Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada.
Environmental and Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada.
PeerJ. 2022 Jun 7;10:e13490. doi: 10.7717/peerj.13490. eCollection 2022.
Landscape structure affects animal movement. Differences between landscapes may induce heterogeneity in home range size and movement rates among individuals within a population. These types of heterogeneity can cause bias when estimating population size or density and are seldom considered during analyses. Individual heterogeneity, attributable to unknown or unobserved covariates, is often modelled using latent mixture distributions, but these are demanding of data, and abundance estimates are sensitive to the parameters of the mixture distribution. A recent extension of spatially explicit capture-recapture models allows landscape structure to be modelled explicitly by incorporating landscape connectivity using non-Euclidean least-cost paths, improving inference, especially in highly structured (riparian & mountainous) landscapes. Our objective was to investigate whether these novel models could improve inference about black bear () density. We fit spatially explicit capture-recapture models with standard and complex structures to black bear data from 51 separate study areas. We found that non-Euclidean models were supported in over half of our study areas. Associated density estimates were higher and less precise than those from simple models and only slightly more precise than those from finite mixture models. Estimates were sensitive to the scale (pixel resolution) at which least-cost paths were calculated, but there was no consistent pattern across covariates or resolutions. Our results indicate that negative bias associated with ignoring heterogeneity is potentially severe. However, the most popular method for dealing with this heterogeneity (finite mixtures) yielded potentially unreliable point estimates of abundance that may not be comparable across surveys, even in data sets with 136-350 total detections, 3-5 detections per individual, 97-283 recaptures, and 80-254 spatial recaptures. In these same study areas with high sample sizes, we expected that landscape features would not severely constrain animal movements and modelling non-Euclidian distance would not consistently improve inference. Our results suggest caution in applying non-Euclidean SCR models when there is no clear landscape covariate that is known to strongly influence the movement of the focal species, and in applying finite mixture models except when abundant data are available.
景观结构会影响动物的运动。不同的景观可能会导致种群内个体的家域大小和移动率的异质性。这些类型的异质性在估计种群数量或密度时会产生偏差，但在分析中很少被考虑。个体的异质性归因于未知或未观察到的协变量，通常使用潜在混合分布来建模，但这些分布对数据的要求很高，并且丰度估计对混合分布的参数很敏感。最近扩展的空间显式捕获-再捕获模型通过使用非欧几里得最小成本路径显式地对景观结构进行建模，从而提高了推断能力，特别是在高度结构化（河岸和山区）的景观中。我们的目标是研究这些新模型是否可以改善关于黑熊（）密度的推断。我们使用标准和复杂结构的空间显式捕获-再捕获模型拟合了来自 51 个独立研究区域的黑熊数据。我们发现，在我们的研究区域中，有一半以上的非欧几里得模型得到了支持。相关的密度估计值高于简单模型，且精度较低，但比有限混合模型略高。估计值对最小成本路径计算的尺度（像素分辨率）敏感，但在协变量或分辨率方面没有一致的模式。我们的结果表明，忽略异质性可能会导致严重的负偏差。然而，处理这种异质性的最流行方法（有限混合模型）产生的丰度点估计值可能不可靠，即使在总检测次数为 136-350 次、个体检测次数为 3-5 次、重捕次数为 97-283 次和空间重捕次数为 80-254 次的数据集之间，也可能无法进行比较。在这些样本量较大的相同研究区域中，我们预计景观特征不会严重限制动物的运动，并且在没有明显的已知强烈影响研究物种运动的景观协变量的情况下，使用非欧几里得距离模型不会一致地改善推断。我们的结果表明，在没有明确的已知会强烈影响目标物种运动的景观协变量的情况下，在应用非欧几里得 SCR 模型时应谨慎，并且在数据不丰富的情况下应用有限混合模型时也应谨慎。
