canis aureus) in Europe. Theriologia Ukrainica, 31, 143–156. https://doi.org/10.53452/tu3112
  41. WANG, Z., WANG, Q., ZHOU, Y., et al. (2026). Identification and Prioritization of Climate-Adaptive Types for Territorial Ecological Restoration Zone in the Chengdu-Chongqing Urban Agglomeration. Landscape Architecture, 33(3), 12–22. https://doi.org/10.3724/j.fjyl.la20250650
  42. Westphalen, M.C., Martins-Cunha, K., Alves-Silva, G., et al. (2026). An announced tragedy: climate-driven habitat loss for the critically endangered Wrightoporia araucariae (Basidiomycota, Russulales). Fungal ecology, 82, 101515. https://doi.org/10.1016/j.funeco.2026.101515

2025 (n = 46)

  1. Alves‐Ferreira, G., Vancine, M.H., Mota, F.M.M., et al. (2025). From Hot to Cold Spots: Climate Change is Projected to Modify Diversity Patterns of Small Mammals in a Biodiversity Hotspot. Diversity and Distributions, 31(5). https://doi.org/10.1111/ddi.70026
  2. Amaro, G.C., Aidoo, O.F., Souza, P.G.C., et al. (2025). Global Climate Suitability and Economic Risks of the Fall Armyworm Spodoptera frugiperda to Key Crops in Brazil. Food and Energy Security, 14(5). https://doi.org/10.1002/fes3.70120
  3. Amaro, G.C., Marchioro, C.A., Silva, R.S.D., et al. (2025). Current and future global distribution of the peach twig borer, Anarsia lineatella Zeller (Lepidoptera: Gelechiidae). Agricultural and Forest Entomology, 28(1), 94–107. https://doi.org/10.1111/afe.70012
  4. Anselmo, L., Caprio, E., Baruzzi, A., et al. (2025). Where and how to conserve butterflies amid climate change: a model-based approach on Papilio alexanor. Biodiversity and Conservation, 34(12), 4575–4615. https://doi.org/10.1007/s10531-025-03170-2
  5. Antonio, A.I., Oliveira, A.C.D., Villalobos, F., et al. (2025). Environmental heterogeneity as a determinant of bee diversity patterns in the Atlantic Forest. Frontiers of Biogeography, 18. https://doi.org/10.21425/fob.18.142410
  6. Augustin, A.F., Lima, D.F., Vieira, F.C.S., et al. (2025). Species distribution modeling of two rare endemic Myrtaceae from the Brazilian Atlantic Forest: challenges in conserving “invisible species”. Flora, 333, 152854. https://doi.org/10.1016/j.flora.2025.152854
  7. Backus, G.A., Rose, M.B., Velazco, S.J.E., et al. (2025). Population Decline for Plants in the California Floristic Province: Does Demography or Geography Determine Climate Change Vulnerability? Diversity and Distributions, 31(8). https://doi.org/10.1111/ddi.70067
  8. Basooma, A., Schmidt‐Kloiber, A., Domisch, S., et al. (2025). ‘specleanr’: an R package for automated flagging of environmental outliers in ecological data for modeling workflows. Ecography, 2025(12). https://doi.org/10.1002/ecog.08221
  9. Bro‐Jørgensen, J., Ikram, S., Spedding, J.V., et al. (2025). Applying habitat suitability modelling to establish the species identity of ambiguous animal depictions in archaeology: new insights into the wild bovids of ancient Egypt. Journal of Archaeological Science, 179, 106239. https://doi.org/10.1016/j.jas.2025.106239
  10. Castillo, D.S.C., Higa, M. (2025). Effectiveness and implications of spatial background restrictions on model performance and predictions: a special reference for Rattus species. Landscape and Ecological Engineering, 21(3), 495–509. https://doi.org/10.1007/s11355-025-00653-w
  11. Cheng, H., Johansen, K., Jin, B., et al. (2025). Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change. Global Ecology and Conservation, 61, e03657. https://doi.org/10.1016/j.gecco.2025.e03657
