Data from: Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity

Changing climate is already impacting the spatial distribution of many taxa, including bees, plants, birds, butterflies and fishes. A common goal is to detect range shifts in response to climate change, including changes in the centre of the population's distribution (the centre of gravity, COG), population boundaries and area occupied. Conventional estimators, such as the abundance-weighted average (AWA) estimator for COG, confound range shifts with changes in the spatial distribution of available survey data and may be biased when the distribution of survey data shifts over time. AWA also does not estimate the standard error of COG in individual years and cannot incorporate data from multiple survey designs.
To explicitly account for changes in the spatial distribution of survey effort, we propose an alternative species distribution function (SDF) estimator. The SDF approach involves calculating distribution metrics, including COG, population boundary and area occupied, directly from the predicted species distribution or density function. We illustrate the SDF approach using a spatiotemporal model that is available as an r package. Using simulated data, we confirm that the SDF substantially decreases bias in COG estimates relative to the AWA estimator. We then illustrate the method by analysing data from two data sets spanning 1977–2013 for 18 marine fishes along the U.S. West Coast.
In our case study, the SDF estimator shows significant northward shifts for six of 18 species (with southward shifts for only 2), where two species (darkblotched and greenstriped rockfishes) have both a northward shift and a decreased area occupied. Pelagic species (e.g. Pacific hake and spiny dogfish) have more variable distribution than bottom-associated species. We also find substantial differences between AWA and SDF estimates of COG that are likely caused by shifts in sampling distribution (which affect the AWA but not the SDF estimator).
We caution that common estimators for range shift can yield inappropriate inference whenever sampling designs have shifted over time. We conclude by suggesting further improvements in model-based approaches to analysing climate impacts, including methods addressing the impact of local and regional temperature changes on species distribution.

气候变化已对诸多生物类群的空间分布造成影响，涉及蜜蜂、植物、鸟类、蝴蝶与鱼类等。本领域的常见研究目标之一，是检测物种分布范围对气候变化的响应偏移，包括种群分布中心（重心，COG）、种群边界以及物种占据区域的变化。传统的估算方法，例如针对COG的丰度加权平均（abundance-weighted average, AWA）估算器，会将分布范围偏移与可用调查数据的空间分布变化相混淆；当调查数据的分布随时间发生偏移时，该方法可能产生估算偏差。此外，AWA无法估算单一年份下COG的标准误，也无法整合多种调查设计下的数据。
为明确考量调查采样投入的空间分布变化，本文提出一种替代方案——物种分布函数（species distribution function, SDF）估算器。SDF方法可直接基于预测的物种分布或密度函数，计算包括COG、种群边界与占据区域在内的各类分布指标。本文采用一款可通过R包获取的时空模型，对SDF方法进行示例演示。通过模拟数据验证，本文证实相较于AWA估算器，SDF方法可大幅降低COG估算的偏差。随后，本文通过分析两组涵盖1977年至2013年、美国西海岸18种海洋鱼类的数据集，对该方法进行实例展示。
在本案例研究中，SDF估算器显示18个物种中有6个出现显著北移（仅2个出现南移），其中2个物种（暗斑岩鱼（darkblotched rockfish）与绿纹岩鱼（greenstriped rockfish））同时出现北移与占据区域缩减的情况。远洋物种（例如太平洋无须鳕与棘背角鲨）的分布相较于底栖关联物种更为多变。同时，本文发现AWA与SDF对COG的估算结果存在显著差异，这一差异大概率由采样分布的偏移所致——采样分布偏移会影响AWA的估算结果，但不会对SDF估算器造成影响。
本文提醒，当采样设计随时间发生偏移时，常用的分布范围偏移估算方法可能得出不恰当的推断结论。最后，本文提出未来可进一步优化基于模型的气候变化影响分析方法，例如开发针对局地与区域温度变化对物种分布影响的相关分析方法。