Вход на сайт

Просмотр новости

Найдите то, что Вас интересует

Integrating environmental and anthropogenic drivers in MaxEnt models to understand the spatial patterns of wildlife crime

Дата публикации: 06-07-2026 00:00:00

Wildlife criminal activity exhibits distinct spatial patterns influenced by environmental and anthropogenic drivers. This study applies Maximum Entropy (MaxEnt) modelling, a presence-only and robust machine learning technique originally designed for species distribution, to estimate the potential distribution of suitable sites based on recorded wildlife crime incidents, in Gonarezhou National Park (GNP), Zimbabwe. A total of 1,305 georeferenced crime incident locations were analysed alongside a suite of environmental predictors including elevation, slope, Normalised Difference Vegetation Index (NDVI), distance to roads, distance to settlements, distance to water bodies, and distance to park boundary. The model achieved an Area Under Curve (AUC) of 0.9, indicating excellent predictive performance. Among the predictors, elevation, distance to settlements and roads emerged as the most influential variables. The spatial distribution of crime suite ability revealed heightened crime risk near park boundaries and adjacent communities, reflecting the interplay between terrain, accessibility, and land-use gradients as key determinants for wildlife crime. These findings highlight the value of integrating ecological modelling techniques into conservation criminology and support the implementation of spatially targeted law enforcement strategies within protected areas. The resulting surface reflects spatial patterns of recorded incidents influenced by patrol detection effort rather than unbiased estimates of actual crime occurrence.

Основное содержимое страницы с новостью.

Abstract

Wildlife criminal activity exhibits distinct spatial patterns influenced by environmental and anthropogenic drivers. This study applies Maximum Entropy (MaxEnt) modelling, a presence-only and robust machine learning technique originally designed for species distribution, to estimate the potential distribution of suitable sites based on recorded wildlife crime incidents, in Gonarezhou National Park (GNP), Zimbabwe. A total of 1,305 georeferenced crime incident locations were analysed alongside a suite of environmental predictors including elevation, slope, Normalised Difference Vegetation Index (NDVI), distance to roads, distance to settlements, distance to water bodies, and distance to park boundary. The model achieved an Area Under Curve (AUC) of 0.9, indicating excellent predictive performance. Among the predictors, elevation, distance to settlements and roads emerged as the most influential variables. The spatial distribution of crime suite ability revealed heightened crime risk near park boundaries and adjacent communities, reflecting the interplay between terrain, accessibility, and land-use gradients as key determinants for wildlife crime. These findings highlight the value of integrating ecological modelling techniques into conservation criminology and support the implementation of spatially targeted law enforcement strategies within protected areas. The resulting surface reflects spatial patterns of recorded incidents influenced by patrol detection effort rather than unbiased estimates of actual crime occurrence.

Access this article

Log in via an institution

Subscribe and save

  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request .

References

  • Anagnostou M, Moreto WD, Gardner CJ, Doberstein B (2021) Poverty, pandemics, and wildlife crime. Conserv Soc 19(4):294–306

    Article  Google Scholar 

  • Anderson DR, Burnham KP, White GC (1998) Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies. J Applied Statistics 25(2):283–282

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1981) Environmental criminology. Sage, London

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1993) Nodes, paths, and edges: Considerations on the complexity of crime and the physical environment. Classics Enviro Criminol, ental (8) pp. 289–326

  • Brantingham PL, Brantingham PJ (2017) Environment, routine, and situation: Toward a pattern theory of crime. In Ronald V. Clarke & Marcus Felson (Eds) Routine activity and rational choice. Routledge, pp 259–294

  • Chadwick FJ et al (2024) LIES of omission: complex observation processes in ecology. Trends Ecol Evol 39(4):368–380

    Article  PubMed  Google Scholar 

  • Cohen LE, Felson M (2010) Social change and crime rate trends: A routine activity approach (1979). In M. A. Anderson, P. J. Brantingham & B. J. Kinney (Eds). Classics in environmental criminology. Routledge, Milton Park, pp 203–232

  • Cohen LE, Marcus F (1979) Social change and crime rate trends: In M.A. Anderson, P. J. Brantingham, &J.B. Kinney (Eds). A routine activity approach. Am Sociol Rev, pp. 588–608

  • Cowan D et al (2020) Applying crime pattern theory and risk terrain modeling to examine environmental crime in Cambodia. J Contemp Crim Justice 36(3):327–350

    Article  Google Scholar 

  • Critchlow R et al (2015) Spatiotemporal trends of illegal activities from ranger-collected data in a Ugandan national park. Conserv Biol 29(5):1458–1470

    Article  CAS  PubMed  Google Scholar 

  • Cunliffe R, Tom M, Mapaura A (2012) Vegetation survey of Gonarezhou National Park, Zimbabwe. Zimbabwe Parks and Wildlife Management Authority, Harare

    Google Scholar 

  • Da Re D et al (2023) USE it: Uniformly sampling pseudo-absences within the environmental space for applications in habitat suiteability models. Methods Ecol Evolutions 14(11):2873–2887