  12. Croft, S., Warren, D., Blanco‐Aguiar, J., et al. (2025). Predicting the distribution of common wild mammal species across Europe - are there sufficient occurrence data? European Journal of Wildlife Research, 71(6). https://doi.org/10.1007/s10344-025-02014-2
  13. Duyar, A., Demır, M.A., Kabalak, M. (2025). Prediction of Current and Future Distributions of Chalcophora detrita (Coleoptera: Buprestidae) Under Climate Change Scenarios. Ecology and Evolution, 15(1), e70693. https://doi.org/10.1002/ece3.70693
  14. Ferreiro, A.M., Romero‐Muñoz, A., Issaly, E.A., et al. (2025). Habitat Loss and Overexploitation Subordinate Climate Change as the Main Threats to the Southern Three‐Banded Armadillo in the Threatened South American Chaco. Animal Conservation, 29(3), 334–345. https://doi.org/10.1111/acv.70047
  15. Fisher, R.J. (2025). Changes in urban landcover picks winners and losers in the non-invasive bird community. Urban Ecosystems, 28(2). https://doi.org/10.1007/s11252-025-01710-w
  16. Gehman, C.S., Gienger, C.M. (2025). Predicting the potential distribution of the Gila Monster and evaluating the extent of protected natural areas for conservation. Journal for Nature Conservation, 86, 126944. https://doi.org/10.1016/j.jnc.2025.126944
  17. Georgopoulou, E., Kougioumoutzis, K., Simaiakis, S.M. (2025). The Impact of Climate and Land Use Change on Greek Centipede Biodiversity and Conservation. Land, 14(8), 1685. https://doi.org/10.3390/land14081685
  18. Habel, J.C., Gros, P., Eberle, J., et al. (2025). Effects of climate- and land-use change on the cold-adapted Poplar Admiral butterfly. Journal of Insect Conservation, 29(6). https://doi.org/10.1007/s10841-025-00716-1
  19. Harapan, T.S., Ong, L., Agung, A.P., et al. (2025). A Slow and Underappreciated Forest Megafauna: Food Habits, Movements, and Multiscale Habitat Preferences of Critically Endangered Sundaic Giant Tortoises ( Manouria emys emys ). Integrative Zoology, 21(4), 798–816. https://doi.org/10.1111/1749-4877.12965
  20. Hayes, S.E., Hilton, J., Mould-Quevedo, J.F., et al. (2025). Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe. Scientific Reports, 16(1), 997. https://doi.org/10.1038/s41598-025-30651-9
  21. Holcomb, K.M., Foster, E., Eisen, R.J. (2025). Estimating the density of questing Ixodes scapularis nymphs in the eastern United States using climate and land cover data. Ticks and Tick-borne Diseases, 16(2), 102446. https://doi.org/10.1016/j.ttbdis.2025.102446
  22. Holcomb, K.M., Foster, E., Maes, S.E., et al. (2025). Estimated density of Borrelia burgdorferi sensu stricto-infected Ixodes scapularis nymphs in the eastern United States. Parasites & Vectors, 18(1), 350. https://doi.org/10.1186/s13071-025-06937-2
  23. Hubbard, J.A.G., Drake, D., Mandrak, N.E. (2025). ‘Euclimatch’: an R package for climate matching with Euclidean distance metrics. Ecography, 2025(4). https://doi.org/10.1111/ecog.07614
  24. Klichowska, E., Wróbel, A.M., Nowak, A.S., et al. (2025). Eco‐Evolutionary Genomics Reveal Mountain Range‐Specific Adaptation and Intraspecific Variation in Vulnerability to Climate Change of Alpine Endemics. Molecular Ecology, 34(21), e70113. https://doi.org/10.1111/mec.70113
  25. Koldasbayeva, D., Zaytsev, A. (2025). Foundation for unbiased cross-validation of spatio-temporal models for Species Distribution Modeling. Ecological Informatics, 92, 103521. https://doi.org/10.1016/j.ecoinf.2025.103521
