Assessing ecosystem collapse risk in ecosystems dominated by foundation species: The case of fringe mangroves

Abstract

Ecosystem collapse, i.e. the endpoint of ecosystem decline, is a central concept of IUCN Red List of Ecosystems (RLE) assessments and the identification of ecosystems most vulnerable to global environmental change. Estimating collapse risk can be challenging for ecosystems reliant on a few dominant species to perform most of their functions because the range of suitable and feasible indicators is small. This study investigates the robustness and adequacy of the current RLE approach for risk assessments in such ecosystems, using a fringe mangrove ecosystem as a case study. Following the RLE protocol, we constructed a conceptual model of the key ecosystem processes for the Philippines' fringe mangrove forests. Satellite remote sensing data and existing maps of mangrove forests were then combined to assess the spatial distribution of the ecosystem considered (Criteria A and B), while the Normalized Difference Vegetation Index was used to assess biotic degradation (Criterion D). Insufficient data were available to assess Criteria C (environmental degradation) and E (quantitative analysis). Overall, the ecosystem was assessed as 'Least Concern' based on extensive geographic distribution and only weak support for declines in extent. Criterion D was classed as 'Data Deficient' since there was no clear relationship between the vegetation index and fringe mangrove degradation. Our results demonstrate how gaps in our appreciation and understanding of the structure and functioning of ecosystems are more likely to impede risk assessments of ecosystems characterised by a small number of foundation species, due to the low level of redundancy between candidate indicators available for their assessments. Satellite remote sensing combined with derivation of explicit conceptual ecosystem models provides a way to structure efforts to identify suitable indicators as well as opportunities to overcome many of these challenges, even for relatively data-poor ecosystems.

How to develop a comprehensive Mangrove Quality Index?

“…The index of biotic integrity [6] for wetlands was based on diversity and dominance but varied amongst vegetation classes. Integrating different types of data via satellite remote sensing, geographical information system (GIS) and modelling could be a useful approach to assess the status of a mangrove ecosystem [7]. Mangrove Vulnerability Index (MVI) using GIS was used to analyze social-ecological response to environmental changes and measure susceptibility to damage and capacity to cope or adapt [8].…”

Manglares y sus Servicios Ecosistémicos: hacia un Desarrollo Sostenible

“…Uno de los mayores logros de los indicadores además de la recuperación paulatina de los ecosistemas, ha sido crear herramientas tecnológicas (programas informáticos) donde se agregan ciertos indicadores del manglar (microfauna, suelo, variables fisicoquímicas) que ya han sido elegidas estadísticamente, lo que ha ayudado a reducir la mano de obra, el tiempo y el costo en el monitoreo en comparación con la práctica actual de verificar manualmente los datos como es el caso del volumen de stock de madera como un indicador de salud del manglar (Faridah-Hanum et al, 2019). Así mismo, a pesar de que en un manglar no exista información suficiente o es complejo obtener datos, con los modelos informáticos aún en las condiciones adversas se puede obtener resultados confiables (Marshall et al, 2018).…”

“…También se han utilizado los Sistemas de Información Geográfica y sensores remotos para trabajar con indicadores de cambio de paisaje (Coto et al, 2018). Marshall et al (2018) determinaron que la teledetección satelital combinada con la derivación de modelos en ecosistemas proporciona una manera de estructurar los esfuerzos para la elección de mejores y más adecuados indicadores, que brinden mayor información certera así como oportunidades para superar desafíos en ecosistemas relativamente pobres en datos. Faridah-Hanum et al (2019) implementaron un programa informático basado en estadísticas para la elección de indicadores de mayor relevancia en los estudios de manglares; lo que ha permitido reducir la mano de obra, el tiempo y el costo en el monitoreo.…”

Estimating changes and trends in ecosystem extent with dense time‐series satellite remote sensing

“…Satellite remote sensing provides regular information at spatial scales sufficient to monitor ecosystems over their full extent (Turner et al 2003(Turner et al , 2015Murray et al 2018a). These data are frequently used to estimate changes in ecosystem extent, and studies range from local (e.g., Murray et al 2014;Reddy et al 2014;Marshall et al 2018) to global (e.g., Hansen et al 2013;Thomas et al 2017;Murray et al 2019). One key advantage of satellite remote sensing data is the regular collection of data, which provides a regular stream of information on Earth's changing land cover.…”

“…Despite extensive satellite time-series data availability and their utility in estimating trends in ecosystem extent, few researchers have used them to their full advantage. Most researchers investigating ecosystem extent use annual estimates of change derived from satellite data (Zalles et al 2019;Qin et al 2019); others compare as few as two estimates obtained from the start and end points of a time series (e.g., Reddy et al 2014;Murray et al 2015;Marshall et al 2018). Such studies typically focus on identifying and using images that are cloudfree, which leads to a large proportion of potentially informative images being discarded prior to analysis.…”