Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

Abstract

Some of the largest impacts of climate change are expected in the environmentally heterogeneous and species rich high mountain ecosystems. Among those, the Neotropical alpine grassland above the tree line (c. 2,800 m), known as Páramo, is the fastest evolving biodiversity hotspot on earth, and one of the most threatened. Yet, predicting climate responses of typically slow-growing, long-lived plant linages in this unique high mountain ecosystem remains challenging. Here we coupled climate sensitivity modeling and adaptive potential inferences to efficiently assess climate vulnerability of Espeletia, Páramo’s most iconic, predominant and rapidly evolving plant complex. In order to estimate climate sensitivity, we first modeled the distribution of 28 Espeletia taxa under a niche conservatism scenario using altitude and five current (1970–2000) and future (2050 RCP 8.5) bioclimatic variables across 36 different Páramo complexes in the northern Andes (49% of the world’s Páramo area). As an alternative to range shifts via migration, we also computed the adaptive capacity of these Páramo complexes by considering three enhancing factors of the biodiversity’s adaptive potential as well as three environmental limiting factors of the populations’ plastic response. These predictors showed that diverse Páramos in the Eastern Cordillera were more vulnerable likely because the counteracting effects of the adaptive potential (r = −0.93 ± 0.01) were not sufficient to buffer higher distribution losses (r = 0.39 ± 0.01). Agriculture (r = −0.48 ± 0.01), mining (r = −0.36 ± 0.01), and rural population density (r = −0.23 ± 0.01) also weakened the adaptive capacity. These results speak for a limited persistence via migration in the short-term responses of Espeletia to climate change, even though the past population dynamics in concert with glacial cycling is indicative of a predominant role of range shifts. Furthermore, changing climate, together with a general inability to adapt, may eventually constrain the rapid diversification in the Espeletia complex. Our integrative modeling illustrates how future climate may impact plant populations in a mega diverse and highly threatened ecosystem such as the Páramo, and encourages carrying out similar estimates in diverse plant complexes across other high mountain and island-like ecosystems.

Introduction

How tropical alpine biodiversity hotspots are affected and will react to climate change is among the most pressing questions in current biological research. Most studies on responses to changing climate assume populations migration to higher altitudes so that organisms would track their ecological niche (Lenoir et al., 2008; Steinbauer et al., 2018), and in some cases even to lower altitudes due to competitive release (Lenoir et al., 2010). Yet, when migration potential is restricted (Jump and Penuelas, 2005; Lees et al., 2020), the only way left for organisms to persist is by adjusting to the new environmental conditions, a traditionally neglected alternative in niche modeling. Adjustment via phenotypic plasticity is presumably important in long-lived taxa because plastic responses can happen within the lifetime of an individual (Nicotra et al., 2010). However, plasticity may be hampered when populations face novel anthropic conditions not found in their evolutionary history (Fox et al., 2019; Kelly, 2019), e.g., agriculture, mining and population density. Organisms can also adjust to climate change through adaptation (Waldvogel et al., 2020). Still, populations may not have enough adaptive potential (e.g. limited diversity) to keep up with the pace of climate change (Hoffmann and Sgro, 2011). Therefore, assessing biodiversity hotspots’ vulnerability to climate change not only requires tracing ecological niche gaps under future scenarios – i.e., sensitivity (Berry et al., 2003), but also needs to explicitly account for the adaptive potential (Razgour et al., 2019) and plasticity to anthropic pressures. These improvements may make predictions more congruent with empirical data, even in potential climate refugia (Sweet et al., 2019).

One of the fastest evolving biodiversity hotspots is the Páramo (Madriñán et al., 2013), a highly threated (Vásquez et al., 2015; Pérez-Escobar et al., 2018) alpine ecosystem dominated by diverse grasslands above the treeline in the American tropics (at elevations of ca. 2,800–5,000 m). It is a main water supplier of wetland ecosystems and densely populated areas in the northern Andes (Cuesta et al., 2019a; Llambí et al., 2020). Despite it has a relatively small surface area (35,000 km²), it contains over 3,000 plant species, several of which are endemic (Luteyn, 1999; Hughes and Atchison, 2015) and emerged as unique adaptations to extreme environments that evolved during the Andean uplift in the last five million years (Antonelli et al., 2009; Madriñán et al., 2013; Mutke et al., 2014). Unparalleled diversification rates across plant groups at these tropical “sky islands” (Sklenáø et al., 2014) has further been attributed to colonization by pre-adapted linages (Muellner-Riehl, 2019), and Pleistocene glacial cycling (Nevado et al., 2018; Perrigo et al., 2019; Rahbek et al., 2019) in the last 2.4 Myr that led to repeated periods of connectivity and spatial isolation (i.e., “species pump hypothesis”). Population fluctuations in concert with past glacial cycling indicate a general inability to adapt and a major role of range shifts (Martín-Bravo et al., 2010; Ronikier, 2011; Hazzi et al., 2018; Muellner-Riehl, 2019). Yet, it remains to be explored whether the rapidly evolving Páramo can keep pace with the quick rate of climate change and human expansion.

The most iconic, abundant and diverse group in the Páramo is the Espeletia complex (Monasterio and Sarmiento, 1991; Madriñán et al., 2013; Mavárez, 2019b). It originated in the Venezuelan Andes (Pouchon et al., 2018) from where it colonized southwards the Colombian Eastern Cordillera and the Ecuadorian Andes (Mavárez, 2019b), followed by a northwards migration into the Colombian Central and Western Cordilleras (Cuatrecasas, 2013). Espeletia taxa (ca. 120) are ecologically abundant across the Páramo landscape (Luteyn, 1999), occurring in highly heterogeneous habitats. Espeletia populations are adapted to grow in the wet depressions of high valleys, in the dry exposed slopes or even within the forests at the tree line, therefore experiencing a wide range of climatic conditions within elevations and localities (Peyre et al., 2018). This environmental heterogeneity may have important implications for the reaction of Espeletia to changing climatic conditions. For example, habitat variation may provide suitable locations for migrants within only a few meters of their current locations (Scherrer and Körner, 2011). However, populations adapted to a narrow range of conditions may respond poorly to future threats. In other words, environmental heterogeneity effects on the responses to climate change may be mixed, and sometime contradictory, especially for a long living tropical alpine plant with limited seed dispersal, such as Espeletia. Hence, a much-needed research is to balance climate sensitivity under the current ecological niche heterogeneity with the adaptive capacity of Espeletia to assess the climate vulnerability of this plant complex.

In order to bridge this gap, our goals in this study were to (1) quantify climate sensitivity in the Espeletia complex by comparing current and future (2050 RCP 8.5) distributions under a niche conservatism scenario (i.e., range shift via migration), (2) compute the adaptive capacity by incorporating enhancing factors of the biodiversity’s adaptive potential (i.e., diversity, protected areas, and forest area in the buffer zone of the Páramo) as well as limiting factors of the plastic response (i.e., agricultural area, mining, and population density), and (3) estimate climate vulnerability of Espeletia by simultaneously coupling its climate sensitivity with its adaptive capacity. This work will help establishing how plant populations may respond to environmental change in a mega diverse tropical alpine region, where the largest impacts of climate pressures are expected (Körner, 2003; Rumpf et al., 2018).

Materials and Methods

Study Area

The study area comprised the Colombian Andean Páramo biogeographical province (2,732–5,212 m, Figure 1), including the high-elevation northern Andes and the Sierra Nevada de Santa Marta, but excluding areas with similar altitudes in the Cordillera de Merida and Central American mountains (Figure 2). It was delimited in the north by the Sierra Nevada of Santa Marta (11°N), and in the south by the Colombia-Ecuador border (1 N). It spanned 36 Páramo complexes, as in Morales et al. (2007) and Londoño et al. (2014), denoting 49% of the world’s Páramos. The surveyed Páramo zones were covered in a 33% by protected areas¹, and surrounded in a 30% by forest patches². Meanwhile, agriculture and mining, respectively, disturbed 6 and 7%³ of these Páramos and their buffer belt (2 km around the tree line). The handling of these data sources is detailed below as part of the computation of the “Adaptive Capacity at the Study Area.”

FIGURE 1

FIGURE 2

Climate Sensitivity in the Espeletia Complex

Climate sensitivity in the Espeletia complex was modeled by comparing current and future (2050 RCP 8.5) distributions under a niche conservatism scenario (i.e., range shift via migration). Occurrences of Espeletia samples for the 36 Colombian Páramo complexes were downloaded as georeferenced specimen data from providers served by GBIF⁴. From an initial set of 15,466 specimen records downloaded, a total of 1,432 (Supplementary Table S1), comprising 28 different Espeletia taxa according to Cuatrecasas (2013) – Supplementary Table S2, were retained after filtering for georeferencing consistency (i.e., non-redundant records within the study area) and number of records. In order to avoid bias due to distributions with low number of input points (Wisz et al., 2008), specially for presumably rare taxa (Fois et al., 2018), only putative species with at least 10 original records were retained (three taxa with more than 100 records, Supplementary Figure S1A). This threshold follows Van Proosdij et al. (2016) recommendation for narrow-ranged species, that is those with a moderate prevalence class (i.e., 27 taxa were present in less than seven different Páramo complexes, and only one taxon was present in 20 Páramos, Supplementary Figure S1B). This latter trend is expected in Espeletia species, for which distance (Diazgranados and Barber, 2017; Padilla-González et al., 2017) and ecology (Cortés et al., 2018a) are the major speciation drivers.

Historical climate data was extracted from the WorldClim⁵ database, at a 30 s resolution, using the georeferencing of each record. All 19 bioclimatic variables were downloaded in the baseline period 1970–2000. The same variables projected to 2050, under the IPCC RCP 8.5 scenario, were gathered using CCAFS’s downscaled delta method (Navarro-Racines et al., 2020). This projection averages 33 different global climatic models at a 1 km² resolution. A Digital Elevation Model (DEM) variable was further included. Prioritization of environmental variables (Zeng et al., 2016) is crucial to enhance model complexity (Warren and Seifert, 2011) for climate sensitivity assessments. By improving variable selection (Cobos et al., 2019), collinearity (Feng et al., 2019) and sampling bias (Syfert et al., 2013) can be minimized. Collinearity in particular refers to a strong correlation between two or more predictor variables, and may cause instability in parameter estimation (Dormann et al., 2013). A measure to diagnose collinearity is the Variance Inflation Factor (VIF), the square of the multiple correlation coefficient resulting from regressing a predictor variable against all other predictor variables (Chatterjee and Hadi, 2006). Hence, we performed variable selection by computing the VIF score using the usdm (Uncertainty Analysis for Species Distribution Models) package (Naimi et al., 2014) in R v.3.3.1 (R Core Team), and drew Pairwise Pearson correlations (r) among all variables by means of the same software (Supplementary Figure S2). Five bioclimatic variables and DEM did not exhibit collinearity (Naimi et al., 2014) at VIF < 10 (Supplementary Table S3), with absolute r-values below 0.7 from 0.05 to 0.65, and therefore were retained hereinafter.

The six non-collinear variables fed Maxent model v.3.4.1. (Phillips et al., 2017), a “machine learning” outline that uses maximum entropy and Bayesian inference to estimate occurrence probability distributions. Ten split-sample models were run, after checking for stabilization in the ROC curves (Spiers et al., 2018), with an average of 10 k pseudo-absences for each of the 28 putative taxa. Each replicated run type was set to “cross-validate” with 500 iterations (Buffum et al., 2015). Presence ≥ 95% was binary reclassified (Liu et al., 2005) and the average across repetitions was recorded in each case. This procedure was performed for the historical dataset (1970–2000). To predict distribution changes of Espeletia, we projected trained models on current presence localities and present background climate to 2050 under the RCP 8.5 scenario through Maxent’s “projection layer” tool. Current model validation considered AUC, an aggregate measure of performance across all possible classification thresholds. Minimum average testing AUC was above 0.98 (Supplementary Table S4). As AUC alone (Supplementary Table S5) may be deceiving (Lobo et al., 2008; West et al., 2016), the AIC and BIC criteria were also computed and checked for consistency (Supplementary Table S6).

