石羊河流域降水化学特征时空变化及来源浅析

运用相关分析、因子分析、富集因子和HYSPLIT模型对石羊河流域从2013年7月到2014年7月连续收集降水样品的主要离子浓度特征及来源进行了探讨分析,结果表明,Ca~(2+)和Na~+是主要的阳离子,SO_4~(2-)和NO_3~-是主要的阴离子,石羊河流域的主要降水类型为SO_4~(2-)-NO_3~--Ca~(2+);石羊河流域的总离子浓度的季节大小变化顺序为冬季春季秋季夏季,石羊河流域的降水水化学主要受地壳源和人为源的影响;石羊河流域的降水类型分为季风降水、西风降水和混合降水,而混合降水是石羊河流域的主要降水类型,其次为西风降水,最后为季风降水。     

降水梯度序列下柠条林地土壤养分的空间变异

通过野外实地调查结合室内分析,研究陕西省境内不同降雨条件下的黄土高原同级树龄柠条林地土壤养分。结果表明,研究区土壤养分含量低,养分中有机质和全氮在降水梯度上的空间变异性显著;有机质和全氮含量在土壤剖面上表现为随着土层的加深逐渐减少,全磷含量在土壤剖面上表现为从表层到心土层逐渐降低,从心土层到底层逐渐增加;全钾含量在剖面上的规律与有机质和全氮相反;有机质、全氮、全磷、全钾含量均随着降水梯度的增加而增加;有机质含量与全氮、全钾呈现极显著正相关,与全磷呈现显著正相关关系。     

长江口及其邻近海域沉积物磷的赋存形态和空间分布

采用SMT磷分级法研究长江口及其邻近海域沉积物中磷的赋存形态及分布特征,并探讨了其影响因子.研究结果表明,沉积物中总磷(TP)含量为476.91~980.04μg·g~(-1),有机磷(OP)含量为15.84~139.21μg·g~(-1);而无机磷(IP)为主要赋存形态,其含量为421.67~844.86μg·g~(-1),约占TP 72.9%~96.7%.IP中以钙结合态磷(HCl-P)为主,其含量为186.88~431.69μg·g~(-1),占IP的42.3%~57.79%;而铁/锰结合态磷(Na OH-P)的含量最少,其含量为4.17~52.76μg·g~(-1),仅占IP的2.0%~3.3%.空间分布上,不同形态磷含量具有明显的时空变化特征.夏季TP和IP的含量由咸水端到淡水端呈现出先降低后升高,冬季则先升高后降低的趋势.OP的含量在夏季时无明显分布规律,而在冬季时表现出由咸水端到淡水端降低的趋势.Na OH-P含量在冬夏季均表现为淡水端处较高;HCl-P含量在夏季无明显的变化规律,而冬季时近淡水端处较高.沉积物粒径大小影响磷的赋存形态,表现为夏季粘粒对OP影响较大;冬季粘粒和粉粒与IP存在着负相关性,与OP存在着显著的正相关性.此外,沉积物总有机碳、盐度和碱性磷酸酶活性也是影响磷赋存形态的主要因子.     

梁子湖沉积物重金属污染现状分析及风险评价

采集了梁子湖柱状沉积物,分析了Cd、Sn、As、Cu、V、Zn、Ni、Cr、Co、TI、Pb和Mo 12种重金属元素的含量及空间分布,并对其污染源进行解析,最后对重金属的生态风险作出评价.结果表明,在空间分布上,东部湖区存在严重的重金属污染问题,其中,Cd的平均含量达到0.80 mg·kg~(-1),是湖北省土壤背景值的4.66倍;Sn和As平均含量分别为6.35 mg·kg~(-1)和35 mg·kg~(-1),已经超过湖北省土壤背景值近2倍;在垂直分布上,Cd和Zn在0~20 cm深度上富集现象明显,平均含量分别为0.67 mg·kg~(-1)和116 mg·kg~(-1).富集系数EF值表明,Cd、Sn、As主要来自人为污染.单一重金属潜在生态风险指数Eri值范围在3~140之间,以Cd污染最严重,Eri平均值为140,表现为较重生态风险,其他11种元素均为低等生态风险水平.综合生态风险指数RI值显示,梁子湖整体处于中度污染水平,其中,东部湖区风险程度最大.梁子湖作为武汉市备用水源地,沉积物重金属Cd、Sn、As含量过高将威胁湖水质量,危及水生态安全和人体健康.     

Protected‐Area Boundaries as Filters of Plant Invasions

Abstract: Human land uses surrounding protected areas provide propagules for colonization of these areas by non‐native species, and corridors between protected‐area networks and drainage systems of rivers provide pathways for long‐distance dispersal of non‐native species. Nevertheless, the influence of protected‐area boundaries on colonization of protected areas by invasive non‐native species is unknown. We drew on a spatially explicit data set of more than 27,000 non‐native plant presence records for South Africa's Kruger National Park to examine the role of boundaries in preventing colonization of protected areas by non‐native species. The number of records of non‐native invasive plants declined rapidly beyond 1500 m inside the park; thus, we believe that the park boundary limited the spread of non‐native plants. The number of non‐native invasive plants inside the park was a function of the amount of water runoff, density of major roads, and the presence of natural vegetation outside the park. Of the types of human‐induced disturbance, only the density of major roads outside the protected area significantly increased the number of non‐native plant records. Our findings suggest that the probability of incursion of invasive plants into protected areas can be quantified reliably.     

