Common approaches to estimating habitat quality are habitat suitability indices, which are often subjective. We propose a quantitative definition of habitat quality as the percentage of selected habitat that has a high probability of contributing to population growth. Using this definition, we created a habitat quality index based on spatial projections of habitat selection and survival probabilities, and the interaction of those probability gradients. We used nest-site selection and nest survival of Gray Vireos (Vireo vicinior) to represent habitat selection and survival probabilities of a sensitive life stage that is likely to increase population size. We used data from 173 Gray Vireo nests in central New Mexico to estimate selection and survival probabilities. Generalized linear mixed-effect models (GLMM) and logistic exposure models (LEM) were used to estimate nest-site selection and daily nest survival, respectively. We projected top-ranked GLMM and LEM models in a geographic information system (GIS) to spatially display relative probabilities of selection and survival. We converted these continuous probabilities into binary rasters representing high and low selection and survival. Multiplying these two rasters in ArcGIS provided us with a raster of four categories: areas with i) low selection and low survival, ii) low selection and high survival, iii) high selection and low survival, and iv) high selection and high survival. We determined the spatial area of each category and calculated percentage of selected habitat where survival was likely to occur, which we termed a habitat quality index (HQI). At our study site, Gray Vireos had a HQI of 0.85, suggesting that approximately 85% of the highly selected habitat had a high probability of contributing to Gray Vireo population growth through nest survival, and that few ecological traps exist in this landscape. These methods provide a quantifiable, continuous index of habitat quality, with spatial projections of potential ecological trap locations.

评估栖息地质量的常用方法是栖息地适宜性指数，这通常是主观的。我们提出了栖息地质量的定量定义，即对人口增长有很大贡献的选定栖息地的百分比。使用这个定义，我们根据栖息地选择和生存概率的空间投影以及这些概率梯度的相互作用创建了一个栖息地质量指数。我们使用了灰色 Vireos ( Vireo vicinior) 代表可能增加种群规模的敏感生命阶段的栖息地选择和生存概率。我们使用来自新墨西哥州中部 173 个 Gray Vireo 巢穴的数据来估计选择和生存概率。广义线性混合效应模型 (GLMM) 和逻辑暴露模型 (LEM) 分别用于估计巢址选择和每日巢存活率。我们在地理信息系统 (GIS) 中投影了排名靠前的 GLMM 和 LEM 模型，以在空间上显示选择和生存的相对概率。我们将这些连续概率转换为代表高低选择和生存的二进制栅格。在 ArcGIS 中将这两个栅格相乘为我们提供了四个类别的栅格：i) 低选择和低生存率的区域，ii) 低选择和高生存率的区域，iii) 高选择低存活率，以及 iv) 高选择高存活率。我们确定了每个类别的空间面积并计算了可能发生生存的选定栖息地的百分比，我们将其称为栖息地质量指数 (HQI)。在我们的研究地点，Grey Vireos 的 HQI 为 0.85，这表明大约 85% 的高度选择的栖息地很有可能通过筑巢生存促进 Grey Vireo 种群增长，并且该景观中几乎不存在生态陷阱。这些方法提供了一个可量化的、连续的栖息地质量指数，以及潜在生态陷阱位置的空间预测。我们称之为栖息地质量指数（HQI）。在我们的研究地点，Grey Vireos 的 HQI 为 0.85，这表明大约 85% 的高度选择的栖息地很有可能通过筑巢生存促进 Grey Vireo 种群增长，并且该景观中几乎不存在生态陷阱。这些方法提供了一个可量化的、连续的栖息地质量指数，以及潜在生态陷阱位置的空间预测。我们称之为栖息地质量指数（HQI）。在我们的研究地点，Grey Vireos 的 HQI 为 0.85，这表明大约 85% 的高度选择的栖息地很有可能通过筑巢生存促进 Grey Vireo 种群增长，并且该景观中几乎不存在生态陷阱。这些方法提供了一个可量化的、连续的栖息地质量指数，以及潜在生态陷阱位置的空间预测。