All 56 distribution maps were converted to binary data, using a threshold of the 20th percentile, as suggested by the Maxent v.3.4.1 software (Phillips et al., 2017). These maps were then averaged to generate spatial information of current and future presence probabilities across all 28 taxa. Finally, the current distribution map was subtracted to the one obtained under a climate change scenario in order to estimate climate sensitivity in the Espeletia complex. Values close to zero in the sensitivity index meant few changes in the forecasted distribution of Espeletia due to climatic variation. On the other hand, negative and positive values corresponded to areas where the presence probabilities were, respectively, diminished or boosted by climate change.

Adaptive Capacity at the Study Area

We computed the adaptive capacity as the scaled additive effects of six main variables that expressed the capacity of the overall Espeletia biodiversity to respond without migration to the effects of climate change. Enhancing factors of the biodiversity’s adaptive potential were diversity, protected areas, and forest area around the Páramo. On the contrary, limiting factors of the plastic response were agriculture, mining, and population density.

Diversity may buffer the effects of climate change by enriching complex interactions, such as adaptive inter-specific introgression (Marques et al., 2019) and hybrid speciation (Coyne and Orr, 2004; Abbott et al., 2013; Payseur and Rieseberg, 2016). Hybridization may allow for local adaptation in transitional environments and bi-directional transfer of adaptive variation through introgression (Isabel et al., 2020). Espeletia in particular exhibits weak species boundaries, rampant gene-flow and reticulate evolution (Cortés et al., 2018a; Pouchon et al., 2018), leading to a high incidence of natural hybrids (Rauscher, 2002; Cuatrecasas, 2013). In order to account for the enhancing role of diversity on the adaptive potential, the number of Espeletia taxa was totalized in a 1 km grid using the occurrence data downloaded from GBIF (see footnote), as described in the previous section, and scaled from 0 to 1.

Another enhancing factor of the biodiversity’s adaptive potential is protected areas, where human intervention does not represent a direct threat, allowing biodiversity to be conserved while naturally coping with climate change. Protected areas selected for this analysis spanned 1,050 natural areas, according to the System of National Natural Parks (see footnote), and included 43 National Natural Parks, 663 Civil Society Reserves, 54 Regional Natural Parks, and 57 National Protected Forest Reserves. These categories were, respectively, rated as 1, 0.3, 0.1, and 0.1, following CIAT (2017). Higher ratings were assigned to areas that presumably provide greater protection for biodiversity due to their low anthropogenic pressure and extent.

Forest patches around the Páramo complexes (Henao-Díaz, 2019) may also contribute buffering climate change effects by facilitating (Bueno and Llambí, 2015) migration and gene-flow, which could increase the frequency of existing genetic variants adapted to particular conditions (Bridle and Vines, 2007). Additionally, forests provide conditions that may favor thermal and water regulation, decreasing the magnitude of climate change itself. Therefore, forest area in the buffer zone of the Páramo (2 km around the tree line) was gathered from the IDEAM (see footnote), and converted to binary points (1 = presence) in a 1 km grid.

On the other hand, major limiting factors of the plastic response are agriculture (Avellaneda-Torres et al., 2020), mining (Pérez-Escobar et al., 2018) and population density within the study area and in its buffer zone (Vásquez et al., 2015). In order to account for these threats, agricultural and mining areas were downloaded from the Geographic Institute Agustín Codazzi (see footnote) for the period 2010–2012, and transformed to binary data (0 = presence) in a 1 km grid within the study area and in its buffer zone (2 km around the tree line). The population density indicator was computed as the inverse value of the normalized rural population density, scaled from 0 to 1, so that 1 meant absence of human settlements and 0 was the population density at the most populated rural zones in the study area. The population information was gathered from data projected to 2015 by the National Administrative Department of Statistics⁶.

Climate Vulnerability Index for Espeletia

In order to estimate climate vulnerability at the overall Espeletia complex, we simultaneously coupled its climate sensitivity with its adaptive capacity. Negative values of the sensitivity index meant areas where the presence probabilities of Espeletia were diminished due to climate change. Meanwhile, lower adaptive capacity scores spoke for a prominent role of limiting factors of the plastic response (i.e., agriculture, mining, and population density) as compared to enhancing drivers of the biodiversity’s adaptive potential (i.e., diversity, protected areas, and forest area around the Páramo). Therefore, the climate vulnerability score was computed as the complement value of the additive contributions of the sensitivity and the adaptive capacity indices. The climate vulnerability score was scaled from 0 to 1 for comparative purposes. Pairwise Pearson correlations (r) among all estimated scores were computed, and boxplots and ridgeline plots were drawn for the three indices in all 36 Páramo complexes. Computing and plotting were done in R v.3.3.1 (R Core Team).

Results

Climate Sensitivity of Espeletia Varied Widely Across Páramo Complexes

Climate sensitivity was estimated across all 36 Colombian Páramo complexes by comparing current (baseline period 1970–2000, Figure 2A) and future (2050 RCP 8.5, Figure 2B) average distribution probabilities of 28 different Espeletia taxa (nine had average sensitivity scores below zero). Values surrounding zero in the sensitivity index (−0.029 to 0.030, 42% of the study area, Figure 2C) stood for few changes in the forecasted distribution of Espeletia due to climate change, and were predominant in the north of the Eastern Cordillera, the Cruz Verde-Sumapaz Páramo complex south of the Bogotá montane savanna (the southwestern wetland complex of the larger Eastern Cordillera’s Andean highland plateau), and the small sky islands of the Western Cordillera.

Given a niche conservatism scenario, negative estimates of the climate sensitivity (≤−0.030, 32% of the study area, Figure 2C) predicted range losses based on the averaged presence probabilities. Páramo areas more likely to experience range losses were those in the Sierra Nevada de Santa Marta, and the Central Cordillera South of Las Hermosas (i.e., Nevado del Huila-Moras, and Guanacas-Puracé-Coconuco), as well as those in the northern corner of the Eastern Cordillera (i.e., Sierra Nevada del Cocuy, Pisba, and Tota-Bijagual-Mamapacha), where diverse summits surrounded the topographically intricate Chichamocha inter-Andean canyon. On the other hand, range shifts via migration enriched presence probabilities in other localities (positive estimates of the climate sensitivity ≥ 0.031, 27% of the study area, Figure 2C). Range gains were only predicted for Páramo areas in the Central Cordillera (Los Nevados, Chilí-Barragán, Las Hermosas, and La Cocha-Patascoy, Supplementary Figure S3) where the dominant taxon was E. hartwegiana, as well as in the summits of the northern tip of the Eastern Cordillera (Perijá).

Adaptive Capacity Was Jeopardized Across Most Páramo Complexes

The adaptive capacity across 36 Colombian Páramo complexes was computed using a scaled additive model of enhancing (Figures 3A–C) and limiting (Figures 3D–F) factors of the biodiversity’s adaptive potential and the plastic response. The adaptive capacity index (Figures 4B, 5A, Supplementary Figure S3A, and Supplementary Table S7) was higher (≥0.72, 34% of the study area) in species-rich Páramo areas (Pearson’s r = 0.19 ± 0.01, Figure 3A and Supplementary Figure S4), and matched extensive National Natural Parks (r = 0.59 ± 0.01, Figure 3B and Supplementary Figure S4), like the Eastern Cordillera’s Sierra Nevada del Cocuy, Chingaza and Sumapaz. The Páramo adaptive capacity index was also moderately superior (0.62–0.71, 29% of the study area, Figure 4B) in the most representative National Natural Parks in the Central Cordillera (Los Nevados and Nevado del Huila) as well as in the Sierra Nevada de Santa Marta. In turn, the western slopes of the Páramo complexes at the Eastern and Central Cordilleras exhibited lower adaptive capacity values (≤0.61, 37% of the study area), and were enclosed by less forest patches (30% on average, Figure 3C, r = 0.39 ± 0.01, Supplementary Figure S4).

FIGURE 3

FIGURE 4

FIGURE 5

However, positive effects due to protected areas and forest coverage on the adaptive potential were offset by limiting factors of the plastic response, primarily agricultural (6% of the study area and its buffer zone, Figure 3D, r = −0.47 ± 0.01, Supplementary Figure S4) and mining (7% of the study area and its buffer zone, Figure 3E, r = −0.41 ± 0.01, Supplementary Figure S4) pressures, mainly in the western slopes of the Páramo areas at the Eastern and Central Cordilleras. This let to the lowest adaptive potential estimates (≤0.52, 20% of the study area, Figure 4B) at the western slopes of the Páramo complexes in the Cauca province, in the Central Cordillera, and the Boyacá and Santander provinces (i.e., Almorzadero and Altiplano Cundiboyacense Páramo complexes), in the Eastern Cordillera. Rural population (Figure 3F) hampered the adaptive potential in all three Cordilleras and Páramo areas (r = −0.28 ± 0.01, Supplementary Figure S4).

Diverse Páramos in the Eastern Cordillera Were More Vulnerable

The scaled complement of the additive contributions of the sensitivity (Figures 2C, 5B and Supplementary Figure S3B) and the adaptive capacity (Figure 4B) indices were used to compute the climate vulnerability score (Figures 4C, 5C and Supplementary Figure S3C). The most vulnerable (≥0.57, 11% of the study area, Figure 4C) Páramo areas coincided with diverse Páramos in the Eastern Cordillera (Figure 3A) because of more distribution losses (r = 0.39 ± 0.01, Supplementary Figure S4). Agriculture (r = 0.48 ± 0.01, Supplementary Figure S4), mining (r = 0.36 ± 0.01, Supplementary Figure S4) and rural population density (r = 0.23 ± 0.01, Supplementary Figure S4) also boosted their vulnerability, mainly on the western slopes in the middle of the Central Cordillera (i.e., Guanacas-Puracé-Coconuco), and the northeast corner of the Eastern Cordillera (i.e., Almorzadero, Pisba, and Altiplano Cundiboyacense). On the other hand, the less vulnerable (≤0.38, 57% of the study area, Figure 4C) Páramo areas were those with the highest adaptive capacity (≥ 0.72, Figure 4B, r = −0.93 ± 0.01, Supplementary Figure S4) due to large National Natural Parks (Sumapaz, Chingaza, Sierra Nevada del Cocuy, Figure 3B), as well as those for which predicted range shifts were neutral (−0.029 to 0.030, Figure 4A, i.e., the Cruz Verde-Sumapaz Páramo complexes in the Eastern Cordillera) or positive (≥0.031, Figure 5B, Los Nevados, Chilí-Barragán, and Las Hermosas in Central Cordillera).

Discussion

The climate sensitivity score of Espeletia to climate change indicated range losses (negative values) widespread across most Páramo complexes. This suggests limited persistence of the Espeletia spp. populations via migration in the majority of Páramos, with some exceptions in the Central Cordillera possibly due to its higher connectivity. An alternative for populations to persist despite changing environments would be adaptation s.l. However, positive effects on the adaptive potential, like those due to protected areas and forests, are likely to be offset by limiting factors of the plastic response, such as rising agriculture and mining in the Páramo areas and their surroundings. Overall, this suggests that diverse Páramo areas of the Eastern Cordillera are the most vulnerable to changing climate. Climate change pushing Espeletia toward mountain summits where there is nowhere else to go (Cuesta et al., 2019a), together with a general inability of those populations to adapt, might constrain the rapid diversification that has made the Espeletia complex so unique.

