Using earth observation data to improve climate-modifying habitat datasets for pest risk modelling

Slides presented at the International Pest Risk Research Group (IPRRG) Annual meeting in Nairobi, Kenya 2023:\r\n\r\nUsing earth observation data to improve climate-modifying habitat datasets for pest risk modelling. Tim Beale (CABI), Pascale Bodevin (CABI), Steve Edgington (CABI), Libertad Sanchez Presa (CABI), Bryony Taylor (CABI), Alex Cornelius (Assimila), Gerardo Lopez Saldana (Assimila), Jon Styles (Assimila), Darren J. Kriticos (Cervantes Agritech) \r\n\r\nNon-climatic habitat factors can have a significant effect on species ranges, allowing them to persist well beyond their natural ranges.\xa0 Irrigation and protected agricultural structures such as glasshouses are used specifically to allow crop species to be grown successfully in locations where the climate is otherwise inhospitable.\xa0 The same conditions that allow the crops to be grown in hostile climates allows pest species to persist beyond their natural limits.\xa0 Species distribution databases such as GBIF and iSCAN do not distinguish between species distribution records collected from natural habitat situations and those from artificial habitat.\xa0 Bioclimatic models that ignore the role of these artificial habitat modifications routinely make egregious errors, incorrectly projecting habitat suitability into inclement climates.\xa0 Methodically overestimating the pest risk area in this manner can have important effects on biosecurity risk management, misdirecting resource allocation for preparedness activities, and undermining the reputation of pest risk assessment.\r\n\r\nAdvances in Earth Observation (EO) technology have opened up new possibilities for addressing agricultural challenges in the face of climate change. The EO4AgroClimate project is using EO-derived data to enhance three critical modelling datasets: irrigation, protected agriculture, and canopy temperature. These datasets will help to contextualise species distribution data from repositories, as well as improve the performance of environmental niche models (ENM) leading to more accurate, high-resolution, and timely information for pest risk assessment.

2023年于肯尼亚内罗毕举办的国际有害生物风险研究组（International Pest Risk Research Group, IPRRG）年会展示的演示幻灯片：

《利用地球观测（Earth Observation, EO）数据优化有害生物风险建模中的气候修正生境数据集》，作者包括：蒂姆·比尔（CABI）、帕斯卡尔·博德万（CABI）、史蒂夫·埃奇灵顿（CABI）、利伯塔德·桑切斯·普雷萨（CABI）、布赖恩尼·泰勒（CABI）、亚历克斯·科尼利厄斯（Assimila）、赫拉尔多·洛佩斯·萨尔达尼亚（Assimila）、乔恩·斯泰尔斯（Assimila）、达伦·J·克里蒂科斯（Cervantes Agritech）

非气候生境因子对物种分布范围具有显著影响，可使物种在远超其自然分布范围的区域存续。灌溉设施与温室等保护性农业设施的设置初衷，便是使作物得以在原本气候不适宜的区域成功种植。而为作物适配恶劣气候创造的相同条件，同样也会让有害生物得以突破其自然分布极限存续。诸如全球生物多样性信息设施（Global Biodiversity Information Facility, GBIF）与iSCAN在内的物种分布数据库，并未区分自然生境与人工生境下的物种分布记录。忽略这类人工生境改造作用的生物气候模型往往会犯下严重错误，错误地将栖息地适宜性投影至气候恶劣的区域。以此方式系统性高估有害生物风险区域，会对生物安全风险管理产生重大负面影响：误导防灾准备活动的资源分配，损害有害生物风险评估的公信力。

地球观测（Earth Observation, EO）技术的进步为应对气候变化背景下的农业挑战带来了新可能。EO4AgroClimate项目正利用地球观测衍生数据优化三类关键建模数据集：灌溉情况、保护性农业分布与冠层温度。这些数据集将有助于明确各物种分布数据在公共数据库中的背景信息，同时提升生态位模型（Environmental Niche Model, ENM）的性能，从而为有害生物风险评估提供更精准、高分辨率且时效性更强的信息。