The exposure of the world’s mountains to global change drivers

doi:10.21203/rs.3.rs-3008744/v1

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The exposure of the world’s mountains to global change drivers

https://doi.org/10.21203/rs.3.rs-3008744/v1

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published 31 Mar, 2024

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Mountain areas around the world are exposed to different drivers of global change, facing a dichotomy between being both climatic refugia and highly sensitive ecosystems. Through two different metrics, the velocity and the magnitude of change, we quantified the exposure of the world’s mountains to three drivers of change: climate, land-use, and human population density. We estimated the acceleration of global change in mountain areas by comparing past (1975–2005) versus future (2020–2050) exposure to each driver. We found that Africa’s and Oceania’s mountains face the highest levels of future exposure to multiple drivers of change and will require strong adaptation strategies to preserve human activities and biodiversity. European mountains, in contrast, experience relatively limited exposure to global change, and could act as refugia. This knowledge can be used to prioritize proactive local-scale assessments and intervention to mitigate the risks faced by mountain biodiversity and mountain communities.

Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction

Earth and environmental sciences/Climate sciences/Climate change/Attribution

Earth and environmental sciences/Environmental social sciences/Sustainability

Earth and environmental sciences/Ecology/Climate-change ecology

Human activities during the Anthropocene have caused direct and indirect impacts on biodiversity through phenomena such as climate change, habitat degradation, and the alteration of biogeochemical cycles ^1–4. The rate of global warming over the past 50 years is almost twice as fast as the prior period ⁵ and higher than any 50-year period in the past two millennia ⁶. In parallel, the frequency and intensity of heavy precipitation have increased since the 1950s over most global land areas ⁶. Future projections suggest that climate change will likely occur at an extraordinary rate for Earth's history ⁷, reaching a global warming of 2.1°C to 3.5°C during 2081–2100 compared to 1850–1900 if emission levels follow a business as usual scenario ⁶. This accelerating speed can be disruptive for many global biological systems ⁸. If the rate of these global changes outpaces the capacity of species’ populations to adapt or adjust to new climates, many species will face a higher risk of extinction ^9–11. Climate change is also having strong impacts on human communities, which depends on intact ecosystems functioning ⁴. As a result, many local communities all over the world are changing their livelihood practices to cope with climate change, sometimes with devastating effects on biodiversity ¹².

Mountains cover just over 20% of the earth's surface, but their importance is extraordinary both for humans and biodiversity ¹³. Around 10–20% of world’s population inhabits mountain or sub-mountain areas and directly depend on the ecosystem services that mountains provide ¹⁴. By 2050, it is anticipated that more than 24% of lowland populations will be significantly dependent on runoff from mountains ¹⁵. Mountain areas have provided refuge to many species during past climatic changes, and are regarded as a possible stronghold for many species under future climate change ^16–18. The local topography creates a diverse mosaic of microclimates, which allows species to relocate to suitable habitat within a short linear distance when climate change occurs ^19,20. However, for the same reason of environmental heterogeneity, mountains host many ecosystems and species that are particularly sensitive to changes ^21,22. Many mountain species are shifting their ranges to higher altitudes or northern latitudes ²³, but species restricted to high elevations (i.e., mountain tops) frequently face a progressive reduction in their distribution and an increased probability of extinction ^22,24,25. Moreover, species movement is limited by habitat availability and connectivity ²⁶, thus the effects of climate change on mountain biota are often exacerbated by other drivers, such as land-use and land-cover change ²⁷.

Almost 60% of mountain areas are under intense human pressure, which consists in overexploitation of natural resources, infrastructure development, and land-use change towards pastureland and cropland ²⁶. The magnitude and the impact of human activities in mountains varies spatially across economic development levels and elevational gradients ²⁸. In many developing countries there is a significant increase in mountain land-use change associated with population growth (Körner, 2004; Guarderas et al., 2022; Maeda & Hurskainen, 2014). One such example is Ethiopia, where about 80% of humans and livestock live above 1500 m of altitude and the human population grows by 3% annually ³¹. Recent mountain population growth is also often linked to increasing urbanization, especially in many tropical mountains in Central America, East Africa, the Middle East, and parts of South-East Asia ³². Most of the mountain population live in rural areas, and will be likely more exposed to climate change ^33–35; to cope with the latest, many mountain populations are already modifying the extent and the intensity of land-use activities ^30,36. On the other hand, mountain areas in developed countries, such as the European Alps, have been experiencing a wave of human depopulation and land abandonment often driven by the cessation of pastoralism and other traditional land-use practices ^37,38.

