Mapping breeding bird species richness at management-relevant resolutions across the United States

Human activities alter ecosystems everywhere, causing rapid biodiversity
loss and biotic homogenization. These losses necessitate coordinated
conservation actions guided by biodiversity and species distribution
spatial data that cover large areas yet have fine-enough resolution to be
management-relevant (i.e., ≤ 5 km). However, most biodiversity products
are too coarse for management or are only available for small areas.
Furthermore, many maps generated for biodiversity assessment and
conservation do not explicitly quantify the inherent tradeoff between
resolution and accuracy when predicting biodiversity patterns. Our goals
were to 1) generate predictive models of overall breeding bird species
richness and species richness of different guilds based on nine functional
or life history-based traits across the conterminous US at three
resolutions (0.5, 2.5, and 5 km), and 2) quantify the tradeoff between
resolution and accuracy, and hence relevance for management, of the
resulting biodiversity maps. We summarized eighteen years of North
American Breeding Bird Survey data (1992-2019) and modeled species
richness using random forests, including 66 predictor variables
(describing climate, vegetation, geomorphology, and anthropogenic
conditions), 20 of which we newly derived. Among the three spatial
resolutions, the percent variance explained ranged from 27% to 60% (median
= 54%; mean = 57%) for overall species richness and 12% to 87% (median =
61%; mean = 58%) for our different guilds. Overall species richness and
guild-specific species richness were best explained at 5-km resolution
using approximately 24 predictor variables based on percent variance
explained, symmetric mean absolute percentage error, and root mean squared
error values. However, our 2.5-km resolution maps were almost as accurate
and provided more spatially detailed information, which is why we
recommend them for most management applications. Our results represent the
first consistent, occurrence-based, and nationwide maps of breeding bird
richness with a thorough accuracy assessment that are also spatially
detailed enough to inform local management decisions. More broadly, our
findings highlight the importance of explicitly considering tradeoffs
between resolution and accuracy to create management-relevant biodiversity
products for large areas.

人类活动在全球范围内改变生态系统，引发生物多样性快速丧失与生物均质化（biotic homogenization）现象。此类生物多样性丧失亟需协调一致的保护行动，而此类行动需以覆盖大范围区域、且分辨率足够精细以适配管理需求（即≤5千米）的生物多样性与物种分布空间数据为指导。然而，当前多数生物多样性相关数据产品要么分辨率过于粗糙，无法满足管理需求，要么仅能覆盖小范围区域。此外，多数用于生物多样性评估与保护的制图工作，在预测生物多样性分布格局时，并未明确量化分辨率与精度之间固有的权衡关系。本研究的目标有二：其一，以美国本土范围内9种基于功能或生活史特征的指标为依据，构建三种分辨率（0.5、2.5、5千米）下的整体繁殖鸟类物种丰富度以及不同功能群鸟类物种丰富度的预测模型；其二，量化最终生成的生物多样性地图在分辨率与精度之间的权衡关系，进而明确其对管理工作的适配性。本研究整合了1992至2019年共18年的北美繁殖鸟类调查（North American Breeding Bird Survey）数据，采用随机森林（random forests）模型构建物种丰富度预测模型，共纳入66个预测变量（涵盖气候、植被、地貌与人为活动条件），其中20个变量为本次研究首次构建。在三种空间分辨率下，整体繁殖鸟类物种丰富度模型的解释方差占比介于27%至60%之间（中位数为54%，平均值为57%）；不同功能群鸟类物种丰富度模型的解释方差占比则介于12%至87%之间（中位数为61%，平均值为58%）。基于解释方差占比、对称平均绝对百分比误差（symmetric mean absolute percentage error）与均方根误差（root mean squared error）三项指标，整体物种丰富度与功能群特有物种丰富度的最优预测模型均采用5千米分辨率，且约纳入24个预测变量。但2.5千米分辨率的制图结果精度几乎与之相当，且空间细节更为丰富，因此我们推荐将该分辨率的数据应用于多数管理场景。本研究生成的繁殖鸟类物种丰富度分布图，是首个基于观测记录、覆盖全美且经过全面精度评估的一致性繁殖鸟类丰富度地图，其空间分辨率足够精细，可用于指导本地管理决策。从更广泛的视角来看，本研究结果凸显了在构建适用于大范围区域的管理导向型生物多样性数据产品时，明确考量分辨率与精度之间权衡关系的重要性。