Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

Feng, Yuhao; Su, Haojie; Tang, Zhiyao; Wang, Shaopeng; Zhao, Xia; Zhang, Heng; Ji, Chengjun; Zhu, Jieshun; Xie, Ping; Fang, Jingyun

doi:10.1038/s43247-021-00163-1

Cited by 43 publications

(32 citation statements)

References 81 publications

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“…A lower cutoff would enlarge the areas where resilience loss is detected, but also increase the risk of false positives. A similar study using NDVI data estimated up to ~65% of terrestrial ecosystems show early warning of critical transitions, with strong bias towards boreal forest and taiga [39]. The estimates presented here complement previous efforts [30,39] in taking into consideration fixed effects by biome and a pre-processing technique that removes seasonality and long-term variations that can lead to errors, or bias towards high variable environments (higher latitudes).…”

Section: Discussionsupporting

confidence: 65%

“…These are biomes where the strongest signals of resilience loss were detected with a right shift on the distribution of |∆|. Other studies quantifying terrestrial ecosystems' resilience with NDVI data also show strong signals from tundra and boreal forests [39]. A recent quantification of aridity thresholds shows that up to 28.6% of current dryland area can cross these thresholds by 2100 in the most drastic climate scenarios [40].…”

Section: Discussionmentioning

confidence: 91%

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Ecosystems are showing symptoms of resilience loss

Rocha

2022

Environ. Res. Lett.

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Ecosystems around the world are at risk of critical transitions due to increasing anthropogenic pressures and climate change. Yet it is unclear where the risks are higher or where in the world ecosystems are more vulnerable. Here I measure resilience of primary productivity proxies for marine and terrestrial ecosystems globally. Up to 29% of global terrestrial ecosystem, and 24% marine ones, show symptoms of resilience loss. These symptoms are shown in all biomes, but Arctic tundra and boreal forest are the most affected, as well as the Indian Ocean and Eastern Pacific. Although the results are likely an underestimation, they enable the identification of risk areas as well as the potential synchrony of some transitions, helping prioritize areas for management interventions and conservation.

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Section: Discussionsupporting

confidence: 65%

Section: Discussionmentioning

confidence: 91%

Ecosystems are showing symptoms of resilience loss

Rocha

2022

Environ. Res. Lett.

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“…Other studies (table 1) combine statistical indicators, including AR(1) and variance, into a composite indicator [11,12], but generally without clear justification for their relative weighting, and with the drawbacks of e.g. merging aspects of resilience and resistance, and double-counting the effects of recovery rate.…”

Section: Defining and Measuring Resiliencementioning

confidence: 99%

“…Some existing studies have started to consider trends in 'resilience' over time, but do not present a mean value as a baseline, and use composite indicators that do not isolate recovery rate [11,12]. Trends in a composite indicator of autocorrelation, standard deviation, skewness and kurtosis of NDVI over 1981-2015 hints at greatest loss of 'resilience' in the tundra [11]. A more confusing composite indicator hints at primary production showing greatest 'resilience' loss in tundra and boreal forest [12].…”

Section: (B) Global Datamentioning

confidence: 99%

“…Abbreviations: NDVI, normalized difference vegetation index; GPP, gross primary productivity; LAI, leaf area index; VOD, vegetation optical depth; EVI, enhanced vegetation index; SAR, synthetic aperture radar; dNBR, difference normalized burn ratio; DLM , dynamic linear model.scale, locationyearref.vegetation, perturbation index(es) (satellite/ database)time spanresolution (native)‘resilience’ metric(s)recovery rate?space a time global world2014[7]15 land cover typesNDVI (MODIS)2001–20067 km (250 m)16 dAR(1), SD, CV, spectral entropy, spectral scalingyes (AR(1)) world2015[8]AR model fit subsetNDVI (GIMMS)1981–20060.072°1 month (15 d)AR(1)yes world2017[9]all, drought GPP (MODIS)2000–2010(1 km)1 month (1 d)recovery timenot really world2020[10]all, drought, fire LAI (GLASS, GIMMS)1982–2016, 1982–20110.05°, 0.083°8 d, 15 dmax. stress /recovery timeproblematic world2021[11]allNDVI 3 g (GIMMS)1981–20150.083°1 monthACF-1, variance, skewness, kurtosisproblematic (composite indicator)…”

Section: Introductionmentioning

confidence: 99%

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A resilience sensing system for the biosphere

Lenton

Buxton

McKay

et al. 2022

Phil. Trans. R. Soc. B

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We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems—loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.

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Speed and degree of functional and compositional recovery varies with latitude and community age

Bonfim,

López,

Repetto

et al. 2024

Ecology

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Rates at which a community recovers after disturbance, or its resilience, can be accelerated by increased net primary productivity and recolonization dynamics such as recruitment. These mechanisms can vary across biogeographic gradients, such as latitude, suggesting that biogeography is likely important to predicting resilience. To test whether community resilience, informed by functional and compositional recovery, hinges on geographic location, we employed a standardized replicated experiment on marine invertebrate communities across four regions from the tropics to the subarctic zone. Communities assembled naturally on standardized substrate while experiencing distinct levels of biomass removal (no removal, low disturbance, and high disturbance), which opened space for new colonizers, thereby providing a pulse of limited resource to these communities. We then quantified functional (space occupancy and biomass) and compositional recovery from these repeated pulse disturbances across two community assembly timescales (early and late at 3 and 12 months, respectively). We documented latitudinal variation in resilience across 47° latitude, where speed of functional recovery was higher toward lower latitudes yet incomplete at late assembly in the tropics and subtropics. The degree of functional recovery did not coincide with compositional recovery, and regional differences in recruitment and growth likely contributed to functional recovery in these communities. While biogeographic variation in community resilience has been predicted, our results are among the first to examine functional and compositional recovery from disturbance in a single large‐scale standardized experiment.

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Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

Cited by 43 publications

References 81 publications

Ecosystems are showing symptoms of resilience loss

Ecosystems are showing symptoms of resilience loss

A resilience sensing system for the biosphere

Speed and degree of functional and compositional recovery varies with latitude and community age

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