Range Losses in the Espeletia Complex Will Widespread Throughout Most Páramos

Widespread range losses in the distribution of Espeletia are expected by 2050, a pessimistic prediction in line with previous modeling efforts (Buytaert et al., 2011; Crandall et al., 2013; Mavárez et al., 2018; Helmer et al., 2019; Peyre et al., 2020; Young and Duchicela, 2020). This tendency is broadly defined as thermophilization, a progressive decline of cold mountain habitats and their biota (Gottfried et al., 2012). Large reductions in modeled area and important upward shifts in the distribution of 28 Espeletiinae are already predicted by 2070 at the Venezuelan Páramo (Mavárez et al., 2018). Despite our projection is carried out for a shorter time frame at another, wider, study area (Colombian Andes spanning the Eastern, Central and Western Cordilleras besides Sierra Nevada de Santa Marta), overall results point toward the same trend and are complementary. Similar predictions of climate sensitivity at the Páramo complexes of the Ecuadorian Cordilleras (1 sp.) were not considered as part of this study because of the difficulties in compiling and comparing data from different national repositories, specifically for some of the variables that compose the adaptive capacity score (i.e., agriculture and mining pressures). Yet, considering analogous approaches in the Ecuadorian Páramo (Crandall et al., 2013; Helmer et al., 2019; Peyre et al., 2020) is essential to gather a more comprehensive understating on the consequences of climate change at high Andean ecosystems north of the Huancabamba depression, a major biogeographical barrier for high-elevation plant taxa (Weigend, 2002).

There are two important exceptions for the widespread range losses in the distribution of Espeletia in the Colombian Andes. First, small and fragmented Páramo sky islands in the Western Cordillera exhibit neutral climate sensitivity, a counterintuitive projection that Mavárez et al. (2018) also pointed out for Espeletiinae in isolated Páramo areas at Cordillera de Mérida. This reiterative finding suggests that scarce topographical complexity (terrain ruggedness) in recently colonized small islands (Flantua et al., 2019) may have favored habitat generalists, while intricate local-scale environmental heterogeneity at larger Páramo complexes could have triggered an explosive radiation (Cortés et al., 2018a; Naciri and Linder, 2020) of habitat specialists, each with a narrow ecological niche for migration to occur (Cuesta et al., 2019b). Modern colonization and limited topographical complexity, together with higher connectivity, may also account for the second exception, which is that range gains are only predicted for Páramo areas in the Central Cordillera (Los Nevados, Chilí-Barragán, Las Hermosas, and La Cocha-Patascoy), where the dominant taxon is E. hartwegiana. In line with this, Páramo areas more likely to exhibit range losses are those in the topographically complex Eastern Cordillera’s northeast corner, where diverse (Luteyn, 1999; Cuatrecasas, 2013; Peyre et al., 2015,2019) summits surround the deep (1,100 m) Chichamocha inter-Andean canyon.

Range shifts presuppose niche conservatism, but also depend on the intrinsic dispersal ability of the taxa (Pelayo et al., 2019; Tovar et al., 2020). In organisms with limited seed dispersal such as Espeletia, other ways for migration are key (Cochrane et al., 2019). For instance, migration via sporadic pollen flow would not be as constrained as seed dispersal (Cuatrecasas, 2013). Alternately, hybrids with intermediate niche requirements (Rieseberg, 2003) may serve as bridges for migration in a plant complex with rampant natural introgression (Rauscher, 2002; Cortés et al., 2018a; Pouchon et al., 2018). Finally, sporadic episodes of habitat connectivity (Flantua et al., 2019) in the lifespan of a long-lived organism such as Espeletia (Cuatrecasas, 2013) could provide a window for migration across modern barriers.

Key traits may relax the niche conservatism assumption by conferring the ability to colonize new habitats. For instance, underground plant-soil interactions (Goh et al., 2013; Sedlacek et al., 2014; Little et al., 2016) across different water levels (Geange et al., 2017) and cellular-anatomical physiological adaptations (Sandoval et al., 2019) may serve as pre-adaptations for a wider range expansion (Cuesta et al., 2017). Plastic traits may also broad the spectrum of novel climates (Arnold et al., 2019). Coupling this modeling with eco-physiological studies in Espeletia (Leon-Garcia and Lasso, 2019; Llambí and Rada, 2019) is hence a key research line to better track future range shifts in the Páramo.

Climate Change May Constrain the Rapid Diversification of the Espeletia Complex

The fact that diverse areas in the Eastern Cordillera are vulnerable to climate change is not only counterintuitive, but it also means that the long-term rapid diversification rate, striking in the Espeletia complex, could be jeopardized. Diversity is regarded as a buffer of the effects of climate change by enriching interactions (Cáceres et al., 2014), such as facilitation (Bueno and Llambí, 2015; Wheeler et al., 2015; Llambí et al., 2018; Mora et al., 2018; Venn et al., 2019), adaptive inter-specific introgression (Schilthuizen et al., 2004; Seehausen, 2004) and hybrid speciation (Payseur and Rieseberg, 2016), of which there is abundant evidence in Espeletia (Rauscher, 2002; Cuatrecasas, 2013; Cortés et al., 2018a; Pouchon et al., 2018). Yet, these positive effects are offset by distribution losses in sensitive Páramo localities, besides rising mining, agriculture and population density. These are Páramo’s current major anthropogenic threats (Vásquez et al., 2015; Pérez-Escobar et al., 2018) primarily in the western slopes of the Eastern and Central Cordilleras.

Permeable species barriers due to pervasive hybridization make the species concept highly debatable within the Espeletia complex. Therefore we refrained from emphasizing single-species responses that are likely to be overwhelmed by meta-population dynamics. Pouchon et al. (2018) suggested that the entire subtribe Espeletiinae requires an in deep taxonomic revision (Mavárez, 2019b) because most of Cuatrecasas’ (2013) species are thought to be paraphyletic. This trend is not unexpected since the available taxonomy greatly relies on potentially plastic morphological and reproductive traits (Mavárez, 2020) that are susceptible to be driven by environmental effects, besides the fact that it disregards rampant hybridization (Rauscher, 2002). Then, a more appropriate scenario for the young rapidly-evolving Espeletia complex with weak species boundaries would be that ecotypes in the same locality, despite depicting microhabitat divergence, are capable of sharing through gene flow and introgression adaptive genetic variants (Cortés et al., 2018a). Gene swapping because of porosities in the species bounds would imply that (1) the effective size of “rare” taxa with few records is higher in terms of standing adaptive variation, and (2) concerted climate responses is feasible across lineages within the same Páramo sky island. Inter-mountain exchange is also plausible, as reported in other tropical sky islands (Chala et al., 2020; Tusiime et al., 2020).

A possible way forward for populations to persist is the very same driver that is thought to contribute with the unbeatable diversification rate in the Páramo flora (Madriñán et al., 2013) – local-scale adaptive variation (Huang et al., 2020; Todesco et al., 2020). As part of this study we considered major enhancers and constrainers of the adaptive potential at a regional scale. However, populations may still harbor intrinsic genetic variation naturally selected to cope with environmental differences at the microhabitat level (Ramírez et al., 2014). In highly heterogeneous mountainous environments, local-scale environmental effects are known for shaping genetic diversity and generating morphological diversity (Hughes and Atchison, 2015), which may prove useful in the reaction to climate change within the same locality. There is already compiling evidence of ecological-driven divergence in the radiation of Andean Espeletia (Cortés et al., 2018a), besides allopatric differentiation and isolation by distance (Diazgranados and Barber, 2017; Padilla-González et al., 2017, 2018). Ecological parapatric speciation is a likely consequence of local patterns of environmental variation (Naciri and Linder, 2020) typically found at the Páramo ecosystem (Monasterio and Sarmiento, 1991). For instance, frost is more extreme in the wind-exposed high valley slopes than in the wind-sheltered depressions of those valleys or in the upper boundary of the cloud forest. Soil moisture content, higher in the well-irrigated high valley depressions and in the upper boundary of the cloud forest than in the drier slopes of the valleys, could also contribute to this divergence. It now remains to be explored whether this mosaic of environmental heterogeneity has enforced enough heritable polygenic (Barghi et al., 2020) phenotypic variation as a pre-adaptation (Nürk et al., 2018) to the new selective forces imposed by climate change.

One potential caveat of our results precisely concerns data availability at the micro-scale level. Bioclimatic data is reliably modeled throughout the Páramo range at a 1 km² spatial resolution, which unfortunately overlooks micro-environmental drivers of the local-scale genetic adaptation (De La Harpe et al., 2017; Leroy et al., 2020). This is of particular importance at mountain/alpine ecosystems (Ramírez et al., 2014; Cortés and Wheeler, 2018), where microhabitats may serve as refugia (Zellweger et al., 2020), like across the treeline due to active landform processes (Bueno and Llambí, 2015; Arzac et al., 2019; Gentili et al., 2020). Populations may rely on specific small-scale habitat attributes that are likely to be overlooked by SDMs (Sinclair et al., 2010), such as topographic, geomorphic, and edaphic features, as well as the distribution of other taxa – e.g., competitors and facilitators (Yackulc et al., 2015). Due to this, SDMs may exhibit substantial uncertainty (Buisson et al., 2010) and lack of validation (Botkin et al., 2007) at local scales. Therefore, niche models could be insensitive to micro-environmental processes, leading to an underestimation of the mechanisms governing populations’ responses. Even if bioclimatic models aim representing the realized niche, they may then disregard the fundamental niche (Loehle and Leblanc, 1996). Despite statistical imputation-like approaches such as environmental downscaling have been considered to model climate at a higher resolution for specific Páramo complexes (Mavárez et al., 2018), consistently applying such techniques across cordilleras seems unfeasible because accuracy would be constrained by the accessibility of fine-scale bioclimatic data from regional weather stations. These restraints are likely to be overcome soon by incorporating microclimate into SDMs (Lembrechts et al., 2019) through mechanistic algorithms (Lembrechts and Lenoir, 2020) physiological models (Cortés et al., 2013; López-Hernández and Cortés, 2019), hydrological equations (Rodríguez-Morales et al., 2019; Correa et al., 2020), in situ data logging, and remote sensing (Ramón-Reinozo et al., 2019; Zellweger et al., 2019).

Another potential caveat of our inferences relate with data availability across the entire Páramo area in the northern Andes. Some components of the adaptive capacity score (i.e., protected areas, agriculture, and mining pressures) are downloadable only through national repositories, making standardizations and comparisons across all Páramo complexes in different countries impractical. Yet, it is worth to clarify that climate sensitivity, adaptation capacity and vulnerability indices are not meant to be absolute scores but rather relative values to compare among Páramo complexes. In this sense, the power of our inferences to rank Páramo areas is unlikely limited by the pessimistic predictions of the future climate scenario RCP 8.5, making our vulnerability index robust. Furthermore, our whole integrative analytical pipeline is in line with the most up-to-date approaches to carry out climate change vulnerability assessment (Ramirez-Villegas et al., 2014; Valladares et al., 2014; Foden et al., 2018).

