Effects of disturbance and landscape structure on the biodiversity value of remnant tropical forest corridors

该研究将基于30个空间上独立的剩余河岸走廊和10个嵌在大片连续森林中的河岸站点，对社区范围内的植物（树木和胸高直径为50 ~ 5厘米的木本藤本植物）、日间鸟类和非飞行哺乳动物进行定量调查。每个站点将采样多达10个地块，但这将不可避免地取决于走廊斑块的大小。所有地点将在3500平方公里的景观多边形（包括破碎矩阵和连续森林）内使用地理参考2006年Landsat图像（定位误差<10米）进行预选。为了控制区域敏感物种源种群的差异，所有廊道都将连接到至少1000公顷的森林斑块，但采样点的位置将明确考虑到这些斑块的距离。在之前的一项研究中，我们在Alta Floresta 80公里半径内建立了一个由154名土地所有者组成的协作网络，这将极大地促进调查地点的访问和调查物流。物种多样性和物种组成的模式将被量化，然后与以前在同一地区不同大小的森林斑块中对同一分类群的调查进行比较（Michalski & Peres 2005, in press, Peres & Michalski 2006, Lees & Peres in press）。鸟类和哺乳动物调查将包括每日样线普查、哺乳动物足迹调查、相机陷阱、犰狳洞穴计数、有限半径点计数。植物区系清单将以0.25公顷（10 × 250米）的地块为基础。为了重建过去十年景观结构的变化，将对所有站点进行gps定位，然后将其与半年一次的Landsat图像时间序列（1996-2006年，最近图像的像素大小为15 m）相关联。在现场获得的环境栖息地变量将包括树木密度、森林基底面积、树种组成、冠层覆盖度（使用数字半球面照片）、下层密度和树叶的垂直分层。植物鉴定将由一名合作植物学家进行，并借助于当地UNEMAT植物标本室已经提供的植物凭证参考资料。此外，沿着50米长度的垂直走廊剖面将使用从邻近牧场拍摄的数码照片进行评估（走廊两侧每个地块两张照片）。森林档案照片将阈值和处理使用专用软件，我们已经编译。然后，使用来自最近Landsat图像的像素级多光谱数据，将得到的斑块度量（即走廊高度、垂直剖面面积）与同一地点的森林栖息地质量联系起来，这些数据提供了相同走廊水平面的鸟瞰图。大多数廊道遗址都是农村的一部分，并且经常受到不同形式的人为森林干扰的影响。因此，将对来自邻近家庭（大型牧场和小农）的自愿当地举报人进行访谈，以恢复每个地点的结构性和非结构性干扰的历史，包括选择性伐木、野火、放牧和狩猎。廊道干扰历史将使用依赖于间隙分数指数（归一化差异分数指数，NDFI）的图像处理技术进一步验证，该指数结合了SMA（光谱混合分析）得出的绿色和非光合植被、土壤和阴影分数，以增强对人为冠层干扰的检测（Souza et al. 2005）。考虑到斑块和景观变量，以及场地内的干扰历史，将主要使用广义线性模型和逻辑回归对每个群落的群落特征、单个物种的占用和丰度模式进行数据分析。

The study will be based on community-wide quantitative inventories of plants (trees and woody lianas >5 cm in diameter at breast height), diurnal birds, and non-flying mammals at 30 spatially independent remnant riparian corridors and 10 riparian sites embedded within large tracts of continuous forest. As many as 10 plots will be sampled at each site, but this will unavoidably depend on corridor patch size. All sites will be pre-selected using a georeferenced 2006 Landsat image (positional error <10m) within a 3,500 km2 landscape polygon including both the fragmented matrix and continuous forests. To control for differences in source populations of area-sensitive species, all corridors will be connected to forest patches of at least 1,000 ha, but sampling site location will explicitly take into account distance to these patches. During a previous study, we developed a collaborative network of 154 landowners within a 80-km radius of Alta Floresta, which will greatly facilitate access to survey sites and survey logistics. Patterns of species diversity and species composition will be quantified and then compared with previous surveys on the same taxa in variable-sized forest patches in the same region (Michalski & Peres 2005, in press, Peres & Michalski 2006, Lees & Peres in press). Bird and mammal surveys will include diurnal line-transect censuses, surveys of mammal tracks, camera trapping, armadillo burrow counts, limited-radius point-counts. Floristic inventories will be based on 0.25-ha (10 x 250m) plots. To reconstruct changes in landscape structure in the last decade, all sites will be GPS-located and then related to a biannual time series of Landsat images (1996-2006, pixel size = 15 m in recent images). Environmental habitat variables obtained in site will include tree density, forest basal area, tree species composition, canopy cover (using digital hemispherical photographs), understorey density, and vertical stratification of foliage. Plant identification will be conducted by a collaborating botanist, aided by a reference collection of plant vouchers that is already available at the local UNEMAT herbarium. In addition, vertical corridor profiles along sections of 50 m in length will be assessed using digital photos taken from neighbouring pasture sites (two photos per plot along both sides of the corridor). Forest profile photos will be thresholded and processed using a dedicated software that we have compiled. The resulting patch metrics (i.e. corridor height, area of vertical profiles) will then be related to the forest habitat quality of the same sites using pixel-scale multi-spectral data from recent Landsat images, which provides an aerial view of the horizontal plane of the same corridors. Most corridor sites are part of the working countryside and have been subjected to a recurrent history of different forms of anthropogenic forest disturbance. Willing local informants from neighbouring households (large cattle ranches and smallholders) will thus be interviewed to recover the history of structural and non-structural disturbance at each site, including selective logging, wildfires, cattle grazing, and game hunting. Corridor disturbance history will be further verified using an image-processing technique that relies on a gap fraction index (the Normalized Difference Fraction Index, NDFI) which combines the green and non-photosynthetic vegetation, soil and shade fractions derived from SMA (spectral mixture analysis) to enhance the detection of anthropogenic canopy disturbance (Souza et al. 2005). Data analysis of community-wide properties of each assemblage, and the patterns of both occupancy and abundance of single species will be carried out primarily using generalized linear modelling and logistic regressions taking into account patch and landscape variables, as well as the history of disturbance within a site.