Quantifying the exposure of mountain ecosystems to different anthropogenic pressures is crucial to understand the role that these systems could play as climate refugia or, conversely, as hotspots of risk, for both humans and biodiversity. Projecting future levels of climate change and other pressures is key for anticipating the risks to which mountain biodiversity will be exposed in the future, and planning effective conservation measures which should involve local communities ^20,36. It is necessary to consider different future trajectories of human, social, and economic development and compare these to past trajectories ³⁹. The most common metrics used to quantify the exposure of a system to climate change is the velocity of change ^40–44, which describes the speed and the direction in which a given organism needs to move to maintain the same climatic conditions ⁷. This metric can also be used to assess the exposure of an area to other drivers of change, such as land-use change ^45,46. Another metric, the magnitude of change, is often used as a complement to velocity to measure how much the conditions of a location have changed (or will change) over time ^40,47. As these metrics provide different and complementary information, it is essential to combine them as different regions could be vulnerable to different aspects of global change ^20,46.

Here we assessed the past and future exposure of mountain areas to three global change drivers: climate change, land-use change, and human population change. Our aim was to identify whether mountain areas face higher or lower exposure in the future compared to the past and determine which driver of global change will play a dominant role in different regions. We estimated the velocity of change and the magnitude of change of each driver under alternative future scenarios (time-slice 2020–2050), as well as for a reference period (time-slice 1975–2005). We looked at scenarios SSP-RCP 2-4.5 and SSP-RCP 5-8.5 ⁴⁸, respectively depicting a business-as-usual socio-economic pathway (achieving a global warming of 2.1–3.5°C by 2100) and a high emission pathway (achieving a global warming of 3.3–5.7°C). We compared the velocity and magnitude of change of all drivers of change across 13 mountain areas, classified according to continents and latitude (Nogués-Bravo et al., 2007, Fig. 1). We also compared the levels of global change drivers within mountain areas of the world and in their lowland reference regions, both in terms of absolute risk (e.g., velocity of change in the future) as well as relative risk (e.g., future vs past velocity or “acceleration”). We identified mountain areas that could act as future refuges from global change, and those which will be exposed to a high risk for one or more drivers.

GLOBAL CHANGE WITHIN MOUNTAIN AREAS

Land-use change and especially climate change showed worldwide lower velocity in mountains compared to lowlands, both in the past (1975–2005) and under future emission scenarios (Fig. 2, Table S1, Fig.S1). These velocities were projected to increase in the future, with the highest climate velocity under scenario SSP-RCP 5-8.5 (median mountain velocity = 0.61 km/year, se = 1.52e-03) and the highest land-use velocity under scenario SSP-RCP 2-4.5 (median mountain velocity = 0.99 km/year, se = 4.86e-03). In contrast, we found that human population density in mountain areas was predicted to decelerate in the future (median velocity SSP-RCP 2-4.5 = -0.05 km/year, se = 2.15e-04, median velocity SSP-RCP 5-8.5 = -0.13 km/year, se = 3.56e-04) compared to the past (median velocity = 0.58 km/year, se = 9.72e-03), reflecting a negative trend in global population growth.

On the contrary, the magnitude of change of all drivers was higher in mountains than lowlands, both in the past and under future emission scenarios (Fig.S2, Fig.S3, Table S2). The only exception was that of past human population density, which showed a higher magnitude of change in the lowlands. Similar to the velocity, the magnitude of population change had negative values under both future scenarios. The relative magnitude of climate change in mountain areas was slightly higher in the baseline epoch (median = 0.38, se = 9.01e-02) compared to both scenarios SSP-RCP 2-4.5 (median = 0.25, se = 1.63e-01) and SSP-RCP 5-8.5 (median = 0.30, se = 1.31e-01). Land-use change showed a higher median magnitude under future scenario SSP-RCP 2-4.5 (0.73, se = 7.38e-03), compared to scenario SSP-RCP 5-8.5 (0.23, se = 1.87e-02) and to the past (0.13, se = 1.31e-02).