By assessing climate sensitivity and adaptive capacity across Páramo sky islands, we predicted climate change vulnerability in the rapidly evolving Espeletia complex, a syngameon (Cannon and Petit, 2020) likely shaped by incomplete ecological speciation (Nosil et al., 2009) and adaptive introgression (Fitzpatrick et al., 2015; Martin and Jiggins, 2017). However, in order to gather a more comprehensive view of climate change responses in the high mountain ecosystems of the northern Andes, other diverse and highly abundant plant genera in the Páramo (Hughes et al., 2013) should be considered under a similar analytical framework as the described here. Immediate candidates to follow up this approach are Bartsia (Uribe-Convers and Tank, 2015), Chusquea (Ely et al., 2019), Diplostephium (Vargas et al., 2017), Hypericum (Nürk et al., 2013), Loricaria (Kolar et al., 2016), Lupinus (Hughes and Eastwood, 2006; Vásquez et al., 2016; Contreras-Ortiz et al., 2018), Oreobolus (Chacón et al., 2006; Gómez-Gutiérrez et al., 2017), Puya (Jabaily and Sytsma, 2013), and Senecio (Duskova et al., 2017; Walter et al., 2020). These combined efforts would ultimately reveal whether the fastest evolving biodiversity hotspot on earth, and in general tropical high mountain ecosystems (Hedberg, 1964; Sklenáø et al., 2014; Chala et al., 2016), have a chance to persist under current environmental and anthropogenic threats.

Perspectives

Besides range shifts (Chen et al., 2011; Feeley et al., 2011; Donoghue and Edwards, 2014; Kolar et al., 2016; Freeman et al., 2020) and adaptive responses (Hoffmann and Sgro, 2011; Franks and Hoffmann, 2012; Donoghue, 2019; Ørsted et al., 2019), introgression, hybridization (Lafon-Placette et al., 2016), and polyploidization (Han et al., 2019; Mason and Wendel, 2020; Nieto Feliner et al., 2020) which have not been explicitly explored in this study, may also provide innovative (Donoghue and Sanderson, 2015) adaptive sources in plants (Abbott et al., 2013; Marques et al., 2019). The Espeletia complex is known for a high incidence of natural hybrids as told by phenotypic and genetic markers (Rauscher, 2002; Pouchon et al., 2018). Hence, assessing the role of reticulate evolution and adaptive introgression for the Espeletia complex to face a changing environment will require sampling of hybrids and their putative parental populations across the Páramo’s current micro-ecological mosaic.

Even though hybrids could occupy intermediate niches, persistence of populations in alpine environments that face climate change is regarded as mostly mediated by local-scale (i.e., microhabitat) variation (Cortés et al., 2014; Cortés and Wheeler, 2018). For example, environmental heterogeneity may provide new suitable locations for migrants within only a few meters of their current locations (Scherrer and Körner, 2011), driving in this way plant responses to warming (Zellweger et al., 2020). More localized environmental data must then be gathered (Zellweger et al., 2019), since current climate repositories lack the resolution needed to describe the microhabitat heterogeneity. Topographic and soil properties, often overlooked by climatic models, are presumably key in defining Páramo’s wet and drier microhabitats in the wind-sheltered high valley depressions, and in the more wind-exposed slopes. This local-scale environmental trend has diversity implications for other endemic plant species in the northern Andes (Cortés et al., 2012a,b; Blair et al., 2016). Besides high-resolution environmental data, eco-physiological (Llambí and Rada, 2019; Sandoval et al., 2019) and ecological adaptive traits (e.g., Bruelheide et al., 2018; Cortés and Blair, 2018) must be noted more systematically at local scales in natural surveys and field tests, like reciprocal transplant assays of ecotypes among microhabitats (as in Sedlacek et al., 2015), and space-by-time substitution trials across altitudes (Cuesta et al., 2017) and habitats (Wheeler et al., 2014, 2016). Ultimately, micro-scales keep genetic variance (Cortés et al., 2011, 2018b; Galeano et al., 2012; Kelleher et al., 2012; Blair et al., 2013, 2018; Cortés, 2013; Wu et al., 2020), morphological diversity, and adaptive trait variation (Sedlacek et al., 2016; Pacifici et al., 2017).

Conservation efforts may also buffer the impact of climate change on Páramo ecosystems by enhancing their adaptive capacity. Vast areas of Páramo land now safe from conflict are not being rapidly protected, making them susceptible to deforestation for cattle and agriculture (Avellaneda-Torres et al., 2020), and illegal logging (Vásquez et al., 2015) and mining (Pérez-Escobar et al., 2018). Thus, improving the current network of protected areas would help minimizing ongoing threats (Duchicela et al., 2019), especially in the highly diverse Páramo complexes of the Eastern Cordillera where putatively new species are still being discovered (Diazgranados and Sanchez, 2017; Mavárez, 2019a). Alongside adoption of mitigation policies, adaptation policies (Elsen et al., 2020) may help safeguarding Páramos. Finding more sustainable land uses by empowering local communities and developing ecotourism are then as essential to relieve human impact on tropical-alpine plant diversity in the northern Andes.

Last but not least, extending the modeling approach implemented in this work to other key pressures (e.g., fire, Wu and Porinchu, 2019; Rivadeneira et al., 2020; Zomer and Ramsay, 2020) and plant groups (Luteyn, 1999) in the Páramo, as well as to other sky islands around the mountains of the world (Hoorn et al., 2018; Pausas et al., 2018; Nürk et al., 2019; Testolin et al., 2020), and more broadly to other island-like systems (Papadopoulou and Knowles, 2015; Lamichhaney et al., 2017; Cámara-Leret et al., 2020; Flantua et al., 2020), will help understanding climate change effects on unrelated taxa experiencing similar evolutionary processes (Condamine et al., 2018; Cortés et al., 2020; Nürk et al., 2020). Such systems offer natural experiments to assess the role of colonization and adaptation (Ding et al., 2020; McGee et al., 2020; Tito et al., 2020) in the past and ongoing responses to climate change, which undeniably will complement ecological predictive modeling.

References

• 1

  AbbottR.AlbachD.AnsellS.ArntzenJ. W.BairdS. J. E.BierneN.et al (2013). Hybridization and speciation.J. Evol. Biol.26229–246.

  • Google Scholar

• 2

  AntonelliA.NylanderJ. A. A.PerssonC.SanmartinI. (2009). Tracing the impact of the andean uplift on neotropical plant evolution.Proc. Natl. Acad. Sci. U.S.A.1069749–9754. 10.1073/pnas.0811421106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  ArnoldP. A.KruukL. E. B.NicotraA. B. (2019). How to analyse plant phenotypic plasticity in response to a changing climate.New Phytol.2221235–1241. 10.1111/nph.15656

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  ArzacA.LlambíL. D.DulhosteR.OlanoJ. M.Chacón-MorenoE. (2019). Modelling the effect of temperature changes on plant life-form distribution across a treeline ecotone in the tropical Andes.Plant Ecol. Divers. 12, 619–631.

  • Google Scholar

• 5

  Avellaneda-TorresL. M.SicardT. L.CastroE. G.RojasE. T. (2020). Potato cultivation and livestock effects on microorganism functional groups in soils from the neotropical high andean páramo.Rev. Brasi. Ciênc. Solo44:e0190122.

  • Google Scholar

• 6

  BarghiN.HermissonJ.SchlöTtererC. (2020). Polygenic adaptation: a unifying framework to understand positive selection.Nat. Rev. Genet.10.1038/s41576-020-0250-z. [Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  BerryP. M.DawsonT. P.HarrisonP. A.PearsonR.ButtN. (2003). The sensitivity and vulnerability of terrestrial habitats and species in britain and ireland to climate change.J. Nat. Conserv.1115–23. 10.1078/1617-1381-00030

  • CrossRef
  • Google Scholar

• 8

  BlairM. W.CortesA. J.FarmerA. D.HuangW.AmbachewD.PenmetsaR. V.et al (2018). Uneven recombination rate and linkage disequilibrium across a reference snp map for common bean (Phaseolus vulgaris L.).PLoS One13:e0189597. 10.1371/journal.pone.0189597

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  BlairM. W.CortésA. J.PenmetsaR. V.FarmerA.Carrasquilla-GarciaN.CookD. R. (2013). A high-throughput snp marker system for parental polymorphism screening, and diversity analysis in common bean (Phaseolus vulgaris L.).Theor. Appl. Genet.126535–548. 10.1007/s00122-012-1999-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  BlairM. W.CortésA. J.ThisD. (2016). Identification of an erecta gene and its drought adaptation associations with wild and cultivated common bean.Plant Sci.242250–259. 10.1016/j.plantsci.2015.08.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  BotkinD. B.SaxeH.AraújoM. B.BettsR.BradshawR. H. W.CedhagenT.et al (2007). Forecasting the effects of global warming on biodiversity.Bioscience57227–236.

  • Google Scholar

• 12

  BridleJ. R.VinesT. H. (2007). Limits to evolution at range margins: when and why does adaptation fail?Trends Ecol. Evol.22140–147. 10.1016/j.tree.2006.11.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  BruelheideH.DenglerJ.PurschkeO.LenoirJ.Jiménez-AlfaroB.HennekensS. M.et al (2018). Global trait-environment relationships of plant communities.Nat. Ecol. Evol.21906–1917.

  • Google Scholar

• 14

  BuenoA.LlambíL. D. (2015). Facilitation and edge effects influence vegetation regeneration in old-fields at the tropical Andean forest line.Appl. Veg. Sci. 18, 613–623.

  • Google Scholar

• 15

  BuffumB.McgreevyT. J.Jr.GottfriedA. E.SullivanM. E.HusbandT. P. (2015). An analysis of overstory tree canopy cover in sites occupied by native and introduced cottontails in the northeastern United States with recommendations for habitat management for new england cottontail.PLoS One10:e0135067. 10.1371/journal.pone.0135067

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  BuissonL.ThuillerW.CasajusN.LekS.GrenouilletG. (2010). Uncertainty in ensemble forecasting of species distribution.Glob. Chang. Biol.161145–1157. 10.1111/j.1365-2486.2009.02000.x

  • CrossRef
  • Google Scholar

• 17

  BuytaertW.Cuesta-CamachoF.TobónC. (2011). Potential impacts of climate change on the environmental services of humid tropical alpine regions.Glob. Ecol. Biogeogr.2019–33. 10.1111/j.1466-8238.2010.00585.x

  • CrossRef
  • Google Scholar

• 18

  CáceresY.LlambíL. D.RadaF. (2014). Shrubs as foundation species in a high tropical alpine ecosystem: a multi-scale analysis of plant spatial interactions.Plant Ecol. Divers.8147–161. 10.1080/17550874.2014.960173

  • CrossRef
  • Google Scholar

• 19

  Cámara-LeretR.FrodinD. G.AdemaF.AndersonC.AppelhansM. S.ArgentG.et al (2020). New guinea has the world’s richest Island Flora.Nature584579–583.

  • Google Scholar

• 20

  CannonC. H.PetitR. J. (2020). The oak syngameon: more than the sum of its parts.N. Phytol.226978–983. 10.1111/nph.16091

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  ChacónJ.MadriñánS.ChaseM. W.BruhlJ. J. (2006). Molecular phylogenetics of oreobolus (Cyperaceae) and the origin and diversification of the American species.Taxon55359–366. 10.2307/25065583

  • CrossRef
  • Google Scholar

• 22

  ChalaD.BrochmannC.PsomasA.EhrichD.GizawA.MasaoC. A.et al (2016). Good-bye to tropical alpine plant giants under warmer climates? loss of range and genetic diversity in lobelia rhynchopetalum.Ecol. Evol.68931–8941. 10.1002/ece3.2603

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  ChalaD.ZimmermannN. E.BrochmannC.BakkestuenV. (2020). Migration corridors for alpine plants among the ‘sky islands’ of eastern africa: do they, or did they exist?Alp. Bot.127133–144. 10.1007/s00035-017-0184-z

  • CrossRef
  • Google Scholar

• 24

  ChatterjeeS.HadiA. S. (2006). Regression Analysis by Example.New York, NY: Wiley.

