1. Forecasting the responses of biodiversity to global change has never been more important. However, many ecologists faced with limited sample sizes and shoestring budgets often resort to extrapolating predictive models beyond the range of their data to support management actions in data‐deficient contexts. This can lead to error‐prone inference that has the potential to misdirect conservation interventions and undermine decision‐making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarizing and visualizing extrapolation in multivariate environmental space.
2. dsmextra automates the process of conducting quantitative, spatially explicit assessments of extrapolation on the basis of two established metrics: the Extrapolation Detection (ExDet) tool and the percentage of data nearby (%N). The package provides user‐friendly functions to (a) calculate these metrics, (b) create tabular and graphical summaries, (c) explore combinations of covariate sets as a means of informing covariate selection and (d) produce visual displays in the form of interactive html maps.
3. dsmextra implements a model‐agnostic approach to extrapolation detection that is applicable across taxonomic groups, modelling techniques and datasets. We present a case study fitting a density surface model to visual detections of pantropical spotted dolphins Stenella attenuata in the Gulf of Mexico.
4. Predictive modelling seeks to deliver actionable information about the states and trajectories of ecological systems, yet model performance can be strongly impaired out of sample. By assessing conditions under which models are likely to fail or succeed in extrapolating, ecologists may gain a better understanding of biological patterns and their underlying drivers. Critical to this is a concerted effort to standardize best practice in model evaluation, with an emphasis on extrapolative capacity.

中文翻译：

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dsmextra：用于密度曲面模型的外推评估工具

1. 预测生物多样性对全球变化的反应从未如此重要。但是，许多面临样本量有限和预算有限的生态学家常常求助于超出其数据范围的预测模型的推断，以支持在数据不足的情况下的管理行动。这可能导致容易出错的推理，有可能误导保护措施并破坏决策。尽管存在外推带来的风险，但在识别外推的最佳方法方面几乎没有指导，从而使用户质疑应在模型输出中放置多少可信度。为了解决这个问题，我们提出了dsmextra，这是一个新的R包，用于测量，汇总和可视化多元环境空间中的外推法。
2. dsmextra会基于两个已建立的指标：外推检测（ExDet）工具和附近的数据百分比（％N），自动进行定量的，空间上明确的外推评估过程。该软件包提供了用户友好的功能，可以（a）计算这些指标，（b）创建表格和图形摘要，（c）探索协变量集的组合作为通知协变量选择的一种手段，以及（d）以交互式html地图。
3. dsmextra为外推检测实现了与模型无关的方法，该方法适用于分类组，建模技术和数据集。我们提出了一个案例研究，该案例研究拟合了密度表面模型，以便在墨西哥湾中观测到泛热带斑点海豚Stenella detecta。
4. 预测建模旨在提供有关生态系统状态和轨迹的可操作信息，但模型性能可能会因样本严重受损。通过评估模型可能无法成功推断的条件，生态学家可以更好地了解生物学模式及其潜在驱动因素。对此至关重要的是一致努力，以标准化模型评估中的最佳实践为重点，并强调外推能力。