INTRODUCTION

Anthropogenic climate change (ACC) is one of the most challenging threats to biodiversity in the 21st century, leading to an increase in temperature, changes in precipitation regimes, and more frequent extreme weather events because of human activities (Beaumont et al. 2011, IPCC 2014). Although ACC is a global phenomenon, its effects are not homogeneous; it has especially strong effects on ecosystems located at the extremes of climatic gradients, such as tropical montane forests (Pacifici et al. 2015, Brotons et al. 2019). Tropical montane forests are ecosystems that extend from the base of mountains to the tree line, and they are highly vulnerable to ACC because of their ecological, topographical, and historical characteristics (Price et al. 2011, Freeman et al. 2018, Bender et al. 2019, Rahbek et al. 2019a). In these regions, the combination of temperature and topography generates a marked climatic zonation with differing temperatures across short distances, favoring heterogeneous habitat configuration as altitude increases (Rzedowski 2006).

In response to climatic alterations, species in montane regions are expected to search for suitable sites by moving to higher altitudes, depending on topography, ecological specialization, and their climatic preferences (Rojas-Soto et al. 2012, Şekercioĝlu et al. 2012, Freeman et al. 2018, Bender et al. 2019). Thus, the first expected change is a modification of the distribution area, which may also produce alterations in demography, synchrony of events (e.g., flowering, migration, or breeding), biotic interactions (e.g., prey availability), and community dynamics (Pacifici et al. 2015, Brotons et al. 2019). Species that are distributed in environments that limit their movements, such as those at higher elevations, could be more strongly impacted and may even face local extinction (Lenoir and Svenning 2015, Foden et al. 2019). However, beyond these general theoretical expectations, details about the response of montane species to the accelerated climatic alterations are still poorly known. Therefore, we need to study species’ distribution dynamics to improve our understanding of potential shifts in species and communities (e.g., Prieto-Torres et al. 2021).

The Mesoamerican montane forests (MMF), comprising the highlands of central Mexico to Panama (Fig. 1), are considered one of the most diverse and relevant regions in Mesoamerica. They lie in a transition zone between the Neotropical and the Nearctic realms, representing an area of biotic interaction between faunas with different affinities. Nearctic lineages tend to occupy the upper mountains, Neotropical lineages are distributed in the lower parts, and the intermediate altitude zone shows mixed biotas (Halffter et al. 2008). Geographically, species richness reaches its maximum in the contact zones between lowlands and mountains along the eastern Mexico slope and decreases as altitude increases (Koleff et al. 2008, Navarro-Sigüenza et al. 2014). Given this high diversity, the MMF are recognized as a center of diversification, endemism, and biogeographic transition for many taxa (Navarro-Sigüenza et al. 2007, Sánchez-Ramos et al. 2018, Moreno-Contreras et al. 2020, Morrone 2020, Ramírez-Albores et al. 2020).

Temperature and precipitation are the main factors controlling the range limits of montane species. Recent studies show spatially heterogeneous temperature and precipitation patterns in Mesoamerica over the last three decades, with a slight increase in mean annual temperature and more pronounced dry and wet seasons (Cuervo-Robayo et al. 2020, Wootton et al. 2022). Combined with the complex topography, these are key factors that contribute to a narrow range of suitable conditions for montane species (Price et al. 2011, Payne et al. 2017, Freeman et al. 2018). In addition, the orientation of the main mountain ranges is crucial in determining precipitation and humidity patterns (Rzedowski 2006, Challenger and Soberón 2008).

Owls (order Strigiformes) are mostly nocturnal birds of prey that feed on other birds, small mammals, reptiles, and invertebrates, and thus play an important role in ecosystems, functioning as regulators of lower trophic levels (König and Weick 2009, Estes et al. 2011). Most owl species in the world are widely distributed in the tropical regions; they are particularly diverse in Mesoamerica, with 38 species (König and Weick 2009). Of these, 17 are endemic to Mesoamerica, whereas nine extend their distribution to the north, eight to the south, and another four are distributed throughout the continent. Mesoamerican owls are distributed in three relatively well-defined altitudinal ranges: 28% (11 species) occur from sea level to 1500 m, 46% (16 species) occur from sea level to approximately 2000 m, and 21% (eight species) occur from approximately 1500 m to 4500 m (Enríquez 2017). Although owls continue to be one of the least studied bird taxa in Mesoamerica (Enríquez et al. 2012), recent studies show a growing interest in exploring ecological traits, population trends, and geographic distributions (e.g., Valencia-Herverth et al. 2012, Vázquez-Pérez and Enríquez 2016, Enríquez 2017, Fernández Martínez 2017, Ramírez-Santos et al. 2018, Ayma et al. 2019).

One approach to exploring the potential change in the distribution of owls is the use of ecological niche models (ENMs). These models are mathematical abstractions that estimate a species’ distribution based on correlative relationships between species’ occurrences and environmental factors (Peterson et al. 2011, Zurell and Engler 2019). When models are projected onto future climates, they can suggest geographic distributional shifts driven by alternative ACC scenarios (Foden et al. 2019). This information can then be used to test hypotheses about the dynamics of the species distribution under climate variations.

Here, our main goal was to analyze the potential effect of anthropogenic climate change on the geographic distribution of Strigiformes inhabiting the MMF. To achieve this, we proposed to (1) characterize current distributional patterns in terms of range size and altitudinal distribution of the species, (2) determine expected distributional changes of the species under alternative ACC scenarios for the years 2050 and 2070, and (3) examine how patterns of owl species richness might change with ACC. We hypothesized a shift of most species toward higher altitudes coupled with a reduction in their range size, expecting the most severe changes in species located at altitudes above 1500 m. For species below 1500 m, the direction of their movements is uncertain, dependent on local conditions and individual species’ climatic preferences, but if they move to lower altitudes the range size will increase. Species with a wide altitudinal distribution are not expected to show significant change because they are able to tolerate a broad range of climatic conditions. Finally, we expected an altitudinal shift with species moving from lower elevations to the highlands, resulting in lower owl species richness in the lowlands and a similar number of species at mid-elevations. Lower owl species richness was also expected in the highlands because of a reduction in the range size of the species present there.