  • Google Scholar

• 25

  ChenI. C.HillJ. K.OhlemullerR.RoyD. B.ThomasC. D. (2011). Rapid range shifts of species associated with high levels of climate warming.Science3331024–1026. 10.1126/science.1206432

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  CIAT (2017). Formulación Del Plan Regional Integral De Cambio Climático Para La Orinoquía, Departamentos De Meta, Casanare, Vichada Y Arauca.Cali: CIAT.

  • Google Scholar

• 27

  CobosM. E.PetersonA. T.Osorio-OlveraL.Jiménez-GarcíaD. (2019). An exhaustive analysis of heuristic methods for variable selection in ecological niche modeling and species distribution modeling.Ecol. Inform.53:100983. 10.1016/j.ecoinf.2019.100983

  • CrossRef
  • Google Scholar

• 28

  CochraneA.NicotraA.OoiM. (2019). Climate change: alters plant recruitment from seed.Aust. Ecol.44931–934. 10.1111/aec.12728

  • CrossRef
  • Google Scholar

• 29

  CondamineF. L.AntonelliA.LagomarsinoL. P.HoornC.LiowL. H. (2018). “Teasing apart mountain uplift, climate change and biotic drivers of species diversification,” in Mountains, Climate, and Biodiversity, edsHoornC.PerrigoA.AntonelliA. (New York, NY: Wiley).

  • Google Scholar

• 30

  Contreras-OrtizN.AtchisonG. W.HughesC. E.MadriñánS. (2018). Convergent evolution of high elevation plant growth forms and geographically structured variation in Andean Lupinus (Fabaceae).Bot. J. Linn. Soc.187118–136. 10.1093/botlinnean/box095

  • CrossRef
  • Google Scholar

• 31

  CorreaA.Ochoa-TocachiB. F.BirkelC.Ochoa-SánchezA.ZogheibC.TovarC.et al (2020). A concerted research effort to advance the hydrological understanding of tropical páramos.Hydrol. Process. 10.1002/hyp.13904

  • CrossRef
  • Google Scholar

• 32

  CortésA. J. (2013). On the origin of the common bean (Phaseolus vulgaris L.).Am. J. Plant Sci.41998–2000.

  • Google Scholar

• 33

  CortésA. J.BlairM. W. (2018). Genotyping by sequencing and genome - environment associations in wild common bean predict widespread divergent adaptation to drought.Front. Plant Sci.9:128. 10.3389/fpls.2018.00128

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  CortésA. J.ChavarroM. C.BlairM. W. (2011). Snp marker diversity in common bean (Phaseolus vulgaris L.).Theor. Appl. Genet.123827–845. 10.1007/s00122-011-1630-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  CortésA. J.ChavarroM. C.MadriñánS.ThisD.BlairM. W. (2012a). Molecular ecology and selection in the drought-related asr gene polymorphisms in wild and cultivated common bean (Phaseolus vulgaris L.).BMC Genet.13:58. 10.1186/1471-2156-13-58

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  CortésA. J.ThisD.ChavarroC.MadriñánS.BlairM. W. (2012b). Nucleotide diversity patterns at the drought-related Dreb2 encoding genes in wild and cultivated common bean (Phaseolus vulgaris L.).Theoret. Appl. Genet.1251069–1085. 10.1007/s00122-012-1896-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  CortésA. J.GarzónL. N.ValenciaJ. B.MadriñánS. (2018a). On the causes of rapid diversification in the páramos: isolation by ecology and genomic divergence in espeletia.Front. Plant Sci.9:1700. 10.3389/fpls.2018.01700

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  CortésA. J.SkeenP.BlairM. W.Chacón-SánchezM. I. (2018b). Does the genomic landscape of species divergence in phaseolus beans coerce parallel signatures of adaptation and domestication?Front. Plant Sci.9:1816. 10.3389/fpls.2018.01816

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  CortésA. J.López-HernándezF.Osorio-RodriguezD. (2020). Predicting thermal adaptation by looking into populations’ genomic past.Front. Genet.10.3389/fgene.2020.564515. [Epub ahead of print].

  • CrossRef
  • Google Scholar

• 40

  CortésA. J.MonserrateF.Ramírez-VillegasJ.MadriñánS.BlairM. W. (2013). Drought tolerance in wild plant populations: the case of common beans (Phaseolus vulgaris L.).PLoS One8:e62898. 10.1371/journal.pone.062898

  • CrossRef
  • Google Scholar

• 41

  CortésA. J.WaeberS.LexerC.SedlacekJ.WheelerJ. A.Van KleunenM.et al (2014). Small-Scale patterns in snowmelt timing affect gene flow and the distribution of genetic diversity in the alpine dwarf shrub salix herbacea.Heredity113233–239. 10.1038/hdy.2014.19

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  CortésA. J.WheelerJ. A. (2018). “The environmental heterogeneity of mountains at a fine scale in a changing world,” in Mountains, Climate, and Biodiversity, edsHoornC.PerrigoA.AntonelliA. (New York, NY: Wiley).

  • Google Scholar

• 43

  CoyneJ. A.OrrH. A. (2004). Speciation.Sunderland, MA: Sinauer.

  • Google Scholar

• 44

  CrandallK. A.TovarC.ArnillasC. A.CuestaF.BuytaertW. (2013). Diverging responses of tropical andean biomes under future climate conditions.PLoS ONE8:e63634. 10.1371/journal.pone.0063634

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  CuatrecasasJ. (2013). A Systematic Study of the Subtribe Espeletiinae: Heliantheae, Asteraceae.New York, NY: The New York Botanical Garden.

  • Google Scholar

• 46

  CuestaF.LlambíL. D.HuggelC.DrenkhanF.GoslingW. D.MurielP.et al (2019a). New land in the neotropics: a review of biotic community, ecosystem, and landscape transformations in the face of climate and glacier change.Reg. Environ. Chang.191623–1642. 10.1007/s10113-019-01499-3

  • CrossRef
  • Google Scholar

• 47

  CuestaF.TovarC.LlambíL. D.GoslingW. D.HalloyS.CarillaJ.et al (2019b). Thermal niche traits of high alpine plant species and communities across the tropical andes and their vulnerability to global warming.J. Biogeogr.47408–420. 10.1111/jbi.13759

  • CrossRef
  • Google Scholar

• 48

  CuestaF.MurielP.LlambíL. D.HalloyS.AguirreN.BeckS.et al (2017). Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical andes.Ecography401381–1394. 10.1111/ecog.02567

  • CrossRef
  • Google Scholar

• 49

  De La HarpeM.ParisM.KargerD. N.RollandJ.KesslerM.SalaminN.et al (2017). Molecular ecology studies of species radiations: current research gaps, opportunities and challenges.Mol. Ecol.262608–2611. 10.1111/mec.14110

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  DiazgranadosM.BarberJ. C. (2017). Geography shapes the phylogeny of frailejones (Espeletiinae Cuatrec., Asteraceae): a remarkable example of recent rapid radiation in sky Islands.PeerJ5:e2968. 10.7717/peerj.2968

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  DiazgranadosM.SanchezL. R. (2017). Espeletia praesidentis, a new species of espeletiinae (Millerieae, Asteraceae) from Northeastern colombia.Phytokeys761–12. 10.3897/phytokeys.76.11220

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  DingW.-N.ReeR. H.SpicerR. A.XingY. W. (2020). Ancient orogenic and monsoon-driven assembly of the world’s richest temperate alpine Flora.Science369578–581. 10.1126/science.abb4484

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  DonoghueM. J. (2019). Adaptation meets dispersal: legumes in the land of succulents.N. Phytol.2221667–1616. 10.1111/nph.15834

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  DonoghueM. J.EdwardsE. J. (2014). Biome shifts and niche evolution in plants.Annu. Rev. Ecol. Evol. Syst.45547–572. 10.1146/annurev-ecolsys-120213-091905

  • CrossRef
  • Google Scholar

• 55

  DonoghueM. J.SandersonM. J. (2015). Confluence, synnovation, and depauperons in plant diversification.New Phytol.207260–274. 10.1111/nph.13367

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  DormannC. F.ElithJ.BacherS.BuchmannC.CarlG.CarréG.et al (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance.Ecography3627–46. 10.1111/j.1600-0587.2012.07348.x

  • CrossRef
  • Google Scholar

• 57

  DuchicelaS. A.CuestaF.PintoE.GoslingW. D.YoungK. R. (2019). Indicators for assessing tropical alpine rehabilitation practices.Ecosphere10:e02595.

  • Google Scholar

• 58

  DuskovaE.SklenáøP.KolarF.VasquezD. L. A.RomolerouxK.FerT.et al (2017). Growth form evolution and hybridization in senecio (Asteraceae) from the high equatorial andes.Ecol. Evol.76455–6468. 10.1002/ece3.3206

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  ElsenP. R.MonahanW. B.DoughertyE. R.MerenlenderA. M. (2020). Keeping pace with climate change in global terrestrial protected areas.Sci. Adv.6:eaay0814. 10.1126/sciadv.aay0814

  • CrossRef
  • Google Scholar

• 60

  ElyF.RadaF.FerminG.ClarkL. G. (2019). Ecophysiology and genetic diversity in species of the bamboo Chusquea in the high Andes, Venezuela.Plant Ecol. Divers. 12, 555–572.

  • Google Scholar

• 61

  FeeleyK. J.SilmanM. R.BushM. B.FarfanW.CabreraK. G.MalhiY.et al (2011). Upslope migration of andean trees.J. Biogeogr.38783–791. 10.1111/j.1365-2699.2010.02444.x

  • CrossRef
  • Google Scholar

• 62

  FengX.ParkD. S.LiangY.PandeyR.PapesM. (2019). Collinearity in ecological niche modeling: confusions and challenges.Ecol. Evol.910365–10376. 10.1002/ece3.5555

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  FitzpatrickS. W.GerberichJ. C.KronenbergerJ. A.AngeloniL. M.FunkW. C. (2015). Locally adapted traits maintained in the face of high gene flow.Ecol. Lett.1837–47. 10.1111/ele.12388

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64

  FlantuaS. G. A.O’deaA.OnsteinR. E.GiraldoC.HooghiemstraH. (2019). The Flickering connectivity system of the North Andean Páramos.J. Biogeogr.461808–1825. 10.1111/jbi.13607

  • CrossRef
  • Google Scholar

• 65

  FlantuaS. G. A.PayneD.BorregaardM. K.BeierkuhnleinC.SteinbauerM. J.DullingerS.et al (2020). Snapshot isolation and isolation history challenge the analogy between mountains and islands used to understand endemism.Glob. Ecol. Biogeogr.10.1111/geb.13155. [Epub ahead of print].

  • CrossRef
  • Google Scholar

• 66

  FodenW. B.YoungB. E.AkçakayaH. R.GarciaR. A.HoffmannA. A.SteinB. A.et al (2018). Climate change vulnerability assessment of species.Wiley Interdiscipl. Rev. Clim. Chang.10:e551.

  • Google Scholar

• 67

  FoisM.Cuena-LombrañaA.FenuG.BacchettaG. (2018). Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions.Ecol. Model.385124–132. 10.1016/j.ecolmodel.2018.07.018

  • CrossRef
  • Google Scholar

• 68

  FoxR. J.DonelsonJ. M.SchunterC.RavasiT.Gaitan-EspitiaJ. D. (2019). Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change.Philos. Trans. R. Soc. Lond. B Biol. Sci.374:20180174.

  • Google Scholar

• 69

  FranksS. J.HoffmannA. A. (2012). Genetics of climate change adaptation.Annu. Rev. Genet.46185–208.

