Modelling spatial distribution and change from wildlife survey data

根据野生动物调查数据对空间分布和变化进行建模

• 批准号: EP/K041061/1
• 负责人: David Borchers
• 金额: $ 40.64万
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK041061%2F1
• 关键词: Modelling; spatial; distribution; change; wildlife

A reduction of biodiversity loss is a key aim of the Convention on Biological Diversity (CBD) for 2020, and quantifying the loss is essential for managing it. This involves estimating the size and distribution of wild populations, which is statistically challenging - using only animals detected (often a very small fraction of the population), one must deduce the abundance and distribution of animals that were not detected.Natural systems invariably have spatial structure, and monitoring and understanding what drives habitat use, spatial distribution and changes in spatial distribution is central to understanding and predicting the effects of natural or human-induced perturbations of natural systems. This is difficult because the spatial structure of fauna and flora is often complex, involving spatial trend, spatial randomness and spatial correlation. Fitting spatial models that cannot accommodate all these aspects of spatial distribution can lead to very misleading conclusions about the drivers of spatial distribution and changes in distribution. In particular, inadequate modelling of randomness and correlation can lead to incorrect inferences and misleading predictions. And while realistically complex spatial models have existed for some time, until very recently the methods for fitting such models were too slow to be useful. With the advent of the Integrated Nested Laplace Approximation (INLA) method this is no longer the case, and as a result, use of this method has grown rapidly and the software implementing it is in great demand.However, there are currently no methods or software (INLA or other) for fitting realistically complex spatial models to data obtained from processes in which the probability of detecting population members is unknown. And a distinguishing feature of wildlife survey data is that they involve exactly such unknown detection probabilities, and what is worse, they involve detection probabilities that vary in space. The spatial distribution(s) of the population(s) of interests and the spatial distribution of detection probability have to be separated in order to draw reliable inferences about the population spatial distribution.Distance sampling (DS) and capture-recapture (CR) methods are far and away the most widely-used wildlife survey methods. Much of DS research effort has focused on developing methods for reliable estimation of spatial detection probability. Until very recently CR methods neglected the spatial component of detection probability entirely, but with the recent advent of Spatially Explicit Capture-Recapture (SECR) methods, CR methods are now also able to estimate spatial detection probability. But (with a few exceptions) both methods currently estimate detection probability assuming unrealistically simple population spatial distributions. While estimates of abundance are robust to this, estimates of distribution are not.This project combines the strengths of DS and CR methods and INLA. It will unite spatial modelling methods in INLA and spatial detection probability estimation methods of SECR and DS methods, to provide for the first time rigorous statistical methods and software for estimating realistically complex spatial distributions using data from the two most widely-used wildlife survey methods. It will provide more powerful methods and tools than are currently available for drawing inferences about what drives the distribution and change in distribution of fauna and flora. In so doing, it will provide substantially more powerful tools for monitoring and managing biodiversity loss than are currently available. And because DS and CR surveys usually record spatial data, the methods will be retrospectively applicable to many existing time series of survey data, so that they can be used immediately to "look into the past" and draw inferences about distribution and changes in distribution stretching as far back into the past as do reliable data sets.

减少生物多样性丧失是《生物多样性公约》（CBD）2020年的一个关键目标，量化损失对于管理生物多样性丧失至关重要。这涉及到估计野生种群的规模和分布，这在统计上具有挑战性-仅使用检测到的动物（通常是种群的一小部分），人们必须推断出未被检测到的动物的丰度和分布。自然系统总是具有空间结构，监测和了解驱动生境使用、空间分布和空间分布变化的因素，对于了解和预测自然或人为扰动对自然系统的影响至关重要。这是困难的，因为动物群和植物群的空间结构往往是复杂的，涉及空间趋势性、空间随机性和空间相关性。如果拟合的空间模型不能涵盖空间分布的所有这些方面，可能会导致对空间分布的驱动因素和分布变化得出非常误导的结论。特别是，随机性和相关性的建模不足可能导致不正确的推断和误导性的预测。虽然实际上复杂的空间模型已经存在了一段时间，但直到最近，拟合这些模型的方法都太慢而无法使用。随着集成嵌套拉普拉斯近似（INLA）方法的出现，情况不再如此，因此，该方法的使用迅速增长，并且实现该方法的软件需求量很大。然而，目前没有方法或软件（INLA或其他）用于将实际复杂的空间模型拟合到从检测群体成员的概率未知的过程中获得的数据。野生动物调查数据的一个显著特点是，它们涉及的正是这种未知的检测概率，更糟糕的是，它们涉及的检测概率在空间上是变化的。为了对种群空间分布做出可靠的推断，必须将感兴趣种群的空间分布和检测概率的空间分布分开，距离抽样（Distance Sampling，DS）和捕获-再捕获（Capture-Recapture，CR）方法是目前应用最广泛的野生动物调查方法。大部分DS研究工作都集中在开发可靠的空间检测概率估计方法上。直到最近，CR方法完全忽略了检测概率的空间分量，但是随着空间显式捕获-再捕获（SECR）方法的出现，CR方法现在也能够估计空间检测概率。但是（除了少数例外），这两种方法目前估计检测概率假设不切实际的简单人口空间分布。虽然丰度的估计是稳健的，但分布的估计不是。它将把INLA中的空间建模方法与SECR和DS方法中的空间探测概率估计方法结合起来，首次提供严格的统计方法和软件，利用两种最广泛使用的野生动物调查方法的数据来估计现实的复杂空间分布。它将提供比目前更强大的方法和工具，以推断是什么驱动了动植物群和植物群的分布和分布变化。这样，它将为监测和管理生物多样性的丧失提供比目前更有力的工具。由于DS和CR调查通常记录空间数据，这些方法将可追溯适用于许多现有的调查数据时间序列，因此它们可以立即用于“回顾过去”，并对分布和分布变化进行推断，这些分布和分布变化可以追溯到过去，就像可靠的数据集一样。

项目成果

inlabru: an R package for Bayesian spatial modelling from ecological survey data

• DOI: 10.1111/2041-210x.13168
• 发表时间: 2019-06-01
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Bachl, Fabian E.;Lindgren, Finn;Illian, Janine B.
• 通讯作者: Illian, Janine B.

Translating area-based conservation pledges into efficient biodiversity protection outcomes.

• DOI: 10.1038/s42003-021-02590-4
• 发表时间: 2021-09-07
• 期刊: Communications biology
• 影响因子: 5.9
• 作者: Cunningham CA;Crick HQP;Morecroft MD;Thomas CD;Beale CM
• 通讯作者: Beale CM

Isotopic niche variation in Tasmanian devils Sarcophilus harrisii with progression of devil facial tumor disease.

• DOI: 10.1002/ece3.7636
• 发表时间: 2021-06
• 期刊: Ecology and evolution
• 影响因子: 2.6
• 作者: Bell O;Jones ME;Cunningham CX;Ruiz-Aravena M;Hamilton DG;Comte S;Hamede RK;Bearhop S;McDonald RA
• 通讯作者: McDonald RA

Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse

• DOI: 10.1002/env.2694
• 发表时间: 2020-03
• 期刊: Environmetrics
• 影响因子: 1.7
• 作者: Jieying Jiao;Guanyu Hu;Jun Yan

Spatiotemporal variation in harbor porpoise distribution and foraging across a landscape of fear

港湾鼠海豚分布和在恐惧景观中觅食的时空变化

• DOI: 10.1111/mms.12839
• 发表时间: 2021
• 期刊: Marine Mammal Science
• 影响因子: 2.3
• 作者: Williamson L