METHODS

Study area

Montane forests are herein defined as the areas extending from the base of the mountain to its tree line and comprise oak forests, pine forests, mixed coniferous forests, cloud forests, and other less extensive vegetation types, such as spruce-fir forests (Rzedowski 2006). These areas exhibit a humid subtropical to temperate climate with annual temperatures of 5 °C to 25 °C and annual precipitation of 600 mm to 1200 mm, although some areas record over 3000 mm. Overall, the altitudinal range of the considered vegetation types vary from 800 m to 3600 m (Rzedowski 2006, Challenger and Soberón 2008). The Mesoamerican montane forests were delimited according to the biogeographical regionalization of the Neotropics (Morrone 2014) and cropped with the terrestrial ecoregions classification (Dinerstein et al. 2017) including the aforementioned vegetation types. The study area is found from north-central Mexico to the northern tip of Panama and grouped into six biogeographic provinces with discontinuities in the Isthmus of Tehuantepec and the Nicaraguan Depression (Fig. 1).

Species sampling, locality records, and climatic variables

Locality records were prepared in three steps. Starting with the 38 owl species distributed in Mesoamerica (König and Weick 2009), we compiled occurrence records from the “Atlas de las Aves de México” (Navarro-Sigüenza et al. 2003), Sistema Nacional de Información sobre Biodiversidad de México-Conabio (SNIB-Conabio; https://www.snib.mx), and Global Biodiversity Information Facility (GBIF; https://www.gbif.org; access links for downloaded GBIF records are in Appendix 1). Second, we carefully reviewed all the records, discarding those with incomplete or duplicated information. This was a critical step because information quality strongly impacts model performance. We kept all historical records to calibrate the models, because the records for most species were from between 1900 and 1965, and the climate in northern Mesoamerica did not vary drastically throughout the 20th century and the first decade of 21st century (Cuervo-Robayo et al. 2020). Third, to reduce possible sampling biases and model overfitting, we retained only the records corresponding to localities separated by at least the mean distance between occurrence records (Appendix 1) with the “spThin” package (Aiello-Lammens et al. 2015) in R (R Core Team 2021). We selected 35 of the 38 Mesoamerican owl species (scientific and common names provided in Appendix 1), considering (1) distribution, functional role, and altitudinal patterns in the MMF (Valencia-Herverth et al. 2012, Vázquez-Pérez and Enríquez 2016, Enríquez 2017, Fernández Martínez 2017, Billerman et al. 2022); and (2) presence records in at least 20 localities in the study area (the number of localities proposed to be sufficient to create and validate the model; Pearson et al. 2007). Although some species can be more strongly associated with other vegetation types (e.g., Burrowing Owl, Athene cunicularia), all were included because there were reliable records of their presence within the delimited area of analysis. Three species (Glaucidium gnoma, G. hoskinsii, and Megascops lambi) were excluded because they did not meet the criterion of a minimum of 20 records.

A subset of potential predictor variables was selected (described below) from the bioclimatic variables provided by WorldClim version 2.1 (Fick and Hijmans 2017) at a resolution of 2.5′ (∼5 km²), which are derived from monthly temperature and rainfall values. From the same repository, an elevation layer with the same resolution was downloaded for spatial analysis of the distributions. The model calibration area was estimated for each species using the intersection of occurrence records with the terrestrial ecoregions (Dinerstein et al. 2017) and the biogeographical provinces of the Neotropics (Morrone 2014), considering that both approaches can address their historical and ecological limits (Soberón and Peterson 2005, Prieto-Torres et al. 2021). Finally, a 1° (∼100 km²) buffer was drawn around the previously delimited area.

The climate projections used were the RCP 4.5 and RCP 8.5 scenarios for the years 2050 and 2070, based on CMIP5 data available at the WorldClim portal. RCP 4.5 is an intermediate stabilization scenario in which emissions peak around 2040 and then decline, whereas RCP 8.5 represents very high emissions of greenhouse gasses and few climate change mitigation policies (IPCC 2014). Thus, for each species we considered the present plus four future scenarios. Given that the choice of different general circulation models (GCMs) has been identified as a source of variability between distribution models (Zappa and Shepherd 2017, Fajardo et al. 2020), we employed the GCM-CompareR platform (Fajardo et al. 2020), which adopts a “storyline” approach to classify GCMs into narratives representing future climate conditions (Zappa and Shepherd 2017). Projections can also be refined by identifying how the future estimates might depend on initial experimental conditions, algorithms, and model biases (Guevara et al. 2018). MIROC5 was the selected model because it incorporates a realistic simulation of El Niño–Southern Oscillation, on the basis of an improved estimation of precipitation, equatorial ocean surface temperature, and zonal mean atmospheric fields (Watanabe et al. 2010). Our analyses were performed under the expectation of higher temperatures and slightly less precipitation than present for both 2050 and 2070.

