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首页> 外文期刊>Ecosphere >Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning
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Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning

机译：使用最大熵模型预测大型树木的潜在分布，以进行保护规划

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Large trees, as keystone structures, are functionally important in savanna ecosystems, and low recruitment and slow growth makes their conservation important. Understanding factors influencing their distribution is essential for mitigation of excessive mortality, for example from management fires or large herbivores. We recorded the locations of large trees in Hluhluwe‐Imfolozi Park (HiP) using GPS to record trees along 43 km of 10 m‐wide transects. Maximum entropy modeling (MaxEnt) uses niche modeling to predict the distribution of a species from the probability of finding it within raster squares, based on environmental variables and recorded locations. MaxEnt is typically applied at a regional spatial scale, and here we assessed its usefulness when predicting the distribution of species at a small (local) scale. HiP has variable topography, heterogeneous soils, and a strong rainfall gradient, resulting in a wide variety of habitat types. We used locations of 179 Acacia nigrescens and 106 Sclerocarya birrea (large trees ≥ 5m), and raster environmental layers for: aspect, elevation, geology, annual rainfall, slope, soil and vegetation. A. nigrescens was largely restricted to the Imfolozi section, while S. birrea had a wider distribution across the reserve. Understanding the interaction of environmental variables dictating tree distribution may facilitate habitat restoration, and will assist planning decisions for persistence of large trees within reserves, including options to reduce fire frequency or herbivore impacts. Though the AUC (Area Under the Curve) values used to test model predictions were high for both species, the ground truthing test data showed that distribution for A. nigrescens was more accurate than that for S. birrea, highlighting the need for independent test data to assess model accuracy. We emphasize that MaxEnt can be used at finer spatial scales than those typically used for species occurrence, but models must be tested using spatially independent test data.
机译：大树作为基石结构，在热带稀树草原生态系统中起着重要的功能，而低的吸收和缓慢的生长使它们的保护很重要。理解影响其分布的因素对于减轻过多的死亡率至关重要，例如，减少因管理火或大型食草动物引起的死亡率。我们使用GPS记录了Hluhluwe-Imfolozi公园（HiP）中大树的位置，以记录沿10 km宽的43 km的树木。最大熵建模（MaxEnt）使用利基模型，根据环境变量和记录的位置，根据在栅格正方形内发现物种的概率来预测物种的分布。 MaxEnt通常用于区域空间尺度，在这里我们在预测小（局部）尺度的物种分布时评估了其有用性。 HiP具有可变的地形，非均质的土壤和强降雨梯度，从而导致了多种生境类型。我们使用了179个相思树和106个 Sclerocarya birrea（≥5m的大树）和栅格环境层的位置：纵横比，海拔，地质，年降雨量，坡度，土壤和植被。 ni.grescens在很大程度上局限于Imfolozi部分，而 S.。 birrea在整个保护区的分布范围更广。了解决定树木分布的环境变量之间的相互作用可能会促进栖息地的恢复，并有助于规划保护区内大型树木的持久性，包括减少火灾频率或食草动物影响的方案。尽管用于测试模型预测的AUC（曲线下面积）值对于这两个物种均较高，但地面实测测试数据显示，黑曲霉的分布比 S的分布更准确。 birrea，强调需要独立的测试数据来评估模型的准确性。我们强调，MaxEnt可以在比通常用于物种发生的空间尺度上更好的空间尺度上使用，但是必须使用空间独立的测试数据来测试模型。

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