  26. Kougioumoutzis, K., Kokkoris, I., Trigas, P., et al. (2025). Projected Impacts of Climate and Land Use Change on Endemic Plant Distributions in a Mediterranean Island Hotspot: The Case of Evvia (Aegean, Greece). Climate, 13(5), 100. https://doi.org/10.3390/cli13050100
  27. Lee, F., Kusabs, I.A.K., Perry, G.L.W., et al. (2025). Identifying refugia from the synergistic threats of climate change and invasive species. Web Ecology, 25(2), 221–239. https://doi.org/10.5194/we-25-221-2025
  28. Lin, Y., Liu, Q., Lv, S., et al. (2025). Assessing the Potential Distribution of the Traditional Chinese Medicinal Plant Spatholobus suberectus in China Under Climate Change: A Biomod2 Ensemble Model-Based Study. Biology, 14(8), 1071. https://doi.org/10.3390/biology14081071
  29. Menchions, E., Golinski, G.K., Naujokaitis‐Lewis, I., et al. (2025). Using rare mosses to resolve barriers in the use of species distribution models for climate change vulnerability assessments. Conservation Science and Practice, 7(10). https://doi.org/10.1111/csp2.70153
  30. Mezhzherin, S., Tytar, V., Rashevska, H.V., et al. (2025). Unveiling the ecological drivers of the great jerboa’s range: a species distribution model with implications for plague risk. Theriologia Ukrainica, 30, 55–66. https://doi.org/10.53452/tu3007
  31. Oliveira, A.C.D., Velazco, S.J.E. (2025). adm : An R package for constructing abundance‐based species distribution models. Methods in Ecology and Evolution, 16(7), 1404–1412. https://doi.org/10.1111/2041-210x.70074
  32. Patrón-Rivero, C., Yáñez‐Arenas, C., Chiappa‐Carrara, X., et al. (2025). Ecological and biogeographic drivers of speciation in neotropical hognose pit vipers, Porthidium (Squamata, Viperidae). Zoologischer Anzeiger, 318, 65–76. https://doi.org/10.1016/j.jcz.2025.07.007
  33. Porto, A.C.M., Santos, M.D.L., Lima, R.P.M., et al. (2025). Modelled potential changes in the climate-related geographic distribution of species of the Passiflora genus in Brazil. Plant Ecology & Diversity, 18(1-2), 69–82. https://doi.org/10.1080/17550874.2025.2505425
  34. Porto, A.C.M., Novaes, E. (2025). Prediction of current and future environmental suitability for Toona ciliata cultivation in Brazil. Discover Forests, 1(1). https://doi.org/10.1007/s44415-025-00029-w
  35. Pulido, K.G.R., Velazco, S.J.E. (2025). On protected areas and other effective area-based conservation measures to conserve biodiversity. Exploring their contribution to Colombian snakes. Perspectives in Ecology and Conservation, 23(2), 110–120. https://doi.org/10.1016/j.pecon.2025.04.002
  36. Rahimi, E., Jung, C. (2025). Exploring Climate-Driven Mismatches Between Pollinator-Dependent Crops and Honeybees in Asia. Biology, 14(3), 234. https://doi.org/10.3390/biology14030234
  37. Rahimi, E., Jung, C. (2025). Investigating the Spatial Biases and Temporal Trends in Insect Pollinator Occurrence Data on GBIF. Insects, 16(8), 769. https://doi.org/10.3390/insects16080769
  38. Rahimi, E., Jung, C. (2025). Mapping co-occurrence dynamics between crops and honeybees under climate change in North America. Community Ecology, 26(3), 489–499. https://doi.org/10.1007/s42974-025-00262-5
  39. Rossi, J., Battisti, A., Avtzis, D.Ν., et al. (2025). Warmer and brighter winters than before: Ecological and public health challenges from the expansion of the pine processionary moth (Thaumetopoea pityocampa). The Science of The Total Environment, 978, 179470. https://doi.org/10.1016/j.scitotenv.2025.179470
