Do Wildlife Management Areas Help to Mitigate Negative Human-Wildlife Interactions? A Case of Eastern Bufferzone of Selous Game Reserve, Tanzania

Tropical Conservation Science, Journal Year: 2025, Volume and Issue: 18

Published: May 1, 2025

Background and Research Aims: Negative interactions with wildlife hinder biodiversity conservation, attempts to devolve management local communities near protected areas have had limited success. This study applied Citizen Science Theory (CST) as a bottom-up approach examine the role of Wildlife Management Areas (WMAs) in reducing human-wildlife conflicts eastern buffer zone Selous Game Reserve. Methods: Data were collected from 240 households through surveys, focus group discussions, key informant interviews. Qualitative data analyzed via content analysis, while quantitative processed using SPSS version 26. Results: Following establishment WMAs, 67.5% 83% respondents reported decreases illegal offtake habitat destruction, respectively. However, incidents crop damage, livestock predation, human attacks caused by wild animals increased 28.6%, 40.6%, 79.2%, Regarding interaction patterns, annual damage was higher averaging 7.2±0.1 acres, compared 0.6±0.1 acres farther away. Similarly, approximately 83 attacked within 500 meters WMA boundaries, only 11 beyond 30 kilometers. About 72% indicated that occurred primarily during farming activities, lions, leopards, hyenas, crocodiles, elephants, buffalo, hippopotamuses identified responsible species. Conclusion: We concluded expansion habitats community-based conservation populations; however, it also heightened challenges posed communities. I mplications for conservation: WMAs inception is paramount particularly those requiring large home ranges. Enhancing control encouraging monitoring, sound odor deterrents, promoting public education on safely interacting animals, strengthening traditional enclosures predator-proof materials such thorny bushes or modern steel fences reduce nighttime attacks, should be prioritized mitigate negative wildlife.

Language: Английский

---

Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

Remote Sensing in Ecology and Conservation, Journal Year: 2021, Volume and Issue: 8(2), P. 236 - 250

Published: Nov. 9, 2021

Abstract We develop a machine learning approach to detect and discriminate elephants from other species, recognise important behaviours such as running rumbling, based only on seismic data generated by the animals. demonstrate our using acquired in Kenyan savanna, consisting of 8000 h recordings 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained seismograms spectrograms, achieved 80%–90% balanced accuracy detecting up 100 m away, over 90% recognising rumbling data. release dataset used this study: SeisSavanna represents unique collection signals with associated wildlife species behaviour. results suggest that offer substantial benefits for monitoring wildlife, we propose further methods dense arrays could result shift monitoring.

Language: Английский

---

Unmanned aircraft systems for protected areas: Gadgetry or necessity?

Journal for Nature Conservation, Journal Year: 2021, Volume and Issue: 64, P. 126078 - 126078

Published: Oct. 1, 2021

The ease of use and availability unmanned aircraft systems (UAS) recently pervaded a wide range topics applications. In nature conservation for the management protected areas (PAs), UAS are still not an established approach compared to other methods such as satellite-based remote sensing, although several research articles have already discussed their use. this context, even denoted 'conservation drones', suggesting that is beneficial in terms accomplishing various tasks land-cover mapping, vegetation monitoring, biomass estimation, animal detection. However, disturbance wildlife or issues caused by debated guidelines studies suggest precautionary measures, implications PAs has been analyzed detail yet. Therefore, reviewing articles, present paper aims show whether relevant irrelevant PA biodiversity conservation, considers controversial debate potential threats, investigates type concerned matters context. We showed majority (73%) selected (89) report biodiversity. most these did consider impacts on environment. possibility disturbances was 15 (approx. 17%) reviewed works, which concluded effects were negligible non-existent. Only three 3%) impact demonstrated. While discussing do nor mention any impacts, banned many PAs. drones' related pros cons need be carefully considered managers stakeholders concerned.

Language: Английский

---

Application of UAS for Monitoring of Forest Ecosystems

Croatian journal of forest engineering, Journal Year: 2022, Volume and Issue: 43(2), P. 487 - 504

Published: April 26, 2022

In the last couple of years, there have been a great number articles that cover and emphasize advantages possibilities UAS (Unmanned Air System) offers in forest ecosystem research. available research, alongside UAS, importance developing sensors are designed to be used with UAV (Unamnned Vehicle), flight programming software collected data processing pointed out. With widespread use high-precision accompanying forestry, it is possible obtain accurate short time replaces long-term manpower field equal or some cases, such as windthrow calculation wildlife counting, greater accuracy. The former practice manual imagery being partly replaced automated approaches. paper analyses studies deal form application e.g. inventory, operations, ecological monitoring, pests fires, monitoring. large applying mapping vegetation individual trees, morphological research parts surface analysis, etc. remote proximal sensing technologies engineering has mainly focused on defining roughness topology, road geometry, planning maintenance, ground-based cable-based harvesting soil characteristics displacement. Wildfire monitoring already relies heavily thermal cameras similar directions spread certain insects important for forestry. numerous abundance terrestrial birds mammals using imagery. drawbacks disturbance limited range, common most processed positive attitudes regarding forestry which slowly becoming operative practice, scientists’ focus approaches processing. Reducing error by improving technological will long run reduce people required collect risks cases increase

Language: Английский

---

Long-term monitoring in transition: Resolving spatial mismatch and integrating multistate occupancy data

Ecological Indicators, Journal Year: 2022, Volume and Issue: 146, P. 109815 - 109815

Published: Dec. 29, 2022

The success of long-term wildlife monitoring programs can be influenced by many factors and study designs often represent compromises between spatial scales costs. Adaptive iteratively manage this tension adopting new cost-efficient technologies, which provide projects the opportunity to reallocate costs address hypotheses, adapt changing ecological conditions, or adjust sampling scale resolution. If there is interest in longer time series data, methodological transitions may necessitate integrated models link newer data with historical data. However, integration difficult if temporal are mismatched. Here, we develop an multistate site-occupancy model resolve sample unit mismatch datasets from two northern spotted owl (Strix occidentalis caurina) schemes that broadly overlapped during a transition. first dataset was obtained decades-long program using call-playback mark-resight surveys on territories varying size shape. This has recently transitioned passive acoustic randomly selected 5-km2 hexagons over larger extents. Both areas barred varia), invasive competitor played important role declines, were being removed experimentally. Reconciling substantially increased representation integrating precision use paired occupancy estimates relative single estimates. Estimates pair across area lower than previous territory-based based surveys. Our further showed concurrent removal experiment landscape site pairs owls. empirical application modelling approach demonstrates useful analytical framework for efforts undergoing (e.g. mark-recapture non-invasive population monitoring). allows maintain continuity objectives transitions, rigorously incorporate findings, adaptively respond conditions.

Language: Английский

---