    Article  Google Scholar 

  • Dudley N, Stolton S, Wendy E (2013) Wildlife crime poses unique challenges to protected areas. Parks 19(1):7–12

    Article  Google Scholar 

  • Duffy R (2022) Crime, security, and illegal wildlife trade: Political ecologies of international conservation. Glob Environ Politics 22(2):23–44

    Article  Google Scholar 

  • Elith J et al (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57

    Article  Google Scholar 

  • Esmail N et al (2019) Emerging illegal wildlife trade issues in 2018: a global horizon scan. Conserv Lett 13(4):e12715

    Article  Google Scholar 

  • Gavin M, Solomon J, Blank S (2010) Measuring and monitoring illegal use of natural resources. Conserv Biol 24(1):89–100

    Article  PubMed  Google Scholar 

  • Goga T et al (2025) Detection of potential illegal environmental activities in Slovakia based on earth observation data. J Maps 21(1):2464054

    Article  Google Scholar 

  • Gore ML, Escouflaire L, Wieland M (2021) Sanction avoidance and the illegal wildlife trade: A case study of an urban wild meat supply chain. J Illicit Economies Dev 3(1):43–67

  • Hansen AJ, DeFries R (2007) Ecological mechanisms linking protected areas to surrounding lands. Ecol Appl 17(4):974–988

    Article  PubMed  Google Scholar 

  • Huberman DB (2020) Advances in Spatial Statistics for Ecological and Environmental Data. North Carolina State University, North Carolina

    Google Scholar 

  • Kahler JS, Gore ML (2015) Local perceptions of risk associated with poaching of wildlife implicated in human-wildlife conflicts in Namibia. Biol Conserv 189:49–58

    Article  Google Scholar 

  • Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A Comparison Between Ensemble and MaxEnt Species Distribution Modelling Approaches for Conservation: A Case Study With Egyptian Medicinal Plants. Ecol Inf 60:101150

    Article  Google Scholar 

  • Kurland J, Pires S, Moreto W (2014) Wildlife crime: a conceptual integration, literature review, and methodological critique. Crime Sci 3(1):1–15

    Google Scholar 

  • Lemieux A, Clarke R (2009) The international ban on ivory sales and its effects on elephant poaching in Africa. Br J Criminol 49(4):451–471

    Article  Google Scholar 

  • Lindsey P et al (2015) Illegal hunting and the bush-meat trade in Savanna Africa. Zoological Society of London Wildlife Conservation Society, Panthera

    Google Scholar 

  • Low BW, Yiwen Z, Heok HT, Yeo DCJ (2021) Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species. Divers Distrib 27(3):497–511

    Article  Google Scholar 

  • Manel S, Jean-Marie D, Ormerod SJ (1999) Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecol Model 120(2–3):337–347

    Article  Google Scholar 

  • Moreto WD (2016) Occupational stress among law enforcement rangers: Insights from Uganda. Oryx 50(4):646–654

    Article  Google Scholar 

  • Moreto WD, Lemieux AM (2015) Poaching in Uganda: Perspectives of law enforcement rangers. Deviant Behav 36(11):853–873

    Article  Google Scholar 

  • Muboko N (2011) Conflict and sustainable development: the case of the Great Limpopo Transfrontier Park (GLTP); Southern Africa. Nelson Mandela Metropolitan University, Port Elisabeth

    Google Scholar 

  • Murwendo T, Murwira A, Masocha M (2023) Vegetation phenology patterns in semi-arid savannah woodlands of Gonarezhou National Park, Southeastern Zimbabwe. Int J Geoheritage Parks, 11(2) pp. 298–309

  • Mutanga CN, Gandiwa E, Muboko N (2017) An analysis of tourist trends in northern Gonarezhou National Park, Zimbabwe. Cogent Social Sci 3(1):1392921

    Google Scholar 

  • Muzhingi DT (2021) Environmental niche modelling of elephant poaching sites in the Matusadona National Park. University of Johannesburg, Zimbabwe. Johannesburg

    Google Scholar 

  • Naimi B et al (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37(4):191–203

    Article  Google Scholar 

  • Pearce J, Simon F (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245

    Article  Google Scholar 

  • Phillips SJ, Miroslav D (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2):161–175

    Article  Google Scholar 

  • Pires SF, Moreto WD (2017) Preventing wildlife crimes: Solutions that can overcome the ‘Tragedy of the Commons’. Transnational environmental crime, pp. 419–442

  • R Studio Team (2023) RStudio: Integrated Development Environment for R. RStudio, PBC. Available at https://posit.co/products/open-source/rstudio/. Accessed 20 July 2025

  • Raschka S (2018) Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv Volume 1811:12808

    Google Scholar 

  • Reineking B (2006) Constrain to perform: regularization of habitat models. Ecol Model 3(4):675–690