  40. Santos, J.C.B.D., Ramos, R.S., Carmo, D.D.G.D., et al. (2025). Assessing the impact of climate changes on the distribution of two corn diseases: corn stunt and corn reddening. Canadian Journal of Plant Pathology, 47(6), 608–627. https://doi.org/10.1080/07060661.2025.2533964
  41. Serva, D., Iannella, M., Biondi, M., et al. (2025). Integrating habitat suitability, connectivity, and individual-based models to guide priorities for the creation of a lynx metapopulation in Southeastern Europe. Biological Conservation, 310, 111381. https://doi.org/10.1016/j.biocon.2025.111381
  42. Soares, I.M.N., Oliveira, A.C.D., Antonio, A.I., et al. (2025). Diversity, floral visitation pattern, and conservation of stingless bees (Apidae: Meliponini) in the Brazilian Legal Amazon. Journal for Nature Conservation, 89, 127120. https://doi.org/10.1016/j.jnc.2025.127120
  43. Stefanidis, A., Kougioumoutzis, K., Zografou, K., et al. (2025). Distribution Patterns and Habitat Preferences of Five Globally Threatened and Endemic Montane Orthoptera (Parnassiana and Oropodisma). Ecologies, 6(1), 5. https://doi.org/10.3390/ecologies6010005
  44. Tytar, V., Kozynenko, I., Navakatikyan, M. (2025). A species distribution modelling analysis of Rafflesia pricei (Rafflesiaceae), a parasitic flowering plant endemic to Borneo. Geo&Bio, 2025(27), 215–233. https://doi.org/10.53452/gb2717
  45. Withers, A.J., Croft, S., Budgey, R., et al. (2025). Modelling vector and host distributions to inform potential disease risk: A case study of West Nile virus in the United Kingdom. Medical and Veterinary Entomology, 39(4), 842–862. https://doi.org/10.1111/mve.12825
  46. Zhang, Z., Kass, J.M., Bede‐Fazekas, Á., et al. (2025). Differences in predictions of marine species distribution models based on expert maps and opportunistic occurrences. Conservation Biology, 39(4), e70015. https://doi.org/10.1111/cobi.70015

2024 (n = 49)

  1. Aidoo, O.F., Amaro, G.C., Souza, P.G.C., et al. (2024). Climate change impacts on worldwide ecological niche and invasive potential of Sternochetus mangiferae. Pest Management Science, 81(2), 667–677. https://doi.org/10.1002/ps.8465
  2. Bayraktarov, E., Low‐Choy, S., Singh, A.R., et al. (2024). EcoCommons Australia virtual laboratories with cloud computing: Meeting diverse user needs for ecological modeling and decision-making. Environmental Modelling & Software, 183, 106255. https://doi.org/10.1016/j.envsoft.2024.106255
  3. Branco, M.S.D., Gomes, P.W.P., Xavier-Sampaio, L., et al. (2024). Were Dry Forests widespread in the Pleistocene and what is their fate under climate change? A modelling approach using a specialist plant. Flora, 321, 152629. https://doi.org/10.1016/j.flora.2024.152629
  4. Buebos-Esteve, D.E., Redeña‐Santos, J.C., Dagamac, N.H.A. (2024). Ensemble modeling to identify high conservation value areas for endemic and elusive large-sized mammals of the Philippines. Journal for Nature Conservation, 80, 126657. https://doi.org/10.1016/j.jnc.2024.126657
  5. Castillo, D.S.C., Higa, M. (2024). Strengthening ecologically based rodent management in the Philippines using maximum entropy (MaxEnt) predictions. Journal of Tropical Ecology, 40. https://doi.org/10.1017/s0266467424000208
  6. Chartois, M., Fried, G., Rossi, J. (2024). Climate and host plant availability are favourable to the establishment of Lycorma delicatula in Europe. Agricultural and Forest Entomology, 27(2), 316–328. https://doi.org/10.1111/afe.12665