COMPARISON OF THE DRIVERS ACROSS MOUNTAIN AREAS

We compared the velocity and magnitude of change of all drivers of change across 13 mountain areas, classified according to continents and latitude ²⁸ (Fig. 1). While climate change showed similar velocity across the different mountain areas, land-use change showed less homogeneous values under the future scenario SSP-RCP 5-8.5 (Fig. 3-a, Table S3, Table S4). Looking at the magnitude of change (Fig. 3-b), median values varied consistently across mountain areas both for climate and land use (Table S6, Table S7). Human population velocity and magnitude of change showed negative values across all mountain areas in the future, with few exceptions very close to 0 (Table S5, Table S8).

Two mountain areas showed overall high exposure for both climate and land-use under scenario SSP-RCP 5-8.5 (in terms of magnitude and velocity): Low Africa and Oceania. Oppositely mountain areas with an overall low exposure to climate and land-use change included High Europe, High North America, and Middle South America.

Mountains in the Low-Africa and Oceania regions were the ones most exposed to land-use change and climate change also when considering scenario SSP-RCP 2-4.5 (Fig. S4), both in terms of velocity and magnitude. Land-use velocity was particularly high in Low-Africa under this scenario (median = 2.38 km/year). Other mountain areas showing high exposure to global change under scenario SSP-RCP 2-4.5 were Low-Asia (showing the highest climate change velocity of all mountain areas (median = 0.64 km/year) and the highest relative magnitude of land-use change (median = 1.07)), Oceania and Middle South America, with particularly high land-use velocity (median = 2.34 km/year). Regions with an overall low exposure to climate and land-use change under scenario SSP-RCP 2-4.5 were High Europe and High North America.

The three drivers of change showed different trends in the past compared to the future (Fig. S5). Human population density has been a relevant driver of past change for many mountain areas, both in terms of velocity and magnitude. Middle South America, for instance, showed the highest values in terms of velocity of human population change (median = 1.82 km/year), while Low Africa showed the highest values in terms of relative magnitude of human population change (median = 1.27). Middle South Africa showed a very high relative magnitude of past climate change (median = 2.32) and very high velocity of past land-use change (median = 1.31 km/year), not reflected in future scenarios for this region.

THE ACCELERATION OF THE EXPOSURE ACROSS MOUNTAIN AREAS

The acceleration, i.e., the difference in velocity of change between the future (scenario SSP-SSP-RCP 5-8.5) and the baseline epoch, was always positive for climate change and for land-use change in mountains, and always negative for human population (Fig. 4 -a). We compared the acceleration and the delta-magnitude (i.e., the difference between future and past magnitude) of each mountain area with its reference lowland region, that is the portion of the globe divided according to continent and latitude which contains that mountain region. Under scenario SSP-RCP 5-8.5, all mountain areas showed lower climatic acceleration than the reference lowland regions, but higher land-use acceleration. Low Asia and Middle South Africa hosted mountain areas with the highest climatic acceleration (respectively + 0.38 km/year and + 0.28 km/year) (Table S3), while Oceania and Low Africa showed the highest acceleration for land-use change (respectively + 2.66 km/year and + 2.50 km/year) (Table S4). The acceleration of the human population was instead always negative, meaning a decrease in the velocity of change from the baseline epoch to the future (Table S5).

Similar to the acceleration of climate change, the Δ-magnitude of climate change, was always positive under this future scenario in the mountains (Fig. 4-b, Table S6). This was also the case for land-use change, with the exception of Low America and Middle South America (Table S7). Human population again showed negative Δ-magnitude values except for Oceania (Table S8). Δ-magnitude, unlike the acceleration, was generally always higher in mountain areas than in the reference regions for both climate and land-use, with a few exceptions (i.e. Middle South America, Middle Asia, High Europe in terms of climate change and Oceania for land-use change). The mountain areas with the highest Δ-magnitude of change for climate were Middle South Africa (+ 1.68) and Oceania (+ 1.40). Low Africa (+ 0.95) and Oceania (+ 0.62) showed the highest Δ-magnitudes for land-use.