    Article  Google Scholar 

  • Schmitt S et al (2017) ssdm: An r package to predict distribution of species richness and composition based on stacked species distribution models. Methods Ecol Evol 8:1795–1803

    Article  Google Scholar 

  • Shaminja TS, Igyo JA, Terhile AJ (2025) Crimes Against Biodiversity in Nigeria: A Search for Sustainable Development. Torkwase J Agricultural Res 2(1):1–15

    Google Scholar 

  • Skidmore A (2023) Exploring the motivations associated with the poaching and trafficking of Amur tigers in the Russian Far East. Deviant Behav 44(3):331–358

    Article  Google Scholar 

  • Solomon JN, Gavin MC, Gore ML (2015) Detecting and understanding non-compliance with conservation rules. Biol Conserv 189:1–4

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    Article  CAS  PubMed  Google Scholar 

  • Tavuyanago B (2016) Living on the fringes of a protected area: Gonarezhou National Park (GNP) and the indigenous communities of South East Zimbabwe 1934–2008. University of Pretoria, Pretoria

    Google Scholar 

  • Titurus B, Friswell MI (2008) Regularization in model updating. Int J Numer Methods Eng 75(4):440–478

    Article  Google Scholar 

  • Valavi R, Guillera-Arroita G, Lahoz‐Monfort JJ, Elith J (2022) Predictive Performance of Presence‐Only Species Distribution Models: A Benchmark Study With Reproducible Code. Ecol Monogr 92:e01486

    Article  Google Scholar 

  • Walker JT (2017) Advancing science and research in criminal justice/criminology: Complex systems theory and non-linear analyses. In J. T. Walker (Ed.), Social, Ecological and Environmental Theories of Crime. Routledge, Oxfordshire, pp 499–525

  • Wyatt T, van Uhm D, Nurse A (2020) Differentiating criminal networks in the illegal wildlife trade: organised, corporate and disorganised crime. Trends Organised Crime 23(4):350–366

    Article  Google Scholar 

Download references

Funding

The authors have no relevant financial or non-financial interests to disclose. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Geography, Geospatial Sciences and Earth Observation, University of Zimbabwe, Harare, Zimbabwe

    Cliff Jawe, Mark Zvidzai, Honour Chinoitezvi, Paul Dzikiti & Ratidzo Blessing Mapfumo

  2. International Conservation Affairs Department, Zimbabwe Parks and Wildlife Management Authority, Harare, Zimbabwe

    Patience Gandiwa

  3. Scientific Services, Parks and Wildlife Management Authority, Causeway, Zimbabwe

    Honour Chinoitezvi

Authors

  1. Cliff Jawe
  2. Mark Zvidzai
  3. Honour Chinoitezvi
  4. Patience Gandiwa
  5. Paul Dzikiti
  6. Ratidzo Blessing Mapfumo

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cliff Jawe, Mark Zvidzai, Paul Dzikiti, Ratidzo Blessing Mapfumo and Fadzai Michelle Zengeya. The first draft of the manuscript was written by [Cliff Jawe] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mark Zvidzai.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Communicated by Vinicius R. Tonetti

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Jawe, C., Zvidzai, M., Chinoitezvi, H. et al. Integrating environmental and anthropogenic drivers in MaxEnt models to understand the spatial patterns of wildlife crime. Biodivers Conserv 35, 207 (2026). https://doi.org/10.1007/s10531-026-03414-9

Download citation

  • Received: 14 October 2025

  • Revised: 31 May 2026

  • Accepted: 27 June 2026

  • Published: 06 July 2026

  • Version of record: 06 July 2026

  • DOI: https://doi.org/10.1007/s10531-026-03414-9

Keywords

Схожие новости

#Наименование новостиТональностьИнформативностьДата публикации
1Deep learning-based computer vision in forest monitoring and management: a systematic review0806-07-2026
2Landscape attributes for all of Brazil’s threatened primates are similar to those listed among the World’s 25 most endangered primates0725-06-2026
3Individual variation in leopard () prey composition in Namibian farmlands highlights the importance of wild prey over livestock0708-07-2026
4Wildfire smoke alters observations of 65% of breeding bird species in New York State0803-07-2026
5Projections of suitable habitat loss and its implications in conservation for endemic non-pseudanthial Euphorbioideae (Euphorbiaceae) species in Northeastern Brazil under climate change scenarios0808-07-2026
6Matching the largest Neotropical primates with their food resources: Rapid suitability declines in light of climate change0825-06-2026
7Flagship species as subterranean monitoring tools: a case study in anchialine ecosystems from Lanzarote (Canary Islands)0808-07-2026
8Not just a room for bees: exploring the complex ecology of insect hotel communities0806-07-2026
9Ученые протестируют систему прогнозирования лесных пожаров в двух селах вблизи Байкала0020-11-2018
10Исследование: почему молчать, гуляя по лесу, опасно для жизни0512-07-2026

Классификация: . Схожих патентов: 0. Схожих новостей: 10. Тональность: 0. Информативность: 7. Источник: link.springer.com.