  • Google Scholar

• 70

  FreemanB. G.SongY.FeeleyK. J.ZhuK. (2020). Montane species and communities track recent warming more closely in the tropics.bioRxiv10.1101/2020.05.18.102848

  • CrossRef
  • Google Scholar

• 71

  GaleanoC. H.CortésA. J.FernandezA. C.SolerA.Franco-HerreraN.MakundeG.et al (2012). Gene-based single nucleotide polymorphism markers for genetic and association mapping in common bean.BMC Genet.13:48. 10.1186/1471-2156-13-48

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  GeangeS. R.BricenÞOV. F.AitkenN. C.Ramirez-ValienteJ. A.Holloway-PhillipsM. M.NicotraA. B. (2017). Phenotypic plasticity and water availability: responses of alpine herb species along an elevation gradient.Clim. Chang. Respon.4:5.

  • Google Scholar

• 73

  GentiliR.BaroniC.PanigadaC.RossiniM.TagliabueG.ArmiraglioS.et al (2020). Glacier shrinkage and slope processes create habitat at high elevation and microrefugia across treeline for alpine plants during warm stages.Catena193:104626. 10.1016/j.catena.2020.104626

  • CrossRef
  • Google Scholar

• 74

  GohC. H.Veliz-VallejosD. F.NicotraA. B.MathesiusU. (2013). The impact of beneficial plant-associated microbes on plant phenotypic plasticity.J. Chem. Ecol.39826–839. 10.1007/s10886-013-0326-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  Gómez-GutiérrezM. C.PenningtonR. T.NeavesL. E.MilneR. I.MadriñánS.RichardsonJ. E. (2017). Genetic diversity in the andes: variation within and between the south American species of oreobolus R. Br. (Cyperaceae).Alp. Bot.127155–170. 10.1007/s00035-017-0192-z

  • CrossRef
  • Google Scholar

• 76

  GottfriedM.PauliH.FutschikA.AkhalkatsiM.BaranèokP.Benito AlonsoJ. L.et al (2012). Continent-wide response of mountain vegetation to climate change.Nat. Clim. Chang.2111–115. 10.1038/nclimate1329

  • CrossRef
  • Google Scholar

• 77

  HanT. S.ZhengQ. J.OnsteinR. E.Rojas-AndrésB. M.HauenschildF.Muellner-RiehlA. N.et al (2019). Polyploidy promotes species diversification of Allium through ecological shifts.New Phytol. 225, 571–583.

  • Google Scholar

• 78

  HazziN. A.MorenoJ. S.Ortiz-MovliavC.PalacioR. D. (2018). biogeographic regions and events of isolation and diversification of the endemic biota of the tropical andes.Proc. Natl. Acad. Sci. U.S.A.1157985–7990. 10.1073/pnas.1803908115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  HedbergO. (1964). Afroalpine plant ecology.Acta Phytogeogr. Suec.498–72.

  • Google Scholar

• 80

  Henao-DíazF.ArrroyoS.Cárdenas-PosadaG.FernándezM.LópezJ. P.MartínezD. C.et al (2019). Biotic characterization of the forest-paramo transition zone in Chingaza Páramo Complex, Colombia.Biota Colombiana20132–145.

  • Google Scholar

• 81

  HelmerE. H.GersonE. A.BaggettL. S.BirdB. J.RuzyckiT. S.VoggesserS. M. (2019). Neotropical cloud forests and páramo to contract and dry from declines in cloud immersion and frost.PLoS ONE14:e0213155. 10.1371/journal.pone.0213155

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82

  HoffmannA. A.SgroC. M. (2011). Climate change and evolutionary adaptation.Nature470479–485.

  • Google Scholar

• 83

  HoornC.PerrigoA.AntonelliA. (2018). “Mountains, climate and biodiversity: an introduction,” in Mountains, Climate, and Biodiversity, edsHoornC.PerrigoA.AntonelliA. (New York, NY: Wiley).

  • Google Scholar

• 84

  HuangK.AndrewR. L.OwensG. L.OstevikK. L.RiesebergL. H. (2020). Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype.Mol. Ecol.20201–15.

  • Google Scholar

• 85

  HughesC.EastwoodR. (2006). Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the andes.Proc. Natl. Acad. Sci. U.S.A.10310334–10339. 10.1073/pnas.0601928103

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  HughesC.PenningtonR. T.AntonelliA. (2013). Neotropical plant evolution: Assembling the big picture.Bot. J. Linn. Soc.1711–18. 10.1111/boj.12006

  • CrossRef
  • Google Scholar

• 87

  HughesC. E.AtchisonG. W. (2015). The ubiquity of alpine plant radiations: from the andes to the hengduan mountains.New Phytol.207275–282. 10.1111/nph.13230

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  IsabelN.HollidayJ. A.AitkenS. N. (2020). Forest genomics: Advancing climate adaptation, forest health, productivity, and conservation.Evol. Appl.133–10. 10.1111/eva.12902

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  JabailyR. S.SytsmaK. J. (2013). Historical biogeography and life-history evolution of Andean Puya (Bromeliaceae).Bot. J. Linnea. Soc.171201–224. 10.1111/j.1095-8339.2012.01307.x

  • CrossRef
  • Google Scholar

• 90

  JumpA. S.PenuelasJ. (2005). Running to stand still: adaptation and the response of plants to rapid climate change.Ecol. Lett.81010–1020. 10.1111/j.1461-0248.2005.00796.x

  • CrossRef
  • Google Scholar

• 91

  KelleherC. T.WilkinJ.ZhuangJ.CortésA. J.QuinteroÁL. P.GallagherT. F.et al (2012). Snp discovery, gene diversity, and linkage disequilibrium in wild populations of populus tremuloides.Tree Genet. Genom.8821–829. 10.1007/s11295-012-0467-x

  • CrossRef
  • Google Scholar

• 92

  KellyM. (2019). Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes.Philos. Trans. R. Soc. Lond. B Biol. Sci.374:20180176. 10.1098/rstb.2018.0176

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  KolarF.DuskovaE.SklenáøP. (2016). Niche shifts and range expansions along cordilleras drove diversification in a high-elevation endemic plant genus in the tropical andes.Mol. Ecol.254593–4610. 10.1111/mec.13788

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  KörnerC. (2003). Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems.Berlin: Springer.

  • Google Scholar

• 95

  Lafon-PlacetteC.Vallejo-MarínM.ParisodC.AbbottR. J.KöhlerC. (2016). Current Plant speciation research: unravelling the processes and mechanisms behind the evolution of reproductive isolation barriers.New Phytol.20929–33. 10.1111/nph.13756

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  LamichhaneyS.HanF.WebsterM. T.AnderssonL.GrantB. R.GrantP. R. (2017). Rapid hybrid speciation in Darwin’s finches.Science359224–228. 10.1126/science.aao4593

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  LeesA. C.AttwoodS.BarlowJ.PhalanB. (2020). Biodiversity scientists must fight the creeping rise of extinction denial.Nat. Ecol. Evol.10.1038/s41559-020-01285-z. [Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  LembrechtsJ. J.LenoirJ. (2020). Microclimatic conditions anywhere at any time!Glob. Chang. Biol.26337–339. 10.1111/gcb.14942

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  LembrechtsJ. J.NijsI.LenoirJ. (2019). Incorporating microclimate into species distribution models.Ecography421267–1279. 10.1111/ecog.03947

  • CrossRef
  • Google Scholar

• 100

  LenoirJ.GégoutJ.-C.GuisanA.VittozP.WohlgemuthT.ZimmermannN. E.et al (2010). Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate.Ecography33295–303.

  • Google Scholar

• 101

  LenoirJ.GegoutJ. C.MarquetP. A.De RuffrayP.BrisseH. (2008). A significant upward shift in plant species optimum elevation during the 20th century.Science3201768–1771. 10.1126/science.1156831

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  Leon-GarciaI. V.LassoE. (2019). High heat tolerance in plants from the Andean highlands: Implications for paramos in a warmer world.PLoS One14:e0224218. 10.1371/journal.pone.0224218

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  LeroyT.LouvetJ. M.LalanneC.Le ProvostG.LabadieK.AuryJ. M.et al (2020). Adaptive introgression as a driver of local adaptation to climate in european white oaks.N. Phytol.2261171–1182. 10.1111/nph.16095

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  LittleC. J.WheelerJ. A.SedlacekJ.CortésA. J.RixenC. (2016). Small-scale drivers: the importance of nutrient availability and snowmelt timing on performance of the alpine shrub salix herbacea.Oecologia1801015–1024. 10.1007/s00442-015-3394-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  LiuC.BerryP. M.DawsonT. P.PearsoR. G. (2005). Selecting thresholds of occurrence in the prediction of species distributions.Ecography28385–393. 10.1111/j.0906-7590.2005.03957.x

  • CrossRef
  • Google Scholar

• 106

  LlambíL. D.BecerraM. T.PeralvoM.AvellaA.BaruffolM.DíazL. J. (2020). Monitoring biodiversity and ecosystem services in Colombia’s high andean ecosystems: toward an integrated strategy.Mt. Res. Dev. 39:3.

  • Google Scholar

• 107

  LlambíL. D.HuppN.SaezA.CallawayR. (2018). Reciprocal interactions between a facilitator, natives, and exotics in tropical alpine plant communities.Perspect. Plant Ecol. Evol. Syst.3082–88. 10.1016/j.ppees.2017.05.002

  • CrossRef
  • Google Scholar

• 108

  LlambíL. D.RadaF. (2019). Ecological research in the tropical alpine ecosystems of the venezuelan páramo: past, present and future.Plant Ecol. Divers.12519–538. 10.1080/17550874.2019.1680762

  • CrossRef
  • Google Scholar

• 109

  LoboJ. M.Jiménez-ValverdeA.RealR. (2008). Auc: a misleading measure of the performance of predictive distribution models.Glob. Ecol. Biogeogr.17145–151. 10.1111/j.1466-8238.2007.00358.x

  • CrossRef
  • Google Scholar

• 110

  LoehleC.LeblancD. (1996). Model-based assessments of climate change effects on forests: a critical review.Ecol. Model.901–31. 10.1016/0304-3800(96)83709-4

  • CrossRef
  • Google Scholar

• 111

  LondoñoC.CleefA.MadriñánS. (2014). Angiosperm flora and biogeography of the páramo region of colombia, Northern Andes.Flora Morphol. Distribut. Funct. Ecol. Plants20981–87. 10.1016/j.flora.2013.11.006

  • CrossRef
  • Google Scholar

• 112

  López-HernándezF.CortésA. J. (2019). Last-generation genome-environment associations reveal the genetic basis of heat tolerance in common bean (Phaseolus vulgaris L.).Front. Genet.10:22. 10.3389/fpls.2018.00022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  LuteynJ. L. (1999). Páramos: A Checklist of Plant Diversity, Geographic Distribution and Botanical Literature.New York, NY: The New York Botanical Garden Press.

  • Google Scholar

• 114

  MadriñánS.CortésA. J.RichardsonJ. E. (2013). Páramo is the world’s fastest evolving and coolest biodiversity hotspot.Front. Genet.4:192. 10.3389/fpls.2018.00192

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  MarquesD. A.MeierJ. I.SeehausenO. (2019). A combinatorial view on speciation and adaptive radiation.Trends Ecol. Evol.34531–544. 10.1016/j.tree.2019.02.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116

  MartinS. H.JigginsC. D. (2017). Interpreting the genomic landscape of introgression.Curr. Opin. Genet. Dev.4769–74. 10.1016/j.gde.2017.08.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  Martín-BravoS.ValcárcelV.VargasP.LuceñoM. (2010). Geographical speciation related to pleistocene range shifts in the Western Mediterranean Mountains (Reseda Sect. Glaucoreseda, Resedaceae).Taxon59466–482. 10.1002/tax.592012

  • CrossRef
  • Google Scholar

• 118

  MasonA. S.WendelJ. F. (2020). Homoeologous exchanges, segmental allopolyploidy, and polyploid genome evolution.Front. Genet.11:1014. 10.3389/fgene.2020.01014

  • CrossRef
  • Google Scholar

• 119

  MavárezJ. (2019a). A taxonomic revision of Espeletia (Asteraceae). The venezuelan radiation.Harvard Pap. Bot.24:131. 10.3100/hpib.v24iss2.2019.n8

  • CrossRef
  • Google Scholar

• 120

  MavárezJ. (2019b). Taxonomic novelties in páramo plants. Espeletia ramosa (Asteraceae), a new species from Colombia.Phytologia101222–230.