Under scenario SSP-RCP 2-4.5 climate showed an overall lower acceleration across mountains compared to the other scenario, while land-use generally showed both a greater acceleration and a greater Δ-magnitude. Climate change acceleration was even negative for mountains in High Europe, High Asia, and Middle Europe (Fig.S6-a). The acceleration of land-use change was always positive instead, with the highest values for Middle South America (+ 2.34) and Low Africa (+ 2.27). The highest Δ-magnitudes of climate change were in mountains of Middle South Africa (+ 1.70), Middle North Africa (+ 1.13) and Oceania (+ 1.05), while the highest values for land-use change in Oceania (+ 0.81) and High Asia (+ 0.70) (Fig. S7-b).

The exposure of mountain areas to drivers of global change is complex and multi-faceted. The pattern of the exposure varies across mountain regions, reflecting the high diversity of the mountains of the globe in terms of different environmental, socio-economic, and political conditions. However, we observed some common trends. First, we found that globally land-use change and especially climate change showed lower velocity of change in mountains compared to the lowlands, both in the past (1975–2005) and under future emission scenarios. Conversely, the magnitude of change was always higher in mountains than the lowlands for these two drivers. The human population density change showed positive values in the past (both velocity and magnitude) and negative values in the future, thus proving to be a more relevant driver of change for the baseline epoch. Both the acceleration and the Δ-magnitude of climate and land-use are positive in most mountain areas, thus showing an increasing future exposure of that regions to these two drivers.

Mountain areas with a high velocity and a high magnitude of both climate change and land-use change will probably be more at risk in the future (e.g., African mountains and Oceania’s mountains). In these areas, the impact of climate change will be significant and will be exacerbated by the synergistic action of land use change. Areas with both low velocities and low magnitudes of climate change are instead areas that will conceivably retain current climates and could act as climate refugia ^49,50. Among these there are Europe or North America (High Europe and Middle North America), or the south-Andes mountain range (Middle South America).

THE RELEVANCE OF CONSIDERING DIFFERENT METRICS AND DIFFERENT DRIVERS

Mountains showed lower velocities of climate and land-use change compared to the lowlands. The capacity of complex topography to provide a spatial buffer for climate change has been recognized qualitatively and assessed at different scales ^7,17,42, and our results highlight that mountain areas could act potentially as climatic refugia, as they already did in past climatic shifts ^{16–18, 51}. However, we also found that the magnitude of change was higher in mountains than the lowlands, both in the past and under future emission scenarios. This result underlines the importance of considering the two metrics together to assess the effective exposure of mountain areas to global change drivers ²⁰. The velocity of change in fact does not inform about how much environmental conditions will change in the area, but it just describes the distance and speed that the organisms must cover to maintain the same conditions ^19,40. The velocity of change is also intrinsically strongly related to the spatial gradient of the variable under consideration. Thus, in general, mountains have lower velocities of change than the lowlands, and mountains with highest spatial gradients (i.e. topographic complexity), showed lower velocities of change than others ⁴⁰ as is the case for Low America and Middle South America (i.e. the Andean mountain range). However, topographic complexity can hinder species dispersal posing risk to species with more limited movement ability ^19,26. Furthermore, a species may find suitable climatic conditions nearby, but perhaps not suitable habitat conditions due, for instance, to a change in land-use or fragmentation ^22,26,45. Mapping different metrics of climate and land-use change enables identifying areas where species may undergo rapid shifts in their optimal climatic conditions while simultaneously having their potential movement constrained by land-use change. In our work, we have focused only on some of the key drivers of change, even if there are other drivers which could be important in specific contexts such as unsustainable tourism ⁵². Likewise our global-scale assessment of human pressure did not consider variables which might be locally important, especially for some species, such as microclimatic conditions ^40,53, solar radiation, wind direction and substrate ^54–57.