Ecological niche modeling

Ecological niche models were constructed separately for each species by using Maxent version 3.4.3 (Phillips et al. 2017) with the “kuenm” package (Cobos et al. 2019). Maxent has shown good performance with presence-only data, whereas kuenm allows to generate candidate models with different parameterization (regularization factors and features). To minimize collinearity of predictor variables and model overfitting, a subset of potential predictor variables was constructed on the basis of a Pearson correlation coefficient (r < 0.8) and the Variance inflation factor (VIF < 10). The parameters that optimize the balance between goodness-of-fit and complexity (Appendix 2) were identified by testing a variety of regularization multipliers (0.1 to 1 every 0.1, and 1 to 4 at intervals of 1) and several combinations of five feature types (“basic” combination of linear, quadratic, product, threshold, and hinge; sensu Phillips et al. 2017). The candidate models’ performance was evaluated via the partial area under the ROC curve (partial ROC), an omission rate (E) lower than 5%, and least complexity according to the corrected Akaike’s information criterion (AICc), selecting those that met all three criteria (Appendix 2). The models were constructed using a random sample of 70% of the locality records as training data and the remaining 30% as validation data, 50,000 background points and 5 replicates.

Future climate projections might include non-analogous conditions (i.e., conditions beyond those available in the model calibration), which can lead to uncertainty in geographical predictions (Owens et al. 2013, Guevara et al. 2018). To identify areas where extrapolation risks could be expected, constructed models were transferred under two assumptions (unconstrained extrapolation and clamping), and for each model the response curves and the geographical prediction were evaluated. Also, a mobility-oriented parity test (Owens et al. 2013) was performed to identify sites with a high degree of environmental dissimilarity. Finally, to generate distribution maps, the median values of the replicates were considered. Presence-absence maps were obtained by converting the cloglog output format (a continuous scale ranging from 0 to 1 of environmental suitability values) to binary, using the 10-percentile training presence threshold values (Appendix 2) with the “raster” package (Hijmans 2021). This threshold omits the 10% of records with the lowest suitability values (under the assumption that these sites are not representative of the species’ requirements), to exclude outliers (Escalante et al. 2013) and thus minimize commission errors (i.e., predicting a species as present when it is not).

Spatial analysis of distributions

Potential impacts of ACC on the species were based on changes in three aspects of their distributions: extent of geographic distribution, altitudinal shift, and species richness. These changes were calculated by subtracting future from current potential distributions, and all post-modeling calculations were performed with the “raster” package (Hijmans 2021). The extent of geographic distribution was characterized by quantifying the range size (km²) and presence within the study area (percentage). The range size was classified into three categories according to the total area (km²) in which the species was predicted to be present in the current scenario (Appendix 3): large (upper quartile > 180,000 km²), intermediate (< 180,000 km², > 20,000 km²), and small (lower quartile < 20,000 km²). The altitudinal distribution was classified into four categories on the basis of the quartile ordination of the current species distribution (Appendix 3; Prieto-Torres et al. 2021) and the median value of each species: highlands (upper quartile 2100 m), mid-elevations (< 2100 m, > 1100 m), lowlands (lower quartile 1100 m), and generalists (difference between Q3 and Q1 > 1000 m). Also, a Kruskal-Wallis test was implemented to estimate differences among altitudinal and range size categories on the basis of species’ range changes. Last, current and future patterns of owl species richness at the regional scale were estimated by adding all the binary distribution maps obtained and converting them to a standardized raster (0–1 values). The patterns obtained for 2050 and 2070 under the two RCP scenarios were compared by subtracting the owl species richness per site with respect to the current distribution. All analyses were performed in R.

RESULTS

Species models and current distributional patterns

The selected species belonged to 12 genera. Seven genera are represented by a single species, whereas Glaucidium and Megascops are represented by seven and 10 species, respectively (Appendix 1). The models exhibited good performance (i.e., far more accurate than expected by chance), with significant values for the partial ROC test (1.08–1.95, p < 0.05), and low omission rates (< 10%); model parameters and performance metrics are detailed in Appendix 1, and individual responses for each modeled species are in Appendix 4.

The area of the predicted distribution varied considerably among species (Appendix 3), from 3000 km² for the Costa Rican Pygmy-Owl (Glaucidium costaricanum) to 375,000 km² for the American Barn Owl (Tyto alba). According to our range size categories, nine species (26%) had small size ranges, 17 (48%) were in the intermediate category, and nine (26%) had large ranges (Fig. 2a, Appendix 3). Categorization by altitudinal range resulted in 13 lowland, 13 mid-elevation, two highland, and seven generalist species (Appendix 3). The altitudinal distribution showed species throughout the altitudinal gradient (Fig. 2b). The Stygian Owl (Asio stygius) had the broadest altitudinal range, from sea level to 3200 m, and the narrowest altitudinal range was shown by the Striped Owl (Asio clamator) from sea level to 1250 m. In addition, even though some species had broad altitudinal ranges, their distributions were generally skewed toward one end of the altitudinal gradient. For example, although the Northern Saw-whet Owl (Aegolius acadicus) had its lower altitudinal limit around 1000 m, it was mostly distributed in the upper mountains, and although the Crested Owl (Lophostrix cristata) was mostly predicted at elevations below 250 m, some areas of its potential distribution reached up to 2500 m. Sixty percent (n = 9) of the species found in highlands and those found in mid-elevations were highly restricted to their respective altitudinal ranges, with more than 75% of their potential distribution occurring in that altitudinal range (Fig. 3). Examples include the genera Megascops, Aegolius, Glaucidium, and Strix, which had distributions ≤ 50,000 km². Finally, there was an apparent association between altitudinal range and range size; species occupying intermediate elevations mostly had small range sizes (56%), those occupying lowlands generally had intermediate range size (47%), and altitude generalists mostly had large distributions (56%).

Impacts of ACC

Areas with non-analogous climates were identified by the mobility-oriented parity (MOP) test. However, they represented a low proportion of our predictions (< 10% on average) and thus were included in subsequent analysis, although we treated them with greater caution (especially in RCP 8.5). These areas were located mainly in the lowlands near montane areas, such as the northern lowlands of the Pacific and Gulf of Mexico slopes, the Nicaraguan Depression, and the lowlands of southern Central America. Considering the MOP results, the response curves, and the fit of geographic predictions, a single extrapolation method, extrapolation by clamping, was selected for all scenarios (Appendix 2).

