Beware of Bear? Long-Term Spatio-Temporal Patterns of Human-Bear Conflict in Connecticut

被引:0
|
作者
Berkowitz, Zachary [1 ,2 ]
Bravo, Larissa Montas [3 ]
Sen Roy, Shouraseni [4 ]
机构
[1] Univ Miami, Abess Ctr Ecosyst Sci & Policy, Coral Gables, FL USA
[2] Univ Oxford, Sch Geog & Environm, Oxford, England
[3] Univ Miami, Univ Miami Lib, Dept Res Data & Open Scholarship, Coral Gables, FL USA
[4] Univ Miami, Dept Geog & Sustainable Dev, Coral Gables, FL 33146 USA
关键词
Bears; Emerging hot spots analysis; Human-bear conflict; Forest-based and boosted classification regression; Connecticut; AMERICAN BLACK BEARS; HUMAN-WILDLIFE CONFLICT; FOOD AVAILABILITY; LARGE CARNIVORE; MANAGEMENT; HIBERNATION; DENSITY; FLORIDA; CONSERVATION; CHALLENGES;
D O I
10.1007/s00267-024-02094-x
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, we examine the spatio-temporal patterns of citizen-reported human-bear conflict (HBC) from 2002 to 2022 and use the Forest-Based and Boosted Classification (FBBC) technique to assess the significance of several factors in the occurrence of HBC. Our analysis reveals a significant increase in HBC incidents over the study period, with the fewest conflicts in 2002 (217) and the most in 2022 (4455). These were concentrated in northwestern Connecticut, particularly eastern Litchfield County and western Hartford County. The results of geostatistical analysis, including measures of dispersion and emerging hot spot analysis indicated a southward trend in HBC on both annual and monthly scales. The validation results of the FBBC highlighted the relevance of forest fragmentation, intermediate housing density, proximity to water bodies, and snowfall in predicting HBC. Each variable demonstrated nearly equal importance (20%) in predicting HBC occurrences from 2010 to 2022, though land cover showed no significant predictive power. These findings elucidate the spatio-temporal dynamics of HBC and offer valuable insights for wildlife managers to prioritize conflict mitigation strategies effectively. The results of this study identify locations prone to HBC. Moreover, FBBC results show that this technique can be used to predict future HBC based on projected changes in these variables due to climate change and expansion of the human-wildlife interface. Our analysis can aid in the development of targeted, evidence-driven, and ethical management interventions in Connecticut.
引用
收藏
页码:638 / 653
页数:16
相关论文
共 50 条
  • [31] Assessment of long-term spatio-temporal radiofrequency electromagnetic field exposure
    Aerts, Sam
    Wiart, Joe
    Martens, Luc
    Joseph, Wout
    ENVIRONMENTAL RESEARCH, 2018, 161 : 136 - 143
  • [32] Spatio-temporal variation and trends of long-term meteorological variables in Nigeria
    Israel, Emmanuel
    David, Adedayo Kayode
    Omolara, Emmanuel Grace
    ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (24)
  • [33] Spatio-temporal variation and trends of long-term meteorological variables in Nigeria
    Emmanuel Israel
    Adedayo Kayode David
    Emmanuel Grace Omolara
    Arabian Journal of Geosciences, 2020, 13
  • [34] Long-term analysis of spatio-temporal patterns in population dynamics and demography of juvenile Pinfish (Lagodon rhomboides)
    Chacin, D. H.
    Switzer, T. S.
    Ainsworth, C. H.
    Stallings, C. D.
    ESTUARINE COASTAL AND SHELF SCIENCE, 2016, 183 : 52 - 61
  • [35] Spatio-temporal exploration strategies for long-term autonomy of mobile robots
    Santos, Joao Machado
    Krajnik, Tomas
    Duckett, Tom
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2017, 88 : 116 - 126
  • [36] Spatio-temporal trend analysis of long-term development patterns (1900-2030) in a Southern Appalachian County
    Kirk, Ryan W.
    Bolstad, Paul V.
    Manson, Steven M.
    LANDSCAPE AND URBAN PLANNING, 2012, 104 (01) : 47 - 58
  • [37] Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation
    Vintr, Tomas
    Blaha, Jan
    Rektoris, Martin
    Ulrich, Jiri
    Roucek, Tomas
    Broughton, George
    Yan, Zhi
    Krajnik, Tomas
    FRONTIERS IN ROBOTICS AND AI, 2022, 9
  • [38] Learning Dynamic Interactions and Long-term Patterns with Spatio-Temporal Graphs for Multi-Vessel Trajectory Prediction
    Zhang, Xiliang
    Liu, Jin
    Chen, Kejie
    Gong, Peizhu
    Liu, Yuxin
    Wu, Zhongdai
    IEEE Transactions on Intelligent Vehicles, 2024, : 1 - 16
  • [39] Patterns of Human-Brown Bear Conflict in the Urban Area of Brasov, Romania
    Cimpoca, Alina
    Voiculescu, Mircea
    SUSTAINABILITY, 2022, 14 (13)
  • [40] Long-term moving object segmentation and tracking using spatio-temporal consistency
    Zhong, D
    Chang, SF
    2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2001, : 57 - 60