  • Google Scholar

• 121

  MavárezJ.BézyS.GoeuryT.FernándezA.AubertS. (2018). Current and future distributions of espeletiinae (Asteraceae) in the venezuelan andes based on statistical downscaling of climatic variables and niche modelling.Plant Ecol. Divers.12633–647. 10.1080/17550874.2018.1549599

  • CrossRef
  • Google Scholar

• 122

  MavárezJ. S. (2020). An illustrated diagnostic key to species in the venezuelan clade of Espeletia (Asteraceae).Harv. Pap. Bot.2579–93.

  • Google Scholar

• 123

  McGeeM. D.BorsteinS. R.MeierJ. I.MarquesD. A.MwaikoS.TaabuA.et al (2020). The ecological and genomic basis of explosive adaptive radiation.Nature10.1038/s41586-020-2652-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  MonasterioM.SarmientoL. (1991). Adaptive radiation of espeletia in the cold andean tropics.Trends Ecol. Evol.6387–391. 10.1016/0169-5347(91)90159-u

  • CrossRef
  • Google Scholar

• 125

  MoralesM.Otero-GarcíaJ.Van-Der-HammenT.Torres-PerdigónA.Cadena-VargasC. E.Pedraza-PeñalozaC. A.et al (2007). Atlas De Páramos De Colombia.Bogotaì: Instituto de Investigación de Recursos Biológicos Alexander von Humboldt.

  • Google Scholar

• 126

  MoraM. A.LlambíL. D.RamírezL. (2018). Giant stem rosettes have strong facilitation effects on alpine plant communities in the tropical Andes.Plant Ecol. Divers. 12, 593–606.

  • Google Scholar

• 127

  Muellner-RiehlA. N. (2019). Mountains as evolutionary arenas: patterns, emerging approaches, paradigm shifts, and their implications for plant phylogeographic research in the tibeto-himalayan region.Front. Plant Sci.10:195. 10.3389/fpls.2018.00195

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  MutkeJ.JacobsR.MeyersK.HenningT.WeigendM. (2014). Diversity patterns of selected Andean plant groups correspond to topography and habitat dynamics, not orogeny.Front. Genet.5:351. 10.3389/fgene.2014.00351

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  NaciriY.LinderH. P. (2020). The genetics of evolutionary radiations.Biol. Rev. 95, 1055–1072.

  • Google Scholar

• 130

  NaimiB.HammN. A. S.GroenT. A.SkidmoreA. K.ToxopeusA. G. (2014). where is positional uncertainty a problem for species distribution modelling?Ecography37191–203. 10.1111/j.1600-0587.2013.00205.x

  • CrossRef
  • Google Scholar

• 131

  Navarro-RacinesC.TarapuesJ.ThorntonP.JarvisA.Ramirez-VillegasJ. (2020). High-resolution and bias-corrected Cmip5 projections for climate change impact assessments.Sci. Data7:7.

  • Google Scholar

• 132

  NevadoB.Contreras-OrtizN.HughesC.FilatovD. A. (2018). Pleistocene glacial cycles drive isolation, gene flow and speciation in the high-elevation Andes.New Phytol.219779–793. 10.1111/nph.15243

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  NicotraA. B.AtkinO. K.BonserS. P.DavidsonA. M.FinneganE. J.MathesiusU.et al (2010). Plant phenotypic plasticity in a changing climate.Trends Plant Sci.15684–692. 10.1016/j.tplants.2010.09.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  Nieto FelinerG.CasacubertaJ.WendelJ. F. (2020). Genomics of evolutionary novelty in hybrids and polyploids.Front. Genet.11:792. 10.3389/fgene.2020.00792

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  NosilP.HarmonL. J.SeehausenO. (2009). Ecological explanations for (Incomplete) speciation.Trends Ecol. Evol.24145–156. 10.1016/j.tree.2008.10.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  NürkN. M.AtchisonG. W.HughesC. E. (2019). Island woodiness underpins accelerated disparification in plant radiations.New Phytol. 224, 518–531. 10.1111/nph.15797

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137

  NürkN. M.MichlingF.LinderH. P. (2018). Are the radiations of temperate lineages in tropical alpine ecosystems pre-adapted?Glob. Ecol. Biogeogr.27334–345. 10.1111/geb.12699

  • CrossRef
  • Google Scholar

• 138

  NürkN. M.ScheriauC.MadrinanS. (2013). Explosive radiation in high andean hypericum-rates of diversification among new world lineages.Front. Genet.4:175. 10.3389/fpls.2018.00175

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139

  NürkN. M.LinderH. P.OnsteinR. E.LarcombeM. J.HughesC. E.Pineiro FernandezL. (2020). Diversification in evolutionary arenas-assessment and synthesis.Ecol. Evol.106163–6182. 10.1002/ece3.6313

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  ØrstedM.HoffmannA. A.RohdeP. D.SørensenP.KristensenT. N. (2019). Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait.Heredity122315–325. 10.1038/s41437-018-0117-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 141

  PacificiM.ViscontiP.ButchartS. H. M.WatsonJ. E. M.CassolaF. M.RondininiC. (2017). Species’ traits influenced their response to recent climate change.Nat. Clim. Chang.7205–208. 10.1038/nclimate3223

  • CrossRef
  • Google Scholar

• 142

  Padilla-GonzálezG. F.DiazgranadosM.CostaF. B. D. (2017). Biogeography shaped the metabolome of the genus espeletia: a phytochemical perspective on an andean adaptive radiation.Sci. Rep.7:8835.

  • Google Scholar

• 143

  Padilla-GonzálezG. F.DiazgranadosM.OliveiraT. B.Chagas-PaulaD. A.CostaF. B. D. (2018). Chemistry of the subtribe espeletiinae (Asteraceae) and its correlation with phylogenetic data: an in silico chemosystematic approach.Bot. J. Linnea. Soc.18618–46. 10.1093/botlinnean/box078

  • CrossRef
  • Google Scholar

• 144

  PapadopoulouA.KnowlesL. L. (2015). Genomic tests of the species-pump hypothesis: recent island connectivity cycles drive population divergence but not speciation in caribbean crickets across the virgin Islands.Evolution691501–1517. 10.1111/evo.12667

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145

  PausasJ. G.BondW. J.SankaranM. (2018). Humboldt and the reinvention of nature.J. Ecol.1071031–1037. 10.1111/1365-2745.13109

  • CrossRef
  • Google Scholar

• 146

  PayseurB. A.RiesebergL. H. (2016). A Genomic perspective on hybridization and speciation.Mol. Ecol.252337–2360. 10.1111/mec.13557

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 147

  PelayoR. C.SorianoP. J.MárquezN. J.NavarroL. (2019). Phenological patterns and pollination network structure in a Venezuelan páramo: a community-scale perspective on plant-animal interactions.Plant Ecol. Divers. 12, 607–618.

  • Google Scholar

• 148

  Pérez-EscobarO. A.Cámara-LeretR.AntonelliA.BatemanR.BellotS.ChomickiG.et al (2018). Mining threatens colombian ecosystems.Science359:1475.

  • Google Scholar

• 149

  PerrigoA.HoornC.AntonelliA. (2019). Why mountains matter for biodiversity.J. Biogeogr.47315–325. 10.1111/jbi.13731

  • CrossRef
  • Google Scholar

• 150

  PeyreG.BalslevH.FontX. (2018). Phytoregionalisation of the andean paramo.PeerJ6:e4786. 10.7717/peerj.4786

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 151

  PeyreG.BalslevH.FontX.TelloJ. S. (2019). Fine-scale plant richness mapping of the andean páramo according to macroclimate.Front. Ecol. Evol.7:12. 10.3389/ffgc.2020.00012

  • CrossRef
  • Google Scholar

• 152

  PeyreG.BalslevH.MartíD.SklenářP.RamsayP.LozanoP.et al (2015). VegPáramo, a flora and vegetation database for the Andean páramo.Phytocoenologia45, 195–201. 10.1127/phyto/2015/0045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 153

  PeyreG.LenoirJ.KargerD. N.GomezM.GonzalezA.BroennimannO.et al (2020). The fate of páramo plant assemblages in the sky islands of the northern Andes.J. Veg. Sci. 10.1111/jvs.12898

  • CrossRef
  • Google Scholar

• 154

  PhillipsS. J.AndersonR. P.DudíkM.SchapireR. E.BlairM. E. (2017). Opening the black box: an open-source release of maxent.Ecography40887–893. 10.1111/ecog.03049

  • CrossRef
  • Google Scholar

• 155

  PouchonC.FernándezA.NassarJ. M.BoyerF. D. R.AubertS.LavergneS. B.et al (2018). Phylogenomic analysis of the explosive adaptive radiation of the espeletia complex (Asteraceae) in the tropical andes.Syst. Biol.671041–1060. 10.1093/sysbio/syy022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 156

  RahbekC.BorregaardM. K.AntonelliA.ColwellR. K.HoltB. G.Nogues-BravoD.et al (2019). Building mountain biodiversity: geological and evolutionary processes.Science3651114–1119. 10.1126/science.aax0151

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157

  RamírezL. A.RadaF.LlambíL. D. (2014). Linking patterns and processes through ecosystem engineering: effects of shrubs on microhabitat and water status of associated plants in the high tropical Andes.Plant Ecol. 216, 213–225.

  • Google Scholar

• 158

  Ramirez-VillegasJ.CuestaF.DevenishC.PeralvoM.JarvisA.ArnillasC. A. (2014). Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change.J. Nat. Conserv. 22, 391–404.

  • Google Scholar

• 159

  Ramón-ReinozoM.BallariD.CabreraJ. J.CrespoP.Carrillo-RojasG. (2019). Altitudinal and temporal evapotranspiration dynamics via remote sensing and vegetation index-based modelling over a scarce-monitored, high-altitudinal Andean páramo ecosystem of Southern Ecuador.Environ. Earth Sci. 78:340.

  • Google Scholar

• 160

  RauscherJ. T. (2002). Molecular phylogenetics of the espeletia complex (Asteraceae): evidence from nrdna its sequences on the closest relatives of an andean adaptive radiation.Am. J. Bot.891074–1084. 10.3732/ajb.89.7.1074

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 161

  RazgourO.ForesterB.TaggartJ. B.BekaertM.JusteJ.IbanezC.et al (2019). Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections.PNAS11610418–10423. 10.1073/pnas.1820663116

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 162

  RiesebergL. H. (2003). Major ecological transitions in wild sunflowers facilitated by hybridization.Science3011211–1216. 10.1126/science.1086949

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 163

  RivadeneiraG.RamsayP. M.MontúfarR. (2020). Fire regimes and pollinator behaviour explain the genetic structure of Puya hamata (Bromeliaceae) rosette plants.Alp. Botany130, 13–23. 10.1007/s00035-020-00234-7

  • CrossRef
  • Google Scholar

• 164

  Rodríguez-MoralesM.Acevedo-NovoaD.MachadoD.AblanM.DugarteW.DávilaF. (2019). Ecohydrology of the Venezuelan páramo: water balance of a high Andean watershed.Plant Ecol. Diver. 12, 573–591.