  7. Cullen, J.A., Domit, C., Lamont, M.M., et al. (2024). A comparative framework to develop transferable species distribution models for animal telemetry data. Ecosphere, 15(12). https://doi.org/10.1002/ecs2.70136
  8. Dorji, S., Stewart, S.B., Shabbir, A., et al. (2024). Comparative Analysis of Mechanistic and Correlative Models for Global and Bhutan-Specific Suitability of Parthenium Weed and Vulnerability of Agriculture in Bhutan. Plants, 14(1), 83. https://doi.org/10.3390/plants14010083
  9. Esparza-Orozco, A., Lira‐Noriega, A. (2024). Use of secondary diversity data to improve diversity estimates at multiple geographic scales. Biodiversity and Conservation, 33(6-7), 2071–2088. https://doi.org/10.1007/s10531-024-02844-7
  10. Fonteyn, W., Serra-Diaz, J.M., Muys, B., et al. (2024). Incorporating Climatic Extremes Using the GEV Distribution Improves SDM Range Edge Performance. Journal of Biogeography, 52(3), 780–791. https://doi.org/10.1111/jbi.15067
  11. Gandaho, S.M., Sogbohossou, E.A., Thompson, L.J. (2024). NIMO : A graphical user interface‐based R package for species distribution modelling. Ecological Solutions and Evidence, 5(3). https://doi.org/10.1002/2688-8319.12385
  12. Habibi, I., Achour, H., Bounaceur, F., et al. (2024). Predicting the future distribution of the Barbary ground squirrel (Atlantoxerus getulus) under climate change using niche overlap analysis and species distribution modeling. Environmental Monitoring and Assessment, 196(11), 1140. https://doi.org/10.1007/s10661-024-13350-2
  13. He, J., Lu, L., He, H., et al. (2024). Estimating the dynamics of ecosystem functions under climate change in a temperate forest region. Ecological Indicators, 166, 112353. https://doi.org/10.1016/j.ecolind.2024.112353
  14. Kass, J.M., Smith, A.B., Warren, D.L., et al. (2024). Achieving higher standards in species distribution modeling by leveraging the diversity of available software. Ecography, 2025(2). https://doi.org/10.1111/ecog.07346
  15. Kougioumoutzis, K., Tsakiri, M., Kokkoris, I., et al. (2024). Assessing the Vulnerability of Medicinal and Aromatic Plants to Climate and Land-Use Changes in a Mediterranean Biodiversity Hotspot. Land, 13(2), 133. https://doi.org/10.3390/land13020133
  16. Kougioumoutzis, K., Constantinou, I.P., Panitsa, M. (2024). Rising Temperatures, Falling Leaves: Predicting the Fate of Cyprus’s Endemic Oak under Climate and Land Use Change. Plants, 13(8), 1109. https://doi.org/10.3390/plants13081109
  17. Lamboley, Q., Fourcade, Y. (2024). No optimal spatial filtering distance for mitigating sampling bias in ecological niche models. Journal of Biogeography, 51(9), 1783–1794. https://doi.org/10.1111/jbi.14854
  18. Lazagabaster, I.A., Thomas, C.D., Spedding, J.V., et al. (2024). Evaluating species distribution model predictions through time against paleozoological records. Ecology and Evolution, 14(10), e70288. https://doi.org/10.1002/ece3.70288
  19. Marom, N., Peretz, A.O., Lazagabaster, I.A., et al. (2024). Water voles of Lake Hula: assessing their past, present, and future. European Journal of Wildlife Research, 70(2). https://doi.org/10.1007/s10344-024-01781-8
  20. Monache, D.D., Martino, G., Chiocchio, A., et al. (2024). Mapping local climates in highly heterogeneous mountain regions: Interpolation of meteorological station data vs. downscaling of macroclimate grids. Ecological Informatics, 82, 102674. https://doi.org/10.1016/j.ecoinf.2024.102674
  21. Nelson, D.L., Marneweck, C.J., McShea, W.J., et al. (2024). Predicted future range expansion of a small carnivore: swift fox in North America. Landscape Ecology, 39(9). https://doi.org/10.1007/s10980-024-01962-5