On the basis of the estimates, three general patterns were identified. First, ACC would lead to a reduction in distribution for 83% of the owls considered (29 species), decreasing consistently across all climate scenarios by an average of 25% for 2050 and 29% for 2070 compared to the present (Fig. 4). Nine species’ distribution areas decreased by less than 25%, whereas 12 showed reductions by 25–50% and eight decreased by more than 50%. The Eastern Screech-Owl (Megascops asio; 73–96%) and the Tamaulipas Pygmy-Owl (Glaucidium sanchezi; 55–92%) consistently showed the highest percentages of decrease in their range size. Other species with substantial reductions were the Bare-shanked Screech-Owl (M. clarkii; 35–57%), Bearded Screech-Owl (M. barbarus; 58–68%), and Northern Saw-whet Owl (73–96%). According to the altitudinal categorization, highland species were the most affected (Fig. 4), with 40% decreases under the closest and theoretically least damaging scenario (RCP 4.5 to 2050), and 57% for the most distant and most severe scenario (RCP 8.5 to 2070). The mid-elevation species showed a similar, though somewhat less severe trend (39–59%), whereas lowland species showed the lowest percentages of change (10–16%). Contrary to expectations, species with occasional presence in montane forests (e.g., altitudinal generalists, Micrathene whitneyi, Athene cunicularia, and Tyto alba; or lowland species, Pulsatrix perspicillata, Glaucidium ridgwayi, and Asio clamator) increased their range sizes (1.5–26% on average). Among the range size categories (Fig. 4), species with small distributions showed the highest percentages of range reduction (36–39%), whereas large range species had reductions of 8–12%.

Most species (88%; 31 spp.) were projected to shift to higher altitudes (Table 1). The shifts were similar among climate scenarios (∼135 m) except for RCP 8.5 in 2070, which had an estimated average increase of 245 m. Once again, the Eastern Screech-Owl and the Tamaulipas Pygmy-Owl had the most notable shifts in all scenarios, with a predicted altitudinal change of 648–1296 m and 577–1014 m, respectively. Other species that showed strong altitudinal increases were the Spotted Owl (Strix occidentalis) and the Bare-shanked Screech-Owl, both mid-elevation species whose movements ranged between 275 and 518 m. Mid-elevation species had the largest altitudinal movements (200–341 m; Table 1), whereas lowland species showed the least change (30 m by 2050, with a maximum of 165 m by 2070). The highland species (genus Aegolius) had opposite responses in the RCP 4.5 in the 2070 scenario: one showed an increase in altitude, whereas the other decreased. Four species (12%) descended 122 m in altitude, and three of those were classified as generalists: Short-eared Owl (Asio flammeus), Stygian Owl, American Barn Owl, and Burrowing Owl. Species with small range sizes had the greatest altitudinal shifts despite their geographic restriction, with movements of 158–353 m (Table 1).

When considering the proportion of the species’ total distribution range that falls within the MMF as defined at present (Fig. 3), 74% (26) of the owl species showed a higher proportion of their potential distribution within the MMF, likely because of their altitudinal shifts. Especially, the proportion of the distribution of the Tamaulipas Pygmy-Owl within the MMF increased from 45% at present to 83% by 2050 and 97% by 2070. On the contrary, six species’ distribution within the MMF decreased: Megascops cooperi, Micrathene whitneyi, Asio flammeus, Aegolius ridgwayi, Glaucidium cobanense, and Megascops seductus. All of these species are currently distributed in the MMF regions but are more associated with other vegetation types. The three remaining species (Strix nigrolineata, Megascops kennicottii, and Bubo virginianus) remained practically constant. By altitudinal range, mid-elevation species increased the most in MMF distribution, especially under the RCP 8.5 scenario for both years, whereas by range size, species with intermediate distributions increased the most in all scenarios.

Species richness patterns

The estimated species richness varied from five to 21 species under the current climate scenario. Sites of high species richness (> 10 species, dark-colored sites in Fig. 3) were mostly located in the contact zones between highlands and lowlands as well as between biogeographic provinces: for example, between the Sierra Madre Oriental and adjacent lowlands, between the Transmexican Volcanic Belt with the Sierra Madre Occidental, and in the southern part of the Sierra Madre del Sur. The lowest species richness was observed in northern Mexico, especially in the northwest of the Sierra Madre Occidental and in the western part of the Nuclear Central American Highlands (Fig. 3). Among biogeographic provinces, the Transmexican Volcanic Belt had the highest species richness with 23, followed by the Sierra Madre del Sur with 20; the Sierra Madre Occidental had the lowest richness, with 17.

All climate scenarios produced a similar pattern of change in owl species richness, but they differed in the total number of species (Fig. 5). The main species losses occurred in the northern part of the Sierra Madre Occidental, the central part of the Sierra Madre Oriental, the southern portion of the Transmexican Volcanic Belt and Sierra Madre del Sur, and the northwestern part of the Nuclear Central American Highlands. The RCP 8.5 for 2050 and 2070 showed particularly widespread losses in these areas. The areas with species gain were in the southern Sierra Madre Occidental, northern Sierra Madre Oriental, and western Transmexican Volcanic Belt. Overall, the projected patterns showed a loss of 11 species and a gain of six, except for RCP 4.5 in 2050, where losses of 10 species were estimated, and for RCP 8.5 in 2070, which showed a maximum gain of seven species.

