Are trapping data suited for home-range estimation?

Modern home-range estimation typically relies on data derived from expensive radio- or GPS-tracking. Although trapping represents a low-cost alternative to telemetry, there lacks an evaluation of the performance of home-range estimators on trap-derived data. Using simulated data, we evaluate 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 compare the performance of five home-range estimators (MCP, LoCoH, KDE, AKDE, bicubic interpolation). We further explore 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 scen..., Using an I.I.D. movement model, we simulated captures in different trapping conditions and compared the home range size obtained with different methods. We analyzed real-world trapping data for white-tailed deer (Florida) and jaguars (Belize) from publicly available repositories and compared the home range sizes obtained with those predicted based on our simulations. , All data can be opened with R. All R scripts to create and analyze the data can be found at https://github.com/llsociasmartinez/home-range-trapping-data.

当前主流的家域估算（home-range estimation）方法通常依赖于成本高昂的无线电追踪或GPS追踪所获取的数据。尽管诱捕是遥测（telemetry）之外的低成本替代方案，但目前尚无针对诱捕来源数据的家域估算器性能评估研究。本研究利用模拟数据，评估了生态学家在设计诱捕实验时面临的三类核心权衡变量：1）单一个体的观测次数，2）诱捕器密度，3）家域落入诱捕区域的比例。本研究对比了五种家域估算器（MCP、LoCoH、KDE、AKDE、双三次插值（bicubic interpolation））的性能。此外，本研究还探索了将这些估算器与渐近模型结合的潜在优势：渐近模型可利用随观测次数增加而趋于饱和的家域面积估算变化规律提升估算精度，同时还测试了不同的数据排序流程。随后，本研究量化了不同情景下家域面积的估算偏差。本研究利用独立同分布（I.I.D.）运动模型，模拟了不同诱捕条件下的捕获情况，并对比了不同方法得到的家域面积估算结果。本研究从公开数据库中获取了佛罗里达白尾鹿以及伯利兹美洲豹的实地诱捕数据，将其估算得到的家域面积与模拟预测结果进行了对比。所有数据均可通过R语言打开，用于生成和分析数据的全部R脚本可在以下网址获取：https://github.com/llsociasmartinez/home-range-trapping-data。