MOUNTAIN AREAS AS HOTSPOTS OF RISK

We identified mountains with a high velocity and a high magnitude of both climate change and land-use change as potential hotspots of risk for human and for biodiversity (e.g., African mountains and Oceania’s mountains). These areas also showed a high relative exposure (i.e., high acceleration and high delta magnitude), meaning that in the future they will be more exposed to global changes than they were in the past. Climate change has been particularly relevant in the African continent over the last century ^58,59 and is now interacting with habitat destruction, land-use change and fragmentation with negative effects on biodiversity and human communities ^60–62. Specifically in East Africa, where many mountains are found, climate change is interacting with land-use change in altering the distributions of the species ^63,64 and their migratory patterns (e.g., herbivores), increasing conflicts with humans and leading to a population decline for large mammals ⁶⁵. In Ethiopian and Kenyan mountain areas, projections of climate change by the mid-21st century are characterized by an increase of temperature, increased frequency of extreme events, and alteration of precipitation patterns, with wetter rainy seasons in Kenya and prolonged aridity in Ethiopian highlands ⁶⁶. Even under a business-as-usual scenario, in those areas human population and activities will be concentrated at high altitudes, triggering cascade effects on remaining forest cover, biodiversity and ecosystem services ³⁶.

Rural communities are recognised as one of the most vulnerable populations to climate change ^33–35, and the proportion of poor and vulnerable people increases with elevation ^67,68. Restoration interventions and adaptation strategies, which may debunk the past and the current effects of climate change on biodiversity, must involve local mountain communities, to avoid that the response of these populations to climate change results in an unsustainable land management ^36,69. In this sense, it is important to engage multiple stakeholders and using a participatory and place-based approach ⁷⁰, which should include the indigenous knowledge and the response to local climate effects ⁶⁹.

Australian and New Zealand mountains are also among the most exposed to global changes in our analyses. From 1910 to 2011 the Australian continent has warmed by 0.9°C, compared to a global average of 0.7◦C for the same time interval ⁷¹. Climate change was certainly a driving force in the encroachment of rainforest into eucalyptus woods, or the establishment of trees in subalpine meadows, as well in determining shifts in species distributions ⁷². The Australian Alps are among the areas more rich in biodiversity but also most vulnerable to climate change and other anthropogenic stressors ⁷³. Future projections suggest that climate changes are expected to have considerable impacts on Australia’s biodiversity on different aspects, but the evidence shows that Australia is still in the early phases of considering climate change adaptation and making it part of other aspects of public policy ⁷⁴.

MOUNTAIN AREAS AS CLIMATE REFUGIA

In our analysis mountains of Europe or North America (High Europe and Middle North America) and the south-Andes mountain range (Middle South America) showed both low velocity and low magnitude of climate change. These regions may serve as climatic refugia, protecting species and from exposure to current climate shifts over the long run ^{49, 75–77}. But to be effective, these regions require proactive conservation, e.g., protection from pressures such as overexploitation or unsustainable tourism ^45,52. Integrating this information on potential refugia identification with the measurements of functional connectivity would also be necessary to increase the ecological meaning of velocity-based climate exposure ^78,79. In this sense, our study is potentially useful as a starting point to identify areas able to connect current and future climate analogues along paths that minimize exposure to different climates ^19,80.

Here we have identified regions facing risk from global change drivers and regions expected to act as refugia. This knowledge can be used to prioritize proactive local-scale assessments and adaptation strategies to minimise the risks faced by mountain biodiversity and human communities. Global climate mitigation is paramount to reduce the risk of high magnitude and velocity of change in mountains, but this needs to be coupled with sustainable land-use policy that together prevent local degradation of important areas for biodiversity ⁸¹. Many human populations living in mountain areas will be more affected by climatic shifts than populations in other areas of the world, with a consequent alteration of activities in those areas and an increasing conflict with biodiversity that might be difficult to predict and manage.

