Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models

Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of “marked” wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of “unmarked” wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.种群密度估算是野生动物有效保护与管理不可或缺的核心组成部分。结合红外相机陷阱（camera traps）技术与空间捕获-再捕获（spatial capture-recapture, SCR）模型的分析框架，已被用于精准估算由可识别个体组成的“标记”野生动物种群的密度。空间计数（spatial count, SC）模型的出现，为无法识别个体的“无标记”野生动物种群提供了低成本高效的密度估算途径。本研究针对一种正在恢复的中型食肉动物——渔貂（Pekania pennanti）种群，对比评估了两种方法的模型一致性、估算精度与调查成本：其一为利用非侵入式遗传数据拟合SCR模型的全标记种群方法，其二为利用红外相机陷阱数据拟合SC模型的无标记种群方法。SCR模型的密度估算值为每100平方千米2.95至3.42只渔貂，95%贝叶斯可信区间（Bayesian Credible Interval, BCI）为2.18~5.19。SC模型的密度估算结果受先验分布影响显著，其估算值范围从无信息先验模型的每100平方千米0.95只渔貂（95% BCI：0.65~2.95），到基于16平方千米渔貂家域先验知识构建的模型的每100平方千米3.60只渔貂（95% BCI：2.01~7.55）。本研究警示需谨慎使用强信息先验，建议采用一系列未加权的先验知识开展建模。稀疏检测数据对SCR与SC模型均存在负面影响，可能导致偏低的有偏估算结果。遗传调查的总成本为47610美元，仅为红外相机陷阱调查成本77080美元的三分之二；若增加遗传采样强度以在SCR模型中纳入性别与诱捕行为协变量，则两者成本相当，为75746美元。无标记种群的密度估算仍需在多维度间进行权衡，但随着相关方法的不断完善与整合，对应的密度估算结果也将愈发可靠。