DISCUSSION

According to the models generated, ACC is predicted to have severe impacts in the coming decades for most of the owls of the Mesoamerican montane forests. Our results are consistent with the widely accepted hypothesis of species range shift to higher altitudes combined with reduction in distribution as species try to track their climatic preferences (Table 1; Parmesan 2006, Pacifici et al. 2015, Bender et al. 2019). We found this pattern in most of the owl species (~86%), and although the scenarios in 2070 show the most severe results, the projections for 2050 require more attention. The mid-elevation species emerge as one of the groups that are most vulnerable to ACC, showing the highest rates of change (Table 1, Fig. 4; Lenoir and Svenning 2015). This is the case with the Tamaulipas Pygmy-Owl and Eastern Screech-Owl, which show reductions of up to 73% in range size and altitudinal shifts of around 650 m. This pattern may be explained by the complex climatic patterns and notable changes in conditions over relatively short distances in mid-altitude areas (Rahbek et al. 2019a, 2019b). Moreover, because highland species cannot expand their distributions beyond the mountain habitable zone (Şekercioglu et al. 2012, Freeman et al. 2018), these species are also projected to be very strongly affected, potentially threatening their persistence in the region (Şekercioglu et al. 2007, Urban 2015). MMF have undergone significant transformation because of land use changes, and in many cases have undergone accelerated biodiversity loss over the last 50 years (Challenger and Soberón 2008, Enríquez 2017). The synergistic effect of land use changes and ACC could limit species’ ability to follow their climatic preferences (Rojas-Soto et al. 2012) and result in even greater negative impacts (Jetz et al. 2007, Beaumont et al. 2011).

As hypothesized, species richness remained relatively constant at middle elevations and the largest losses were in the highlands and the contiguous lowlands (Fig. 4; Bender et al. 2019). When a species shifts upward, it can be replaced by species from lower elevations (Bender et al. 2019), so altitudinal shifts do not necessarily lead to a net decrease in species richness at mid-elevations (Colwell et al. 2008, Rahbek et al. 2019a). However, species richness is expected to decrease at both ends of the altitudinal gradient, though for different reasons at each end. The lowlands surrounding the MMF of northern and central Mexico might show a reduction because there are no longer species to replace the emigrants in those areas (Dunn and Møller 2019). Meanwhile, highland species (e.g., Aegolius spp., Saw-Whet Owls) cannot expand their distributions beyond the mountain habitable zone, so their distribution areas are expected to decrease (Şekercioglu et al. 2012, Freeman et al. 2018). Moreover, the gains and losses in owl species richness suggest that the highlands are climatically different from the lowlands (Fig 4; Rahbek et al. 2019a), favoring geographic isolation of remaining sites with high species richness (Şekercioĝlu et al. 2012, Payne et al. 2017). Special attention should be paid to the Transmexican Volcanic Belt and Sierra Madre del Sur, where the greatest reduction in owl species richness is expected to occur.

Although our results are interesting and informative, they should be interpreted with caution. Species distribution is shaped by factors other than climate that our models are unable to process but should be considered (Peterson et al. 2011, Guisan et al. 2017). These include ecological factors like density-dependent interactions (e.g., prey availability) and population dynamics (Parmesan 2006, Dunn and Møller 2019), or historical processes such as speciation or colonization (Espinosa and Ocegueda 2008, Rahbek et al. 2019b). Moreover, multiple compensatory mechanisms such as modifying activity patterns or physiological adjustment have been recorded when climatic changes exceed the natural variation of the region (Prieto-Torres et al. 2021). Mechanisms such as diet modification, reduction of body mass, demographic responses, or use of secondary vegetation may buffer the effects of ACC (Newton 2003, Dunn and Møller 2019), although ecological information is scarce for most Mesoamerican owls. Characterization of species’ responses to environmental changes is clearly a complex task, but information on their distributions is a critical starting point to understand underlying mechanisms (Foden et al. 2019).

Species with small niches are typically specialists, both ecologically and in terms of distribution, so in theory they are less likely to be able to adapt to new climatic conditions (Wiens et al. 2010). Our findings are in line with this notion because the species with the largest changes were specialists, restricted to a particular altitudinal range or area. Moreover, the projection of mostly loser (specialists) and few winner species (generalists) under new climatic conditions supports the hypothesis that the ecological specialization of a species is a key attribute influencing species vulnerability (Thuiller et al. 2005, Pacifici et al. 2015, Guisan et al. 2017). The pattern found here (range shift to higher altitudes and reduction in range size) supports previous studies conducted on several study systems. For example, similar trends were found within a single province, the Sierra Madre Oriental (Rojas-Soto et al. 2012); indeed, just like our study, the central part of the Sierra Madre Oriental was found to be the most strongly affected. These results have also been found when assessing the effects of projected climate changes on endemic bird species in the main montane regions of Mexico (Sierra-Morales et al. 2021) and tropical mountain regions worldwide (Freeman et al. 2018).

Consistent with previous studies (Sánchez-Ramos et al. 2018, Bender et al. 2019), we found that species that inhabit higher altitudes had smaller range sizes. This was evident in owls with median elevations above 2000 m, whose distributions were smaller than 50,000 km² and for which ≥ 90% of the range was contained within the MMF. The distribution limits of a species are mostly influenced by large-scale environmental variables, such as temperature, precipitation, humidity, or vegetation type (Sexton et al. 2009, Freeman et al. 2018, Rahbek et al. 2019b). Altitude can be considered an indicator of these significant variables (Dunn and Møller 2019); thus, classifying species according to their altitudinal distribution seems to be a straightforward strategy to understand distributional patterns. Moreover, responses to ACC should be similar among species that share an altitudinal range, as occurred in our models within mid-altitude and highland owls (Şekercioĝlu et al. 2012, Rahbek et al. 2019b).

