Abstract Forecasting the spread and potential impacts of invasive, alien species is vital to relevant management and policy decisions. Models that estimate areas of potential suitability are useful to guide early detection and eradication, inform effective budget allocations, and justify quarantine regulations. Machine-learning is a rapidly emerging technology with myriad applications, including the analysis of factors that govern species' distributions. However, forecasts for invasive species often require extrapolation into novel spaces, which may severely erode model reliability. Using the popular machine-learning platform, MaxEnt, we integrate numerous tools and recommendations to demonstrate a method of rigorous model development that emphasizes assessment of model transferability. Our models use Lymantria dispar dispar (L.) (Lepidoptera: Erebidae), an insect brought to the United States in the late 1860s from Europe and subsequently well monitored in spread. Recent genetic analyses provide evidence that the eastern North American population originated in Germany, France, and northern Italy. We demonstrate that models built and assessed using typical methodology for invasive species (e.g., using records from the full native geographic range) showed the smallest extent of extrapolation, but the worst transferability when validated with independent data. Conversely, models based on the purported genetic source of the eastern North American populations (i.e., a subset of the native range) showed the greatest transferability, but the largest extent of extrapolation. Overall, the model that yielded high transferability to North America and low extrapolation was built following current recommendations of spatial thinning and parameter optimization with records from both the genetic source in Europe and early North American invasion.

中文翻译：

---

最小化风险和最大化空间可转移性：构建入侵昆虫潜在适宜性的有用模型的挑战

摘要 预测外来入侵物种的传播和潜在影响对于相关管理和政策决策至关重要。估计潜在适用区域的模型有助于指导早期发现和根除、告知有效的预算分配以及证明检疫规定的合理性。机器学习是一种迅速崛起的技术，具有无数的应用，包括对控制物种分布的因素的分析。然而，对入侵物种的预测通常需要外推到新的空间，这可能会严重削弱模型的可靠性。使用流行的机器学习平台 MaxEnt，我们集成了大量工具和建议，以展示一种强调模型可迁移性评估的严格模型开发方法。我们的模型使用 Lymantria dispar dispar (L.)（鳞翅目：Erebidae），一种于 1860 年代后期从欧洲带到美国的昆虫，随后对其传播进行了很好的监测。最近的基因分析提供了证据表明北美东部人口起源于德国、法国和意大利北部。我们证明，使用典型的入侵物种方法（例如，使用来自整个本地地理范围的记录）建立和评估的模型显示出最小的外推范围，但在使用独立数据进行验证时的可转移性最差。相反，基于北美东部种群（即本地范围的一个子集）的据称遗传来源的模型显示出最大的可转移性，但外推的范围最大。全面的，