Delineation of mountain areas

We used the mountain inventory 1.2 data set produced by the Global Mountain Biodiversity Assessment (GMBA) to identify mountainous areas worldwide ⁸². This classification is based on ruggedness, defined as the maximal elevational difference among neighbouring grid points. A pixel is defined as rugged (i.e., mountainous) when the difference between the lowest and the highest of the 9 neighbouring 30 arcsec cells in a 2.5 × 2.5 arcmin area exceeds 200 m. This classification also considers climate, by defining different climatic belts, which derive from elevational belts that account for the rise of isotherms toward the equator. We then grouped mountain areas into regions, following Nogués-Bravo et al., 2007, who grouped the World Mountain Map ⁸³ by continents and latitude using the Holdridge classification ⁸⁴. Thus, 13 different mountain areas were considered: (1) America high-latitude mountains at northern hemisphere (High North America); (2) America mid-latitude mountains at northern hemisphere (Middle North America); (3) America low-latitude mountains (Low America); (4) America mid-latitude mountains at southern hemisphere (Middle South America); (5) Europe high-latitude mountains (High Europe); (6) Europe mid-latitude mountains (Middle Europe); (7) Africa mid-latitude mountains at northern hemisphere (Middle North Africa); (8) Africa low-latitude mountains (Low Africa); (9) Africa mid-latitude mountains at southern hemisphere (Middle South Africa); (10) Asia high latitude mountains (High Asia); (11) Asia mid-latitude mountains (Middle Asia); (12) Asia low-latitude mountains (Low Asia); (13) Australia and New Zealand (Oceania) (Fig. 1). We then associated each mountain region with a reference region, i.e., the area outside mountains in each continent and latitudinal band considered.

Drivers of global change

We quantified the exposure of the world’s mountain areas to three major global change drivers: climate change, land-use change, and human population density. We focused on two different climatic variables: average monthly temperature (C°) and monthly mean precipitation amount (g/m3), extracted from CHELSA V1.2 database ⁸⁵. Temperature is the standard variable used in calculating the velocity of climate change ^7,45,46 and we decided to also consider precipitation which is often included in climate-velocity analyses, as they regulate the distribution and productivity of plant communities ^40,45. We obtained the yearly mean temperature by averaging the monthly maximum and minimum temperature values across each year. We estimated the velocity of change and the magnitude of change separately for mean temperature and precipitation, and then we added the absolute values of the two climatic variables to consider the combined effect of climate change ⁴⁵. We decided to use the absolute values as if temperature and precipitation velocities have opposing directions that are equal in magnitude, they would cancel, even if the climatic conditions are not the same as the original ⁴⁰.

We extracted yearly land-use data from the Land-Use Harmonization (LUH2) dataset ⁸⁶. We decided to focus on anthropic land-use categories which are considered to have overall negative impacts on biodiversity ⁸⁷: cropland, pastureland, and urban.

We extracted human population density data from the human population’s count scenarios of the dataset of Olén & Lehsten, 2022, at 5-year intervals. Since we did not have past human population data from Olén & Lehsten, we used a different dataset ⁸⁹ to extrapolate them, after verifying the spatial correlation between the two data in the present (Pearson’s coefficient = 0.80). We added a value of 1 to the rasters of the human population count used for interpolation to avoid having NA or infinite values in the final raster.

Data had a native original resolution of 1km for population data, 5 km for climate, and 25 km for land-use. We re-aggregated human population data at a resolution of 5 km to reduce the difference with land-use data, and we analyzed the other datasets at their native resolution.

Metrics of global change

For each driver of global change, we estimated two different metrics, the velocity and the magnitude of change. We estimated each metric for a time slice of 30 years, both in the past (1975–2005) and in the future (2020–2050), under two different emission scenarios, the SSP-RCP 2-4.5 and the SSP-RCP 5-8.5 (Tebaldi et al., 2020). According to SSP 2-4.5, global warming is estimated to be between 2.1 and 3.5 degrees Celsius in 2100, with development trends remaining mostly consistent with historical patterns ^6,91. The SSP 5-8.5 scenario projects the highest emissions, with global warming by 3.3°C to 5.7°C °C by 2100 ^6,92.

We estimated the “acceleration” of each driver as the difference between the past and future velocity of change, and the Δ-magnitude as the difference between the past and future magnitude of change.