  22. Nieto‐Lugilde, M., Nieto‐Lugilde, D., Piatkowski, B., et al. (2024). Ecological differentiation and sympatry of cryptic species in the Sphagnum magellanicum complex (Bryophyta). American Journal of Botany, 111(9), e16401. https://doi.org/10.1002/ajb2.16401
  23. Ninsin, K.D., Souza, P.G.C., Amaro, G.C., et al. (2024). Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana. Bulletin of Entomological Research, 114(3), 327–346. https://doi.org/10.1017/s0007485324000105
  24. Noel, A.R., Schlaepfer, D.R., Butterfield, B.J., et al. (2024). Most Pinyon–Juniper Woodland Species Distributions Are Projected to Shrink Rather Than Shift Under Climate Change. Rangeland Ecology & Management, 98, 454–466. https://doi.org/10.1016/j.rama.2024.09.002
  25. Pires, M.B., Kougioumoutzis, K., Norder, S.J., et al. (2024). The future of plant diversity within a Mediterranean endemism centre: Modelling the synergistic effects of climate and land-use change in Peloponnese, Greece. The Science of The Total Environment, 947, 174622. https://doi.org/10.1016/j.scitotenv.2024.174622
  26. Rahimi, E., Jung, C. (2024). Global Trends in Climate Suitability of Bees: Ups and Downs in a Warming World. Insects, 15(2), 127. https://doi.org/10.3390/insects15020127
  27. Rahimi, E., Dong, P., Ahmadzadeh, F., et al. (2024). Assessing climate change threats to biodiversity and protected areas of Iran. European Journal of Wildlife Research, 70(5). https://doi.org/10.1007/s10344-024-01842-y
  28. Rahimi, E., Jung, C. (2024). A Global Estimation of Potential Climate Change Effects on Pollinator-Dependent Crops. Agricultural Research, 14(4), 812–822. https://doi.org/10.1007/s40003-024-00802-x
  29. Rahimi, E., Jung, C. (2024). A New SDM-Based Approach for Assessing Climate Change Effects on Plant–Pollinator Networks. Insects, 15(11), 842. https://doi.org/10.3390/insects15110842
  30. Rahimi, E., Jung, C. (2024). A global evaluation of urban agriculture potential for pollinator‐dependent crops in major cities. Urban Agriculture & Regional Food Systems, 9(1). https://doi.org/10.1002/uar2.20058
  31. Rahimi, E., Jung, C. (2024). Spatial Overlap Between Bees and Pollinator‐Dependent Crops in Europe and North America. Journal of Sustainable Agriculture and Environment, 3(4). https://doi.org/10.1002/sae2.70021
  32. Rahimi, E., Jung, C. (2024). Identifying pollinator‐friendly sites within urban green spaces for sustainable urban agriculture. Journal of Sustainable Agriculture and Environment, 3(3). https://doi.org/10.1002/sae2.12109
  33. Rahimi, E., Jung, C. (2024). Estimating potential climate change effects on pollinating insects: A multi‐taxa study in the Republic of Korea. Entomological Research, 54(12). https://doi.org/10.1111/1748-5967.70010
  34. Rahimi, E., Dong, P., Ahmadzadeh, F. (2024). Assessing climate niche similarity between persian fallow deer (Dama mesopotamica) areas in Iran. BMC Ecology and Evolution, 24(1), 93. https://doi.org/10.1186/s12862-024-02281-8
  35. Ramírez‐Arce, D.G., Ochoa‐Ochoa, L.M., Lira‐Noriega, A., et al. (2024). Reptile Diversity Patterns Under Climate and Land Use Change Scenarios in a Subtropical Montane Landscape in Mexico. Journal of Biogeography, 52(1), 108–121. https://doi.org/10.1111/jbi.15017
  36. Reis, K.H.D.B., Picanço, M.M., Pereira, P.S., et al. (2024). Mapping the potential distribution and invasion risk of Watermelon mosaic virus using MaxEnt ecological niche modeling. Theoretical and Applied Climatology, 156(1). https://doi.org/10.1007/s00704-024-05289-8
