Looking to the past to conserve present-day biodiversity: using distributional information and ecological modelling
Looking to the past to conserve present-day biodiversity: using distributional information and ecological modelling
批准号:
2598735
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
长期以来,物种保护一直专注于防止人类驱动的灭绝。因此,在过去的50年里,保护的成功是用物种灭绝风险的变化来衡量的,主要的例子是世界自然保护联盟的物种红色名录。然而,最近有人呼吁同时关注物种的恢复,并制定评估其成就的指标。保护界的这种观点转变可以部分归因于人们认识到,物种丰度和分布的基线在人类世代之间发生了巨大变化,全球可检测到的人类对生态系统的影响至少始于几千年前。灭绝风险评估通常只考虑物种在过去几十年的变化,而恢复评估则意味着考虑数百年至数千年的变化。这就需要确定人类首次成为影响物种丰度和分布的主要因素时的基线状况,为了促进基线研究,保护古生物学网络工作组的主要目标是制定和实施新的自然保护联盟物种绿色状况框架。该框架考虑了相对于撞击前基线的物种保护状况。将利用来自全球的古生态和历史生态数据集,以促进对这些分布基线的估计。这项工作的一个关键方面是为从业人员提供一个方便的方法,以确定其感兴趣的物种的基线。CPN项目的目标是应用预测物种分布的方法来重建给定物种的过去分布范围。一般的做法是:1.识别可用于告知过去物种分布的数据集。变量的主要类别将是:>土地覆盖(例如LUH 2)>气候(例如BioClim变量,CHELSA)>人类足迹(例如ANTHROMES)>物种出现(例如GBIF)>历史或考古物种的存在点。利用当前数据(如气候和土地覆被)和当前物种发生率来模拟物种的生态位。使用模型与预测变量的历史数据来推断物种的分布。4.使用物种出现的历史数据(如博物馆记录,化石数据,历史记录)来验证模型预测。重点是重建1500年至1750年的物种分布。最终目标是演示和验证一个工作流程,并使保护评估人员能够广泛使用这个工作流程。一个适当的起点是将上述方法应用于一个或几个物种,作为可行性的证明和找出困难的方法。最初的目标物种应涵盖一系列生境(陆地、淡水和海洋)和空间尺度(从局部种群到全球分布)。另一个重要的要素是,在某些要素缺失的情况下,制定和验证替代工作流程(例如,存在当前出现的现象,但不存在过去出现的现象;存在相关/相关分类群的现象,但不存在焦点种)。
英文摘要
Species conservation has long focused on preventing human-driven extinctions. Conservation success has therefore been measured using changes in species' extinction risk over the past 50 years, with the primary example being the IUCN Red List of Species. However, recently calls have been made for a parallel focus on species' recovery, and on developing metrics with which to assess its achievement. This shift in perspective within the conservation community can be partially attributed to the recognition that baselines of species' abundance and distribution have shifted dramatically across human generations, with globally detectable human impacts on ecosystems beginning at least several thousand years ago. Assessment of extinction risk generally only considers species' change over the past few decades, whereas assessment of recovery means considering change over centuries to millennia. This requires identifying the baseline status at the time when humans first became a major factor influencing the abundance and distribution of a species.In order to facilitate research on baselines, the primary aim of the Conservation Paleobiology Network (CPN) working group is the development and implementation of the new IUCN Green Status of Species framework. This framework considers the conservation status of species relative to a pre-impact baseline. Globally-derived palaeo-ecological and historical ecological datasets will be leveraged to facilitate estimation of these distributional baselines. A crucial aspect of this work will be to provide an easily accessible way for practitioners to determine baselines for their species of interest. The goal of the CPN project is to apply methods for predicting species distributions to reconstruct the past range of a given species. The general approach is:1. Identification of datasets that can be used to inform past species' distributions. The major categories of variables will be:> Land cover (e.g. LUH2)> Climate (e.g. BioClim variables, CHELSA)> Human footprint (e.g. ANTHROMES)> Species occurrences (e.g. GBIF)> Historical or Archaeological species' presence points.2. Model the ecological niche of the species' using current data (such as climate and land cover) and current species occurrences.3. Use the model with historical data on the predictor variables to extrapolate the species' distribution back in time.4. Use historical data on species occurrences (such as museum records, fossil data, historical accounts) to validate the model prediction. Priority will be on reconstructing species' distributions in the temporal range 1500 to 1750 CE.The ultimate goal is to demonstrate and validate a workflow, and to make this workflow widely accessible to conservation assessors. An appropriate starting point is to apply the above approach to one or few species as a demonstration of feasibility and as a way to figure out difficulties. Initial target species should cover a range of habitat niches (terrestrial, freshwater, and marine examples) and spatial scales (localised population to global distribution). Another important element will be to develop and validate alternate workflows when certain elements are missing (e.g. current occurrences exist, but not past occurrences; occurrences exist for related/associated taxa but not focal species).
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