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. We used data from an 8‐year study in Savé Valley Conservancy, Zimbabwe, to show how geographic profiling—a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology—can be adapted for use in investigations of wildlife crime. The data set contained information on over 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. We used a subset of data for which the illegal hunters’ identities were known. Our model identified the illegal hunters’ home villages based on the spatial locations of the hunting incidences (e.g., snares). Identification of the villages was improved by manipulating the probability surface inside the conservancy to reflect the fact that although the illegal hunters mostly live outside the conservancy, the majority of hunting occurs inside the conservancy (in criminology terms, commuter crime). These results combined with rigorous simulations showed for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns, for example, where landscape heterogeneity means some parts of the study area are less likely to be used (e.g., aquatic areas for terrestrial animals) or where landscape permeability differs (e.g., forest bats tend not to fly over open areas). More broadly, these results show how geographic profiling can be used to target antipoaching interventions more effectively and more efficiently and to develop management strategies and conservation plans in a range of conservation scenarios.     

Rank aggregation of local expert knowledge for conservation planning of the critically endangered saola

There has been much recent interest in using local knowledge and expert opinion for conservation planning, particularly for hard‐to‐detect species. Although it is possible to ask for direct estimation of quantities such as population size, relative abundance is easier to estimate. However, an expert's knowledge is often geographically restricted relative to the area of interest. Combining (or aggregating) experts’ assessments of relative abundance is difficult when each expert only knows a part of the area of interest. We used Google's PageRank algorithm to aggregate ranked abundance scores elicited from local experts through a rapid rural‐appraisal method. We applied this technique to conservation planning for the saola (Pseudoryx nghetinhensis), a poorly known bovid. Near a priority landscape for the species, composed of 3 contiguous protected areas, we asked groups of local people to indicate relative abundances of saola and other species by placing beans on community maps. For each village, we used this information to rank areas within the knowledge area of that village for saola abundance. We used simulations to compare alternative methods to aggregate the rankings from the different villages. The best‐performing method was then used to produce a single map of relative abundance across the entire landscape, an area larger than that known to any one village. This map has informed prioritization of surveys and conservation action in the continued absence of direct information about the saola.     

A multiscale hierarchical Markov transition matrix model for generating and analyzing thematic raster maps

A model is described for generating hierarchically scaled spatial pattern as represented in a thematic raster map. The model involves a series of Markov transition matrices, one for each level in the scaling hierarchy. In full generality, the model allows the transition matrices to be different at each level, potentially making available a large number of parameters for landscape characterization. The model is self-similar when the transition matrices are all equal. A method is presented for fitting the model to data that take the form of a single-resolution thematic raster map. Explicit analytic solutions are obtained for the fitted parameters. The fitting method is based on a relationship between the hierarchical transitions in the model and spatial transitions at varying distance scales in the data map, a categorical analogy of the geostatistical variogram.     

Achieving Conservation Science that Bridges the Knowledge–Action Boundary

There are many barriers to using science to inform conservation policy and practice. Conservation scientists wishing to produce management‐relevant science must balance this goal with the imperative of demonstrating novelty and rigor in their science. Decision makers seeking to make evidence‐based decisions must balance a desire for knowledge with the need to act despite uncertainty. Generating science that will effectively inform management decisions requires that the production of information (the components of knowledge) be salient (relevant and timely), credible (authoritative, believable, and trusted), and legitimate (developed via a process that considers the values and perspectives of all relevant actors) in the eyes of both researchers and decision makers. We perceive 3 key challenges for those hoping to generate conservation science that achieves all 3 of these information characteristics. First, scientific and management audiences can have contrasting perceptions about the salience of research. Second, the pursuit of scientific credibility can come at the cost of salience and legitimacy in the eyes of decision makers, and, third, different actors can have conflicting views about what constitutes legitimate information. We highlight 4 institutional frameworks that can facilitate science that will inform management: boundary organizations (environmental organizations that span the boundary between science and management), research scientists embedded in resource management agencies, formal links between decision makers and scientists at research‐focused institutions, and training programs for conservation professionals. Although these are not the only approaches to generating boundary‐spanning science, nor are they mutually exclusive, they provide mechanisms for promoting communication, translation, and mediation across the knowledge–action boundary. We believe that despite the challenges, conservation science should strive to be a boundary science, which both advances scientific understanding and contributes to decision making. Logrando que la Ciencia de la Conservación Trasponga la Frontera Conocimiento‐Acción     

Use of Inverse Spatial Conservation Prioritization to Avoid Biological Diversity Loss Outside Protected Areas

Globally expanding human land use sets constantly increasing pressure for maintenance of biological diversity and functioning ecosystems. To fight the decline of biological diversity, conservation science has broken ground with methods such as the operational model of systematic conservation planning (SCP), which focuses on design and on‐the‐ground implementation of conservation areas. The most commonly used method in SCP is reserve selection that focuses on the spatial design of reserve networks and their expansion. We expanded these methods by introducing another form of spatial allocation of conservation effort relevant for land‐use zoning at the landscape scale that avoids negative ecological effects of human land use outside protected areas. We call our method inverse spatial conservation prioritization. It can be used to identify areas suitable for economic development while simultaneously limiting total ecological and environmental effects of that development at the landscape level by identifying areas with highest economic but lowest ecological value. Our method is not based on a priori targets, and as such it is applicable to cases where the effects of land use on, for example, individual species or ecosystem types are relatively small and would not lead to violation of regional or national conservation targets. We applied our method to land‐use allocation to peat mining. Our method identified a combination of profitable production areas that provides the needed area for peat production while retaining most of the landscape‐level ecological value of the ecosystem. The results of this inverse spatial conservation prioritization are being used in land‐use zoning in the province of Central Finland.