  37. Rolph, S., Andrews, C., Carbone, D., et al. (2024). Prototype Digital Twin: Recreation and biodiversity cultural ecosystem services. Research Ideas and Outcomes, 10. https://doi.org/10.3897/rio.10.e125450
  38. Rose, M.B., Velazco, S.J.E., Regan, H.M., et al. (2024). Uncertainty in consensus predictions of plant species’ vulnerability to climate change. Diversity and Distributions, 30(8). https://doi.org/10.1111/ddi.13898
  39. Serra-Diaz, J.M., Borderieux, J., Maitner, B., et al. (2024). occTest : An integrated approach for quality control of species occurrence data. Global Ecology and Biogeography, 33(7). https://doi.org/10.1111/geb.13847
  40. Shitara, T., Aihara, T., Momohara, A., et al. (2024). Are disjunct populations of Betula costata in the Japanese Archipelago glacial relict? An attempt at verification by species distribution modeling. Ecological Research, 40(4), 475–490. https://doi.org/10.1111/1440-1703.12541
  41. Somerville, R.M., MacNeil, C., Lee, F. (2024). Habitat suitability of Aotearoa New Zealand for the recently invaded gold clam ( Corbicula fluminea ). New Zealand Journal of Marine and Freshwater Research, 59(4), 762–779. https://doi.org/10.1080/00288330.2024.2368856
  42. Stefanidis, A., Kougioumoutzis, K., Zografou, K., et al. (2024). Mitigating the extinction risk of globally threatened and endemic mountainous Orthoptera species: Parnassiana parnassica and Oropodisma parnassica. Insect Conservation and Diversity, 18(1), 54–68. https://doi.org/10.1111/icad.12784
  43. Syphard, A.D., Velazco, S.J.E., Rose, M.B., et al. (2024). The importance of geography in forecasting future fire patterns under climate change. Proceedings of the National Academy of Sciences, 121(32), e2310076121. https://doi.org/10.1073/pnas.2310076121
  44. Tytar, V., Kozynenko, I., Pupiņš, M., et al. (2024). Species Distribution Modeling of Ixodes ricinus (Linnaeus, 1758) Under Current and Future Climates, with a Special Focus on Latvia and Ukraine. Climate, 12(11), 184. https://doi.org/10.3390/cli12110184
  45. Tytar, V., Kozynenko, I., Navakatikyan, M. (2024). Modeling the distribution of the proboscis monkey (Nasalis larvatus) in Sabah (Borneo) based on remotely sensed high-resolution global cloud dynamics. Theriologia Ukrainica, 2024(27). https://doi.org/10.53452/tu2711
  46. Urban, D.L. (2024). Species Distribution Modeling. Landscape Ecology, 29–79. https://doi.org/10.1007/978-3-031-72251-6_2
  47. Vélez, D., Vivallo, F. (2024). Key areas for conserving and sustainably using oil-collecting bees (Apidae: Centridini, Tapinotaspidini, Tetrapediini) in the Americas. Journal of Insect Conservation, 28(6), 1247–1263. https://doi.org/10.1007/s10841-024-00620-0
  48. Xu, Q., Wang, X., Yi, J., et al. (2024). Bias correction in species distribution models based on geographic and environmental characteristics. Ecological Informatics, 81, 102604. https://doi.org/10.1016/j.ecoinf.2024.102604
  49. Zhao, H., Xian, X., Yang, N., et al. (2024). A Proposed Coupling Framework of Biological Invasions: Quantifying the Management Prioritization in Mealybugs Invasion. Global Change Biology, 30(11), e17583. https://doi.org/10.1111/gcb.17583

2023 (n = 13)

  1. Amaro, G.C., Fidelis, E.G., Silva, R.S.D., et al. (2023). Effect of study area extent on the potential distribution of Species: A case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae). Ecological Modelling, 483, 110454. https://doi.org/10.1016/j.ecolmodel.2023.110454