Finally, knowledge is limited for most Mesoamerican owl species, and this affects our confidence in their conservation status (Camacho-Varela and Arguedas-Porras 2017, Eisermann and Avendaño 2017, Enríquez and Vázquez-Pérez 2017, Ramírez-Albores et al. 2020). Here, we provide relevant information in terms of distributional patterns and owl species richness, which can be helpful criteria for assessing their conservation status (IUCN 2021, Prieto-Torres et al. 2021). Restricted distributions are a risk factor associated with the geographic isolation of species, leading to a low capacity for response to environmental changes (Şekercioĝlu et al. 2012, Urban 2015). Nine of the owl species we analyzed (26%) have a range size that could justify including them in some category of risk, although they are not currently recognized (IUCN 2021, https://www.iucnredlist.org). For example, two species (Glaucidium costaricanum and Megascops clarkii) had an estimated distribution of less than 5000 km², which could qualify them as Endangered, whereas seven could be Vulnerable given their distributions smaller than 20,000 km² (BirdLife International 2022). From this conservation perspective, further attention is also required to explore changes in species turnover (e.g., Ochoa-Ochoa et al. 2014) and changes at the level of individual species, communities, or ecosystem functioning (Pacifici et al. 2015, Brotons et al. 2019). These potential effects may provide a picture of the possible impact of human-induced changes not only on charismatic owls, but on the already threatened whole biota of our planet.

LITERATURE CITED

Aiello-Lammens, M. E., R. A. Boria, A. Radosavljevic, B. Vilela, and R. P. Anderson. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38(5):541-545. https://doi.org/10.1111/ecog.01132

Ayma, G. R., A. O. Kerstupp, A. G. Velasco, and J. I. G. Rojas. 2019. Diet and prey delivery of Burrowing Owls (Athene cunicularia hypugaea) during the breeding season in the Chihuahuan Desert, Mexico. Journal of Raptor Research 53(1):75-83. https://doi.org/10.3356/JRR-17-90

Beaumont, L. J., A. Pitman, S. Perkins, N. E. Zimmermann, N. G. Yoccoz, and W. Thuiller. 2011. Impacts of climate change on the world’s most exceptional ecoregions. Proceedings of the National Academy of Sciences 108(6):2306-2311. https://doi.org/10.1073/pnas.1007217108

Bender, I. M. A., W. D. Kissling, K. Böhning-Gaese, I. Hensen, I. Kühn, L. Nowak, T. Töpfer, T. Wiegand, D. M. Dehling, and M. Schleuning. 2019. Projected impacts of climate change on functional diversity of frugivorous birds along a tropical elevational gradient. Scientific Reports 9:17708. https://doi.org/10.1038/s41598-019-53409-6

Billerman, S. M., B. K. Keeney, P. G. Rodewald, and T. S. Schulenberg, editors. 2022. Birds of the world. Cornell Laboratory of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow

BirdLife International. 2022. IUCN Red List for birds. http://www.birdlife.org

Brotons, L., S. Herrando, F. Jiguet, and A. Lehikoinen. 2019. Bird communities and climate change. Pages 221-235 in P. O. Dunn and A. P. Møller, editors. Effects of climate change on birds. Second edition. Oxford University Press, Oxford, UK. https://doi.org/10.1093/oso/9780198824268.003.0016

Camacho-Varela, P., and R. Arguedas-Porras. 2017. The owls of Costa Rica. Pages 291-315 in P. L. Enríquez, editor. Neotropical owls: diversity and conservation. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-57108-9_8

Challenger, A., and J. Soberón. 2008. Los ecosistemas terrestres. Pages 87-108 in J. Soberón, G. Halffter, and J. Llorente-Bousquets, editors. Capital natural de México. Volume 1. Conocimiento actual de la biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico City, Mexico.

Cobos, M. E., A. T. Peterson, N. Barve, and L. Osorio-Olvera. 2019. kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7:e6281. https://doi.org/10.7717/peerj.6281

Colwell, R. K., G. Brehm, C. L. Cardelús, A. C. Gilman, and J. T. Longino. 2008. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322(5899):258-261. https://doi.org/10.1126/SCIENCE.1162547

Cuervo-Robayo, A. P., C. Ureta, M. A. Gómez-Albores, A. K. Meneses-Mosquera, O. Téllez-Valdés, and E. Martínez-Meyer. 2020. One hundred years of climate change in Mexico. PLoS ONE 15(7):e0209808. https://doi.org/10.1371/journal.pone.0209808

Dinerstein, E., D. Olson, A. Joshi, C. Vynne, N. D. Burgess, E. Wikramanayake, N. Hahn, S. Palminteri, P. Hedao, R. Noss, M. Hansen, H. Locke, E. C. Ellis, B. Jones, C. V. Barber, R. Hayes, C. Kormos, V. Martin, E. Crist, W. Sechrest, L. Price, J. E. M. Baillie, D. Weeden, K. Suckling, C. Davis, N. Sizer, R. Moore, D. Thau, T. Birch, P. Potapov, S. Turubanova, A. Tyukavina, N. De Souza, L. Pintea, J. C. Brito, O. A. Llewellyn, A. G. Miller, A. Patzelt, S. A. Ghazanfar, J. Timberlake, H. Klöser, Y. Shennan-Farpón, R. Kindt, J. P. Barnekow Lillesø, P. van Breugel, L. Graudal, M. Voge, K. F. Al-Shammari, and M. Saleem. 2017. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67(6):534-545. https://doi.org/10.1093/biosci/bix014

Dunn, P. O., and A. P. Møller. 2019. Effects of climate change on birds. Second edition. Oxford University Press, Oxford, UK. https://doi.org/10.1093/oso/9780198824268.001.0001

Eisermann, K., and C. Avendaño. 2017. The owls of Guatemala. Pages 447-515 in P. L Enríquez, editor. Neotropical owls: diversity and conservation. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-57108-9_13

Enríquez, P. L. 2017. Neotropical owls: diversity and conservation. First edition. Springer, Cham, Switzerland.

