738 J. R. Ferrer‐Paris et al.


PLATE 2 Mosaic of satellite images (Sentinel-2 MultiSpectral Instrument, Level 2A; December 2021– March 2022; clouds and shadows removed) over the three peaks in the Cordillera de Mérida, Venezuela: (1) Bolívar, (2) La Concha and (3) Humboldt. The square represents the same 10 × 10 km grid cell shown in Fig. 1.


Methods


We applied the IUCN Red List of Ecosystems protocol (Bland et al., 2017) that requires assessment of five criteria: A, declining distribution; B, restricted distribution and ex- posure to threats; C, degradation of abiotic environment; D, disruption of biotic processes and E, quantitative risk analysis. Three of the five criteria (A, C and D) include sub- criteria applied to three different timeframes to capture the effects of historical, current and future threats. Criterion B evaluates the current exposure to threats using various spa- tial metrics, and criterion E is evaluated over 50 and 100 years into the future. The complete assessment is provided in the Supplementary Material. We captured the main ecosystem processes and the


threats of climate change and air pollution in a conceptual ecosystem model (Fig. 2) and identified spatiotemporal indicators for the assessment of each criterion and sub-criterion. We defined a threshold of collapse for each indicator and interpolated or extrapolated time series data to infer rates of change over the relevant timeframes. We defined the collapsed state of the ecosystem as the


complete loss of the icy substrate that can sustain a cold-resistant microbiota (zero ice extent for spatial criteria A and B or zero ice mass for criterion E). We used three in- dicators to assess climatic suitability for glacial function under criterion C: the equilibrium-line altitude (the altitude at which rates of ice accumulation and ablation are equal); the atmospheric freezing level height (the altitude of the 0 °C isotherm); and a suitability threshold based on a cor- relative bioclimatic suitability model. An increase in equilibrium-line altitude or freezing level height reduces the available area for long-term glacier persistence, with


thresholds of collapse for both indicators set to the altitude of the mountain summit (Polissar et al., 2006; Braun & Bezada, 2013). We based the threshold of bioclimatic suit- ability on the optimal classification of the current occur- rence of glacier outlines. There is currently no suitable indicator of collapse for criterion D because the microbiota of the supraglacial zone and its relationships with other eco- logical zones of the glacier are still poorly understood (Ball et al., 2014; Rondón et al., 2016). Data collected at Humboldt Peak in 2019 and 2021 (including supraglacial, englacial and subglacial samples as well as soil from the glacial forefront) may shed light on this issue. Preliminary results indicate marked changes in microbiota composition and function and a sizeable role of themicrobiota on ecosystem processes in four chronosequence sites deglaciated during 1910–2009 (B. Huber, pers. comm., 2023); similar findings have been reported for a tropical glacier in Ecuador (Díaz et al., 2023). We used availablemeasures of glacier ice extent to calculate


best estimates of past decline and to extrapolate rates of future declines in ice extent (sub-criteria A1,A2band A3). We obtained measurements and their standard errors from pub- lished, ground-validated reconstructions based on cartograph- ic data from 1910, aerial photographs from 1952 and 1998, satellite images from 2009, 2015 and 2016 and field measure- ments using GPSand drone imagery from 2019 (Ramírez et al., 2020). Indirect and less accurate evidence of the extent of glacier ice prior to 1910 is available from a few other sources and is provided for context (Polissar et al., 2006; Jomelli et al., 2009;Braun&Bezada, 2013).We calculated past declines based on pairs of measurements and considering error propa- gation. For future declines we calculated first proportional rates of decline (as percentage area loss per year) and then used this rate to project the expected magnitude of decline


Oryx, 2024, 58(6), 735–745 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001771


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