  • Google Scholar

• 165

  RonikierM. (2011). Biogeography of high-mountain plants in the carpathians: an emerging phylogeographical perspective.Taxon6373–389. 10.1002/tax.602008

  • CrossRef
  • Google Scholar

• 166

  RumpfS. B.HulberK.KlonnerG.MoserD.SchutzM.WesselyJ.et al (2018). Range dynamics of mountain plants decrease with elevation.Proc. Natl. Acad. Sci. U.S.A.1151848–1853. 10.1073/pnas.1713936115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 167

  SandovalD.RadaF.SarmientoL. (2019). Stomatal response functions to environmental stress of dominant species in the tropical andean Páramo.Plant Ecol. Divers.12649–661. 10.1080/17550874.2019.1683094

  • CrossRef
  • Google Scholar

• 168

  ScherrerD.KörnerC. (2011). Topogaphically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming.J. Biogeogr.38406–416. 10.1111/j.1365-2699.2010.02407.x

  • CrossRef
  • Google Scholar

• 169

  SchilthuizenM.HoekstraR. F.GittenbergerE. (2004). Hybridization, rare alleles and adaptive radiation.Trends Ecol. Evol.19404–405. 10.1016/j.tree.2004.06.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170

  SedlacekJ.BossdorfO.CortésA. J.WheelerJ. A.Van-KleunenM. (2014). What role do plant-soil interactions play in the habitat suitability and potential range expansion of the alpine dwarf shrub Salix Herbacea?Basic Appl. Ecol.15305–315. 10.1016/j.baae.2014.05.006

  • CrossRef
  • Google Scholar

• 171

  SedlacekJ.CortésA. J.WheelerJ. A.BossdorfO.HochG.KlapsteJ.et al (2016). Evolutionary potential in the alpine: trait heritabilities and performance variation of the dwarf willow Salix Herbacea from different elevations and microhabitats.Ecol. Evol.63940–3952. 10.1002/ece3.2171

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172

  SedlacekJ.WheelerJ. A.CortésA. J.BossdorfO.HochG.LexerC.et al (2015). The response of the alpine dwarf shrub Salix Herbacea to altered snowmelt timing: lessons from a multi-site transplant experiment.PLoS One10:e0122395. 10.1371/journal.pone.0122395

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173

  SeehausenO. (2004). Hybridization and adaptive radiation.Trends Ecol. Evol.19198–207. 10.1016/j.tree.2004.01.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 174

  SinclairS. J.WhiteM. D.NewellG. R. (2010). How useful are species distribution models for managing biodiversity under future climates?Ecol. Soc.15:8.

  • Google Scholar

• 175

  SklenáøP.HedbergI.CleefA. M. (2014). Island biogeography of tropical alpine floras.J. Biogeogr.41287–297. 10.1111/jbi.12212

  • CrossRef
  • Google Scholar

• 176

  SpiersJ. A.OathamM. P.RostantL. V.FarrellA. D. (2018). Applying species distribution modelling to improving conservation based decisions: a gap analysis of Trinidad and Tobago’s endemic vascular plants.Biodivers. Conserv.272931–2949. 10.1007/s10531-018-1578-y

  • CrossRef
  • Google Scholar

• 177

  SteinbauerM. J.GrytnesJ. A.JurasinskiG.KulonenA.LenoirJ.PauliH.et al (2018). Accelerated increase in plant species richness on mountain summits is linked to warming.Nature556231–234.

  • Google Scholar

• 178

  SweetL. C.GreenT.HeintzJ. G. C.FrakesN.GraverN.RangitschJ. S.et al (2019). Congruence between future distribution models and empirical data for an iconic species at joshua tree national park.Ecosphere10:e0276.

  • Google Scholar

• 179

  SyfertM. M.SmithM. J.CoomesD. A. (2013). The effects of sampling bias and model complexity on the predictive performance of maxent species distribution models.PLoS One8:e55158. 10.1371/journal.pone.0055158

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 180

  TestolinR.AttorreF.Jiménez-AlfaroB. (2020). Global distribution and bioclimatic characterization of alpine biomes.Ecography43779–788.

  • Google Scholar

• 181

  TitoR.VasconcelosH. L.FeeleyK. J. (2020). Mountain ecosystems as natural laboratories for climate change experiments.Front. For. Glob. Chang.3:38. 10.3389/ffgc.2020.00038

  • CrossRef
  • Google Scholar

• 182

  TodescoM.OwensG. L.BercovichN.LégaréJ. S.SoudiS.BurgeD. O.et al (2020). Massive haplotypes underlie ecotypic differentiation in sunflowers.Nature584602–607. 10.1038/s41586-020-2467-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 183

  TovarC.MelcherI.KusumotoB.CuestaF.CleefA.MenesesR. I.et al (2020). Plant dispersal strategies of high tropical alpine communities across the Andes.J. Ecol. 108, 1910–1922.

  • Google Scholar

• 184

  TusiimeF. M.GizawA.GussarovaG.NemomissaS.PoppM.MasaoC. A.et al (2020). Afro-alpine flagships revisited: parallel adaptation, intermountain admixture and shallow genetic structuring in the giant senecios (Dendrosenecio).PLoS One15:e0228979. 10.1371/journal.pone.00228979

  • CrossRef
  • Google Scholar

• 185

  Uribe-ConversS.TankD. C. (2015). Shifts in diversification rates linked to biogeographic movement into new areas: an example of a recent radiation in the Andes.Am. J. Bot.1021854–1869. 10.3732/ajb.1500229

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 186

  ValladaresF.MatesanzS.GuilhaumonF.AraujoM. B.BalaguerL.Benito-GarzonM.et al (2014). The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change.Ecol. Lett.171351–1364. 10.1111/ele.12348

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 187

  Van ProosdijA. S. J.SosefM. S. M.WieringaJ. J.RaesN. (2016). Minimum required number of specimen records to develop accurate species distribution models.Ecography39542–552. 10.1111/ecog.01509

  • CrossRef
  • Google Scholar

• 188

  VargasO. M.OrtizE. M.SimpsonB. B. (2017). Conflicting phylogenomic signals reveal a pattern of reticulate evolution in a recent high-andean diversification (Asteraceae: Astereae: Diplostephium).New Phytol.2141736–1750. 10.1111/nph.14530

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 189

  VásquezD. L. A.BalslevH.HansenM. M.SklenáøP.RomolerouxK. (2016). Low genetic variation and high differentiation across sky island populations of Lupinus alopecuroides (Fabaceae) in the Northern Andes.Alp. Bot.126135–142. 10.1007/s00035-016-0165-7

  • CrossRef
  • Google Scholar

• 190

  VásquezD. L. A.BalslevH.SklenáøP. (2015). Human impact on tropical-alpine plant diversity in the Northern Andes.Biodivers. Conserv.242673–2683. 10.1007/s10531-015-0954-0

  • CrossRef
  • Google Scholar

• 191

  VennS.Myers-SmithI.CamacJ.NicotraA. (2019). Climate change: alpine shrubs as ecosystem engineers.Austr. Ecol.44927–930. 10.1111/aec.12727

  • CrossRef
  • Google Scholar

• 192

  WaldvogelA. M.FeldmeyerB.RolshausenG.Exposito-AlonsoM.RellstabC.KoflerR.et al (2020). Evolutionary genomics can improve prediction of species’ responses to climate change.Evol. Lett.44–18. 10.1002/evl3.154

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 193

  WalterG. M.AbbottR. J.BrennanA. C.BridleJ. R.ChapmanM.ClarkJ.et al (2020). Senecio as a model system for integrating studies of genotype, phenotype and fitness.New Phytol.226326–344. 10.1111/nph.16434

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 194

  WarrenD. L.SeifertS. N. (2011). Ecological niche modeling in maxent: the importance of model complexity and the performance of model selection criteria.Ecol. Appl.21335–342. 10.1890/10-1171.1

  • CrossRef
  • Google Scholar

• 195

  WeigendM. (2002). Observations on the biogeography of the amotape-huancabamba zone in Northern Peru.Bot. Rev.6838–54. 10.1663/0006-8101(2002)068[0038:ootbot]2.0.co;2

  • CrossRef
  • Google Scholar

• 196

  WestA. M.KumarS.BrownC. S.StohlgrenT. J.BrombergJ. (2016). Field validation of an invasive species maxent model.Ecol. Inform.36126–134. 10.1016/j.ecoinf.2016.11.001

  • CrossRef
  • Google Scholar

• 197

  WheelerJ. A.CortésA. J.SedlacekJ.KarrenbergS.Van KleunenM.WipfS.et al (2016). The snow and the willows: accelerated spring snowmelt reduces performance in the low-lying alpine shrub Salix Herbacea.J. Ecol.1041041–1050. 10.1111/1365-2745.12579

  • CrossRef
  • Google Scholar

• 198

  WheelerJ. A.HochG.CortésA. J.SedlacekJ.WipfS.RixenC. (2014). Increased spring freezing vulnerability for alpine shrubs under early snowmelt.Oecologia175219–229. 10.1007/s00442-013-2872-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 199

  WheelerJ. A.SchniderF.SedlacekJ.CortésA. J.WipfS.HochG.et al (2015). With a little help from my friends: community facilitation increases performance in the dwarf shrub Salix Herbacea.Basic Appl. Ecol.16202–209. 10.1016/j.baae.2015.02.004

  • CrossRef
  • Google Scholar

• 200

  WiszM. S.HijmansR. J.LiJ.PetersonA. T.GrahamC. H.GuisanA. (2008). Effects of sample size on the performance of species distribution models.Divers. Distrib.14763–773. 10.1111/j.1472-4642.2008.00482.x

  • CrossRef
  • Google Scholar

• 201

  WuJ.PorinchuD. F. (2019). A high-resolution sedimentary charcoal- and geochemistry-based reconstruction of late Holocene fire regimes in the páramo of Chirripó National Park, Costa Rica.Quat. Res. 93, 314–329.

  • Google Scholar

• 202

  WuX.IslamA. S. M. F.LimpotN.MackasmielL.MierzwaJ.CortésA. J.et al (2020). Genome-wide snp identification and association mapping for seed mineral concentration in mung bean (Vigna radiata L.).Front. Genet.11:656. 10.3389/fgene.2020.00656

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 203

  YackulcC. B.NicholsJ. D.ReidJ.DerR. (2015). To predict the niche, model colonization and extinction.Ecology9616–23. 10.1890/14-1361.1

  • CrossRef
  • Google Scholar

• 204

  YoungK. R.DuchicelaS. (2020). Abandoning holocene dreams: proactive biodiversity conservation in a changing world.Ann. Am. Assoc. Geogr. 1–9. 10.1080/24694452.2020.1785833

  • CrossRef
  • Google Scholar

• 205

  ZellwegerF.De FrenneP.LenoirJ.RocchiniD.CoomesD. (2019). Advances in microclimate ecology arising from remote sensing.Trends Ecol. Evol.34327–341. 10.1016/j.tree.2018.12.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 206

  ZellwegerF.De FrenneP.LenoirJ.VangansbekeP.VerheyenK.Bernhardt-RömermannM.et al (2020). Forest microclimate dynamics drive plant responses to warming.Science368772–775.

  • Google Scholar

• 207

  ZengY.LowB. W.YeoD. C. J. (2016). Novel methods to select environmental variables in maxent: a case study using invasive crayfish.Ecol. Model.3415–13. 10.1016/j.ecolmodel.2016.09.019

  • CrossRef
  • Google Scholar

• 208

  ZomerM. A.RamsayP. M. (2020). The impact of fire intensity on plant growth forms in high-altitude andean grassland.bioRxiv [Preprint], 10.1101/2020.04.25.061051

  • CrossRef
  • Google Scholar