Enríquez, P. L., K. Eisermann, and H. Mikkola. 2012. Los búhos de México y Centroamérica: necesidades en investigación y conservación. Ornitología Neotropical 23:245-258.

Enríquez, P. L., and J. R. Vázquez-Pérez. 2017. The owls of Mexico. Pages 535-570 in P. L. Enríquez, editor. Neotropical owls: diversity and conservation. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-57108-9_15

Escalante, T., G. Rodríguez-Tapia, M. Linaje, P. Illoldi-Rangel, and R. González-López. 2013. Identification of areas of endemism from species distribution models: threshold selection and Nearctic mammals. TIP Revista Especializada en Ciencias Químico-Biológicas 16(1):5-17. https://doi.org/10.1016/s1405-888x(13)72073-4

Espinosa Organista, D., and S. Ocegueda Cruz. 2008. El conocimiento biogeográfico de las especies y su regionalización natural. Pages 33-65 in J. Soberón, G. Halffter, and J. Llorente-Bousquets, editors. Capital natural de México. Volume 1. Conocimiento actual de la biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico City, Mexico.

Estes, J. A., J. Terborgh, J. S. Brashares, M. E. Power, J. Berger, W. J. Bond, S. R. Carpenter, T. E. Essington, R. D. Holt, J. B. C. Jackson, R. J. Marquis, L. Oksanen, T. Oksanen, R. T. Paine, E. K. Pikitch, W. J. Ripple, S. A. Sandin, M. Scheffer, T. W. Schoener, J. B. Shurin, A. R. E. Sinclair, M. E. Soulé, R. Virtanen, and D. A. Wardle. 2011. Trophic downgrading of planet earth. Science 333(6040):301-306. https://doi.org/10.1126/science.1205106

Fajardo, J., D. Corcoran, P. R. Roehrdanz, L. Hannah, and P. A. Marquet. 2020. GCM CompareR: a web application to assess differences and assist in the selection of general circulation models for climate change research. Methods in Ecology and Evolution 11(5):656-663. https://doi.org/10.1111/2041-210X.13360

Fernández Martínez, E. C. 2017. Ocupación, uso de hábitat y actividad vocal de una comunidad de búhos en un bosque templado en Tlaxcala, México. Thesis. Universidad Nacional Autónoma de México, México.

Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12):4302-4315. https://doi.org/10.1002/joc.5086

Foden, W. B., B. E. Young, H. R. Akçakaya, R. A. García, A. A. Hoffmann, B. A. Stein, C. D. Thomas, C. J. Wheatley, D. Bickford, J. A. Carr, D. G. Hole, T. G. Martin, M. Pacifici, J. W. Pearce-Higgins, P. J. Platts, P. Visconti, J. E. M. Watson, and B. Huntley. 2019. Climate change vulnerability assessment of species. WIREs Climate Change 10:e551. https://doi.org/10.1002/wcc.551

Freeman, B. G., M. N. Scholer, V. Ruiz-Gutierrez, and J. W. Fitzpatrick. 2018. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proceedings of the National Academy of Sciences 115(47):11982-11987. https://doi.org/10.1073/pnas.1804224115

Guevara, L., J. J. Morrone, and L. León-Paniagua. 2018. Spatial variability in species’ potential distributions during the last glacial maximum under different global circulation models: relevance in evolutionary biology. Journal of Zoological Systematics and Evolutionary Research 57(1):113-126. https://doi.org/10.1111/jzs.12238

Guisan, A., W. Thuiller, and N. E. Zimmermann. 2017. Habitat suitability and distribution models: with applications in R. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/9781139028271

Halffter, G., J. Llorente-Bousquets, and J. J. Morrone. 2008. La perspectiva biogeográfica histórica. Pages 67-86 in J. Soberón, G. Halffter, and J. Llorente-Bousquets, editors. Capital natural de México. Volume 1. Conocimiento actual de la biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico City, Mexico. https://doi.org/https://doi.org/10.5962/bhl.title.113645

Hijmans, R. J. 2021. Geographic data analysis and modeling. R package raster version 3.4-10. https://cran.r-project.org/package=raster

Intergovernmental Panel on Climate Change (IPCC). 2014. Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Core Writing Team, R.K. Pachauri and L.A. Meyer, editors. IPCC, Geneva, Switzerland.

International Union for Conservation of Nature (IUCN). 2021. The IUCN red list of threatened species. Version 2021-2. https://www.iucnredlist.org/

Jetz, W., D. S. Wilcove, and A. P. Dobson. 2007. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biology 5(6):e157. https://doi.org/10.1371/journal.pbio.0050157

Koleff, P., J. Soberón, H. Arita, P. Dávila, O. Flores-Villela, J. Golubov, G. Halffter, A. Lira-Noriega, C. E. Moreno, E. Moreno, M. Munguía, M. Murguía, A. G. Navarro-Sigüenza, O. Téllez, L. Ochoa-Ochoa, A. T. Peterson, and P. Rodríguez. 2008. Patrones de diversidad espacial en grupos selectos de especies. Pages 323-364 in J. Soberón, G. Halffter, and J. Llorente-Bousquets, editors. Capital natural de México. Volume 1. Conocimiento actual de la biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico City, Mexico.

König, C., and F. Weick. 2009. Owls of the world. Second edition. A & C Black, London, UK.

