U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont 05405, USA.
Ecol Appl. 2012 Dec;22(8):2265-76. doi: 10.1890/11-1804.1.
Habitat suitability (HS) maps are widely used tools in wildlife science and establish a link between wildlife populations and landscape pattern. Although HS maps spatially depict the distribution of optimal resources for a species, they do not reveal the population size a landscape is capable of supporting--information that is often crucial for decision makers and managers. We used a new approach, "maximum clique analysis," to demonstrate how HS maps for territorial species can be used to estimate the carrying capacity, N(k), of a given landscape. We estimated the N(k) of Ovenbirds (Seiurus aurocapillus) and bobcats (Lynx rufus) in an 1153-km2 study area in Vermont, USA. These two species were selected to highlight different approaches in building an HS map as well as computational challenges that can arise in a maximum clique analysis. We derived 30-m2 HS maps for each species via occupancy modeling (Ovenbird) and by resource utilization modeling (bobcats). For each species, we then identified all pixel locations on the map (points) that had sufficient resources in the surrounding area to maintain a home range (termed a "pseudo-home range"). These locations were converted to a mathematical graph, where any two points were linked if two pseudo-home ranges could exist on the landscape without violating territory boundaries. We used the program Cliquer to find the maximum clique of each graph. The resulting estimates of N(k) = 236 Ovenbirds and N(k) = 42 female bobcats were sensitive to different assumptions and model inputs. Estimates of N(k) via alternative, ad hoc methods were 1.4 to > 30 times greater than the maximum clique estimate, suggesting that the alternative results may be upwardly biased. The maximum clique analysis was computationally intensive but could handle problems with < 1500 total pseudo-home ranges (points). Given present computational constraints, it is best suited for species that occur in clustered distributions (where the problem can be broken into several, smaller problems), or for species with large home ranges relative to grid scale where resampling the points to a coarser resolution can reduce the problem to manageable proportions.
生境适宜性 (HS) 图是野生动物科学中广泛使用的工具，它在野生动物种群和景观格局之间建立了联系。虽然 HS 图在空间上描绘了物种最佳资源的分布，但它们并没有揭示景观能够支持的种群规模——对于决策者和管理者来说，这些信息往往是至关重要的。我们使用了一种新方法，“最大团分析”，来展示如何利用领地物种的 HS 图来估计给定景观的承载能力 N(k)。我们估计了美国佛蒙特州一个 1153 平方公里研究区域内的鸣禽 (Seiurus aurocapillus) 和山猫 (Lynx rufus) 的 N(k)。选择这两个物种是为了突出构建 HS 图的不同方法以及在最大团分析中可能出现的计算挑战。我们通过占有模型 (鸣禽) 和资源利用模型 (山猫) 为每个物种生成了 30 平方米的 HS 图。对于每个物种，我们在地图上确定了所有具有足够资源的像素位置 (点)，以维持一个家域 (称为“伪家域”)。这些位置被转换为一个数学图形，其中任何两个点如果可以在景观上存在两个伪家域而不违反领地边界，则它们之间就有连接。我们使用 Cliquer 程序来找到每个图形的最大团。由此得出的 N(k) = 236 只鸣禽和 N(k) = 42 只雌性山猫的估计值对不同的假设和模型输入很敏感。通过替代的、特别的方法得出的 N(k) 估计值比最大团估计值高 1.4 到 > 30 倍，这表明替代结果可能存在向上偏差。最大团分析计算量很大，但可以处理 < 1500 个总伪家域 (点) 的问题。鉴于目前的计算限制，它最适合于呈聚类分布的物种 (可以将问题分解为几个较小的问题)，或者对于家域相对于网格尺度较大的物种，通过将点重新采样到较粗的分辨率可以将问题缩小到可管理的比例。
