Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California

In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and human actions to enhance wetland habitat. Taking advantage of the Landsat satellite time series (2007–2016) and structured and community science bird survey data, we built species distribution models to describe how three duck species – Northern Pintail (Anas acuta), Green-winged Teal (A. crecca), and Northern Shoveler (A. clypeata) – respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models were designed to compare duck habitat suitability between the wettest to driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (i) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the surface water availability within the 16-day window of a Landsat observation, (ii) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (iii) long-term average covariates – the most common approach to species distribution modeling – in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to overall surface water availability. When models of habitat suitability based on the long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly more accurate models. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.

Methods
Please see mansucript [currently in revision] with the same title for the full description of methods and results.

在干旱与地中海气候区域，开展景观尺度的湿地保护工作，需明确野生动物对动态淡水供给与人类活动的响应规律，以此优化湿地栖息地配置。本研究依托2007—2016年的Landsat卫星时序数据，以及结构化调查与社区科学鸟类观测数据，构建物种分布模型，以描述加利福尼亚中央谷内3种鸭类——针尾鸭（Northern Pintail，Anas acuta）、绿翅鸭（Green-winged Teal，A. crecca）与北琵嘴鸭（Northern Shoveler，A. clypeata）——对不同淹水土地覆盖类型下的淡水供给与食物资源的响应特征。具体而言，本研究的模型旨在对比9月至次年4月各月内，从最湿到最旱条件下的鸭类栖息地适宜性差异。研究采用丰度加权提升回归树，基于三类不同协变量构建了3组物种出现模型：(i) 近实时（下称“实时”）协变量：将鸟类观测记录与Landsat观测16天窗口内的地表水可获得性进行匹配；(ii) 实时协变量与水禽食物资源协变量的组合：后者包含基于Landsat数据推导的年度玉米、水稻生物量，以及人工湿地湿土种子产量估算值；(iii) 长期平均协变量：这是物种分布建模中最常用的方法，采用长期平均地表水可获得性作为核心因子。本研究以月为单位，将3种鸭类的出现概率建模为地表水可获得性、土地覆盖类型、道路密度、气温及鸟类数据来源的函数。

研究结果显示，干旱条件会降低栖息地适宜性，其中11月至次年1月及农田生境的适宜性降幅最为显著；与之相对，淹水湿地生境的适宜性对整体地表水可获得性的变化相对稳健。将基于长期平均气候条件的栖息地适宜性模型与基于实时条件的模型对比后可见，长期适宜性最高的区域，无论干湿年份，其栖息地适宜性均维持在较高水平。尽管所有模型均表现优异，但加入作物与湿地植物产量协变量的模型精度略高。总体而言，基于鸟类观测时的实际环境条件构建的物种分布模型，可有效助力大空间尺度下极端气候条件中的湿地保护工作。

方法
请参见同名且目前处于修订阶段的手稿，以获取方法与结果的完整描述。