Modern home-range estimation typically relies on data derived from expensive radio- or GPS-tracking. Although trapping represents a low-cost alternative to telemetry, evaluation of the performance of home-range estimators on trap-derived data is lacking. Using simulated data, we evaluated three variables reflecting the key trade-offs ecologists face when designing a trapping study: 1) the number of observations obtained per individual, 2) the trap density and 3) the proportion of the home range falling inside the trapping area. We compared the performance of five home-range estimators (MCP: Minimum Convex Polygon, LoCoH: Local Convex Hull, KDE: Kernel Density Estimation, AKDE: Autocorrelated Kernel Density Estimation, BicubIt: Bicubic Interpolation). We further explored the potential benefits of combining these estimators with asymptotic models, which leverage the saturating behavior of changes in the estimated home-range area as the number of observations increases to improve accuracy, as well as different data-ordering procedures. We then quantified the bias in home-range size under the different scenarios investigated. The number of observations and the proportion of the home range within the trapping grid were the most important predictors of the accuracy and the precision of home-range estimates. The use of asymptotic models helped to obtain accurate estimates at smaller sample sizes, while distance ordering improved the precision and asymptotic consistency of estimates. While AKDE was the best performing estimator under most conditions evaluated, bicubic interpolation was a viable alternative under common real-world conditions of low trap density and area covered. A case study using empirical data from white-tailed deer in Florida and another from jaguars in Belize demonstrated support for the findings of our simulation results. Although researchers with trap data often overlook home-range estimation, our results indicate that these data have the capacity to yield accurate estimates of home-range size. Trapping data can, therefore, lower the economic costs of home-range analysis, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.

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诱捕数据是否适合家庭范围估计？

现代家庭范围估计通常依赖于从昂贵的无线电或 GPS 跟踪中获得的数据。尽管陷阱代表了遥测的低成本替代方案，但缺乏对家庭范围估计器对陷阱派生数据的性能的评估。使用模拟数据，我们评估了反映生态学家在设计诱捕研究时面临的关键权衡取舍的三个变量：1）每个个体获得的观察数量，2）诱捕密度和 3）落入诱捕范围内的家域比例区域。我们比较了五个家庭范围估计器的性能（MCP：最小凸多边形，LoCoH：局部凸包，KDE：核密度估计，AKDE：自相关核密度估计，BicubIt：双三次插值）。我们进一步探讨了将这些估计量与渐近模型相结合的潜在好处，随着观测数量的增加，它利用估计的家庭范围区域变化的饱和行为来提高准确性，以及不同的数据排序程序。然后，我们量化了调查的不同情景下家庭范围大小的偏差。观察次数和诱捕网格内的家域比例是家域估计准确度和精密度的最重要预测因子。渐近模型的使用有助于在较小的样本量下获得准确的估计，而距离排序提高了估计的精度和渐近一致性。虽然 AKDE 在大多数评估条件下是性能最好的估算器，但在低陷阱密度和覆盖面积的常见现实条件下，双三次插值是一种可行的替代方案。使用来自佛罗里达州白尾鹿和伯利兹美洲虎的另一项经验数据的案例研究证明了对我们模拟结果的支持。尽管拥有陷阱数据的研究人员经常忽视家域估计，但我们的结果表明这些数据有能力准确估计家域大小。因此，诱捕数据可以降低家域分析的经济成本，有可能扩大生态学和保护研究中的物种、研究人员和问题的范围。