  2. Du, Y., Jueterbock, A.O., Firdaus, M., et al. (2023). Niche comparison and range shifts for two Kappaphycus species in the Indo-Pacific Ocean under climate change. Ecological Indicators, 154, 110900. https://doi.org/10.1016/j.ecolind.2023.110900
  3. Franklin, J. (2023). Species distribution modelling supports the study of past, present and future biogeographies. Journal of Biogeography, 50(9), 1533–1545. https://doi.org/10.1111/jbi.14617
  4. Kokkoris, I., Kougioumoutzis, K., Charalampopoulos, I., et al. (2023). Conservation Responsibility for Priority Habitats under Future Climate Conditions: A Case Study on Juniperus drupacea Forests in Greece. Land, 12(11), 1976. https://doi.org/10.3390/land12111976
  5. Mathias, S., Galen, L.G.V., Jarvie, S., et al. (2023). Range reshuffling: Climate change, invasive species, and the case of Nothofagus forests in Aotearoa New Zealand. Diversity and Distributions, 29(11), 1402–1419. https://doi.org/10.1111/ddi.13767
  6. Moura, M.R., Nascimento, F.A.O.D., Paolucci, L.N., et al. (2023). Pervasive impacts of climate change on the woodiness and ecological generalism of dry forest plant assemblages. Journal of Ecology, 111(8), 1762–1776. https://doi.org/10.1111/1365-2745.14139
  7. Moura, M.R., Silva, G.A.O.D., Paglia, A.P., et al. (2023). Climate change should drive mammal defaunation in tropical dry forests. Global Change Biology, 29(24), 6931–6944. https://doi.org/10.1111/gcb.16979
  8. Petersen, W.J., Savini, T. (2023). Lowland forest loss and climate-only species distribution models exaggerate a forest-dependent species’ vulnerability to climate change. Ecological Informatics, 78, 102327. https://doi.org/10.1016/j.ecoinf.2023.102327
  9. Rodriguez, C.S., Rose, M.B., Velazco, S.J.E., et al. (2023). High potential for Brassica tournefortii spread in North American introduced range, despite highly conserved niche. Biological Invasions, 26(1), 337–351. https://doi.org/10.1007/s10530-023-03176-3
  10. Silva, J.P.D., Sousa, R., Gonçalves, D.V., et al. (2023). Streams in the Mediterranean Region are not for mussels: Predicting extinctions and range contractions under future climate change. The Science of The Total Environment, 883, 163689. https://doi.org/10.1016/j.scitotenv.2023.163689
  11. Tytar, V., Nekrasova, O., Pupiņš, M., et al. (2023). Modeling the Distribution of the Chytrid Fungus Batrachochytrium dendrobatidis with Special Reference to Ukraine. Journal of Fungi, 9(6), 607. https://doi.org/10.3390/jof9060607
  12. Velazco, S.J.E., Rose, M.B., Júnior, P.D.M., et al. (2023). How far can I extrapolate my species distribution model? Exploring shape, a novel method. Ecography, 2024(3). https://doi.org/10.1111/ecog.06992
  13. Wang, X., Xu, Q., Liu, J. (2023). Determining representative pseudo-absences for invasive plant distribution modeling based on geographic similarity. Frontiers in Ecology and Evolution, 11. https://doi.org/10.3389/fevo.2023.1193602

2022 (n = 2)

  1. Rose, M.B., Velazco, S.J.E., Regan, H.M., et al. (2022). Rarity, geography, and plant exposure to global change in the California Floristic Province. Global Ecology and Biogeography, 32(2), 218–232. https://doi.org/10.1111/geb.13618
  2. Zhang, X., Huang, Q., Liu, P., et al. (2022). Geography, ecology, and history synergistically shape across-range genetic variation in a calanoid copepod endemic to the north-eastern Oriental. Evolution, 77(2), 422–436. https://doi.org/10.1093/evolut/qpac043