The velocity of change is a vector that describes how fast and in which direction a point on a gridded map would need to move to remain static in the same environmental conditions ( e.g., to maintain an isoline of a climate variable in the climatic space, under climate change) ⁴⁰. We measured the velocity of change following the gradient-based approach ⁷, and using the gVoCC package ⁹³ implemented in R (version 4.2.2, R Core team, 2022). Here the velocity of change is defined as the ratio between a temporal trend (the rate of change of each variable through time, estimated as a regression slope) and the corresponding spatial gradient of that variable (i.e., vector sum of longitudinal and latitudinal pairwise differences at each focal cell using a 3 x 3-cell neighbourhood):

$\frac{slope (unit/year)}{spatial gradient (unit/km)}$ = VoCC (km/year) (1)

To estimate climate change velocity, we focused on mean temperature (C°) and precipitation amount (g/m3). We considered 4 Global Circulation Models (GCMs) for each future scenario, derived from the Coupled Model Intercomparison Project 5 (CMIP5): ACCESS 1–3, CESM1-BGC, CMCC-CM, MIROC 5. We obtained the climate velocity for each GCM under each emission scenario, and we averaged velocity estimates between GCMs for the same variable within each RCP scenario to produce an ensemble estimate. We also measured the uncertainty of the estimates by calculating the standard error across values.

We estimated the future velocity of change of land-use according to the same emission scenarios considered for climate (SSP 2-RCP4.5 and the SSP5-RCP8.5). In this case, we estimated the velocity of change for the three different categories of land-use (i.e., cropland, pastureland and urban) separately and then we summed the absolute values to obtain the overall velocity of land-use change.

We estimated the velocity of change of the human population density, extracting human population’s count scenarios of Coupled Model Intercomparison Project 6 (CMIP6) from the dataset of ⁸⁸. For this variable, we scaled all values in log10 to reduce the skewness of data. The choice to use two different CMIPs, respectively CMIP5 for climate and CMIP6 for population density, was forced by the need of data with the finest possible temporal resolution, to estimate the velocity of change. However, this has not affected our evaluation of multiple global change drivers, as we scaled the velocity and magnitude of each driver for our comparison.

To quantify the acceleration of global change across the world’s mountains, we calculated the difference between the future velocity of change and the velocity of change of the baseline epoch. This allowed us to compare the velocity of change between the past and the future.

We estimated the magnitude of change for the same variables used to estimate velocity (i.e., climate, land use, human population density), for the baseline epoch (1975–2005) and future epoch (2020–2050). Following Nogués-Bravo et al., 2007, we estimated the magnitude of change according to the following formula:

$$M = \frac{(Xt2-Xt1)}{Xt1}$$

Where x_t1 and x_t2 represent values for the variable x at the beginning and at the end of the period.

To estimate values of the climatic variables, we considered the average values of the 10 years before and the 10 years after the reference year. For instance, for the historical epoch we considered the average values from 1965 to 1985 to estimate 1975 (t1) and the average values from 1995 to 2005 to estimate 2005 (t2).

We scaled the values of each variable based on the mean and the standard deviation of the year 2000, to obtain comparable estimates. As we did for the velocity of change, we considered different Global Circulation Models under two emission scenarios (i.e., SSP-RCP 2-4.5 and SSP-RCP 5-8.5), and then we averaged the magnitude estimates between GCMs within an RCP scenario to produce an ensemble estimate. We quantified the difference in magnitude of change across the world’s mountains (Δ-magnitude), as the difference between future magnitude and the magnitude in the baseline epoch. Such Δ-magnitude for climate is estimated as the sum of the absolute values of the differences estimated separately for temperature and precipitation.

We did not perform statistical tests to determine the significance of the differences observed between different epochs and regions, as in each comparison we consider the entire statistical population of values (i.e., every pixel). We represented the entire distribution of values for each region (mountain and lowland reference) and each epoch to allow for a full comparison (Fig. S7-S12).

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The exposure of the world’s mountains to global change drivers

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Abstract

Figures

Main

RESULTS

GLOBAL CHANGE WITHIN MOUNTAIN AREAS

COMPARISON OF THE DRIVERS ACROSS MOUNTAIN AREAS

THE ACCELERATION OF THE EXPOSURE ACROSS MOUNTAIN AREAS

DISCUSSION

THE RELEVANCE OF CONSIDERING DIFFERENT METRICS AND DIFFERENT DRIVERS

MOUNTAIN AREAS AS HOTSPOTS OF RISK

MOUNTAIN AREAS AS CLIMATE REFUGIA

METHODS

Delineation of mountain areas

Drivers of global change

Metrics of global change

References

Additional Declarations

Supplementary Files

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