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Mumini Dzoga Danny Mulala Simatele Cosmas Munga Shadrack Yonge 《Ocean Science Journal》2020,55(2):193-201
Natural resource management frameworks are important in generating information that promotes the development of appropriate policies and regulation for eff 相似文献
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蒲城水库水质污染与富营养化评价和预测的研究 总被引：5，自引：3，他引：5
本文对蒲城水库污染负荷量进行了动态平衡研究,开展了物理、化、学、放射性、水生生物和富营养化等多项指标的监测分析,采用多种方法对水库水质污染现状与富营养化程度作出了综合评价和中、长期预测,提出了水质保护对策。研究结果表明:30年后蒲城水库水质除pH、总锰等个别项目可能超标外,其它水质指标均符合地面水三类标准;总氮、总磷浓度各低于0.40mg/L和0.03mg/L,水库仍将处于中—富营养型。但10年后的夏季总氮、总磷浓度将会分别超过0.50mg/L和0.35mg/L,存在富营养化的危险。 相似文献
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Tawanda W. Gara Tiejun Wang Andrew K. Skidmore Shadrack M. Ngene Timothy Dube Mbulisi Sibanda 《国际地球制图》2017,32(11):1243-1253
Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R2 < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R2 values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively. 相似文献
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Neil D. Burgess Shadrack Mwakalila Pantaleo Munishi Marion Pfeifer Simon Willcock Deo Shirima Seki Hamidu George B. Bulenga Jason Rubens Haji Machano Rob Marchant 《Global Environmental Change》2013,23(5):1349-1354
Norwegian funded REDD+ projects in Tanzania have attracted a lot of attention, as has the wider REDD+ policy that aims to reduce deforestation and degradation and enhance carbon storage in forests of the developing countries. One of these REDD+ projects, managed by WWF Tanzania, was criticised in a scientific paper published in GEC, and consequently in the global media, for being linked to attempted evictions of communities living in the Rufiji delta mangroves by the Government of Tanzania, allegedly to make the area ‘ready for REDD’. In this response, we show how this eviction event in Rufiji mangroves has a history stretching back over 100 years, has nothing to do with REDD+ or any policy changes by government, and is not in any way linked to the work of any WWF project in Tanzania. We also outline some of the broader challenges faced by REDD+ in Tanzania. 相似文献
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Parinaz Rashidi Andrew Skidmore Tiejun Wang Roshanak Darvishzadeh Shadrack Ngene Anton Vrieling 《International journal of geographical information science》2018,32(3):622-636
Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required. 相似文献
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