Context

Spatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking.

Objectives

We identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.

Methods

We simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.

Results

Highly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.

Conclusions

Unknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.

中文翻译：

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在空间捕获-再捕获中忽略变量和空间自相关检测概率的后果

语境

空间捕获-再捕获 (SCR) 模型在分析野生动物监测数据方面越来越受欢迎。SCR 可以解释由个体空间使用（检测内核）、采样过程的变化和个体分布（密度）引起的检测中的空间异质性。然而，由于研究系统内在和外在的神秘因素，无法解释和未建模的可检测性空间异质性可能仍然存在。例如，当用于可变努力和检测概率的协变量编码通常不完整或完全缺乏时，就是这种情况。

目标

我们确定未建模的空间可变检测概率的大小和配置如何影响 SCR 参数估计。

方法

我们模拟了具有空间变量和自相关检测概率的 SCR 数据。然后，我们拟合了一个 SCR 模型，忽略了对模拟数据的这种变化，并评估了模型错误指定对推理的影响。

结果

检测概率中高度自相关的空间异质性（Moran's I = 0.85–0.96），受未建模异质性的大小调制，可导致明显的负偏差（高达 65%，或与参考情景相比降低约 44 倍） ，减少在精密（249％或2.5倍），并用大量的估计相关联的为0。相反，在空间自相关的低水平的95％可信区间的覆盖概率（中位数莫兰我= 0），在检测甚至是严重的未建模的异质概率并没有导致明显的偏差，只是导致丰度估计的精确度和覆盖率略有下降。

结论

在 SCR 研究中，未知和未建模的检测概率变化可能是常态，而不是例外。我们鼓励从业者考虑可检测性的空间自相关对其推断的影响，并敦促开发 SCR 方法，该方法可以考虑检测概率的结构化、未知或部分未知的空间变异性。