Lenoir, J., and J.-C. Svenning. 2015. Climate-related range shifts — a global multidimensional synthesis and new research directions. Ecography 38(1):15-28. https://doi.org/10.1111/ecog.00967

Moreno-Contreras, I., L. A. Sánchez-González, M. C. Arizmendi, D. A. Prieto-Torres, and A. G. Navarro-Sigüenza. 2020. Climatic niche evolution in the Arremon brunneinucha complex (Aves: Passerellidae) in a Mesoamerican landscape. Evolutionary Biology 47(2):123-132. https://doi.org/10.1007/s11692-020-09498-7

Morrone, J. J. 2014. Biogeographical regionalisation of the Neotropical region. Zootaxa 3782(1):1-110. https://doi.org/10.11646/zootaxa.3782.1.1

Morrone, J. J. 2020. The Mexican transition zone. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-030-47917-6

Navarro-Sigüenza, A. G., A. T. Peterson, and A. Gordillo-Martínez. 2003. Museums working together: the atlas of the birds of Mexico. Bulletin of the British Ornithologists’ Club. 123A:207-225.

Navarro-Sigüenza, A. G., M. F. Rebón-Gallardo, A. Gordillo Martínez, A. T. Peterson, H. Berlanga García, and L. A. Sánchez González. 2014. Biodiversidad de aves en México. Revista Mexicana de Biodiversidad 85(Supplement):476-495. https://doi.org/10.7550/rmb.41882

Navarro-Sigüenza, A. G., A. Lira-Noriega, A. T. Peterson, A. Oliveras, and A. Gordillo-Martínez. 2007. Diversidad, endemismo y conservación de las aves. Pages 461-483 in I. Luna, J. J. Morrone, and D. Espinosa, editors. Biodiversidad de La Faja Volcanica Transmexicana. Universidad Nacional Autónoma de México, Mexico City, Mexico.

Newton, I. 2003. The role of natural factors in the limitation of bird of prey numbers: a brief review of the evidence. Pages 5-23 in D. B. A. Thompson, editor. Birds of prey in a changing environment. Stationery Office/TSO, London, UK.

Ochoa-Ochoa, L. M., M. Munguía, A. Lira-Noriega, V. Sánchez-Cordero, O. Flores-Villela, A. Navarro-Sigüenza, and P. Rodríguez. 2014. Spatial scale and β-diversity of terrestrial vertebrates in Mexico. Revista Mexicana de Biodiversidad 85(3):918-930. https://doi.org/10.7550/RMB.38737

Owens, H. L., L. P. Campbell, L. L. Dornak, E. E. Saupe, N. Barve, J. Soberón, K. Ingenloff, A. Lira-Noriega, C. M. Hensz, C. E. Myers, and A. T. Peterson. 2013. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263:10-18. https://doi.org/10.1016/j.ecolmodel.2013.04.011

Pacifici, M., W. B. Foden, P. Visconti, J. E. M. Watson, S. H. M. Butchart, K. M. Kovacs, B. R. Scheffers, D. G. Hole, T. G. Martin, H. R. Akçakaya, R. T. Corlett, B. Huntley, D. Bickford, J. A. Carr, A. A. Hoffmann, G. F. Midgley, P. Pearce-Kelly, R. G. Pearson, S. E. Williams, S. G. Willis, B. Young, and C. Rondinini. 2015. Assessing species vulnerability to climate change. Nature Climate Change 5:215-224. https://doi.org/10.1038/nclimate2448

Parmesan, C. 2006. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics 37(1):637-669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Payne, D., E. M. Spehn, M. Snethlage, and M. Fischer. 2017. Opportunities for research on mountain biodiversity under global change. Current Opinion in Environmental Sustainability 29:40-47. https://doi.org/10.1016/j.cosust.2017.11.001

Pearson, R. G., C. J. Raxworthy, M. Nakamura, and A. T. Peterson. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 34(1):102-117. https://doi.org/10.1111/j.1365-2699.2006.01594.x

Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martinez-Meyer, M. Nakamura, and M. B. Araújo. 2011. Ecological niches and geographic distributions. First edition. Princeton University Press, Princeton, New Jersey, USA. https://doi.org/10.23943/princeton/9780691136868.003.0003

Phillips, S. J., R. P. Anderson, M. Dudík, R. E. Schapire, and M. E. Blair. 2017. Opening the black box: an open-source release of Maxent. Ecography 40(7):887-893. https://doi.org/10.1111/ecog.03049

Price, M. F., G. Gratzer, L. A. Duguma, T. Kohler, D. Maselli, and R. Romeo. 2011. Mountain forests in a changing world: realizing values, addressing challenges. Food and Agriculture Organization of the United Nations and Centre of Development and Environment, Rome, Italy.

Prieto-Torres, D. A., L. E. Nuñez-Rosas, D. Remolina-Figueroa, and M. del C. Arizmendi. 2021. Most Mexican hummingbirds lose under climate and land-use change: long-term conservation implications. Perspectives in Ecology and Conservation 19(4):487-499. https://doi.org/10.1016/j.pecon.2021.07.001

R Core Team. 2021. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/

Rahbek, C., M. K. Borregaard, A. Antonelli, R. K. Colwell, B. G. Holt, D. Nogues-Bravo, C. M. Ø. Rasmussen, K. Richardson, M. T. Rosing, R. J. Whittaker, and J. Fjeldså. 2019b. Building mountain biodiversity: geological and evolutionary processes. Science 365(6458):1114-1119. https://doi.org/10.1126/science.aax0151

Rahbek, C., M. K. Borregaard, R. K. Colwell, B. Dalsgaard, B. G. Holt, N. Morueta-Holme, D. Nogues-Bravo, R. J. Whittaker, and J. Fjeldså. 2019a. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365(6458):1108-1113. https://doi.org/10.1126/science.aax0149

Ramírez-Albores, J. E., D. A. Prieto-Torres, A. Gordillo-Martínez, L. E. Sánchez-Ramos, and A. G. Navarro-Sigüenza. 2020. Insights for protection of high species richness areas for the conservation of Mesoamerican endemic birds. Diversity and Distributions 27:18-33. https://doi.org/10.1111/ddi.13153

Ramírez-Santos, P., P. L. Enríquez, J. R. Vázquez-Pérez, and J. L. Rangel-Salazar. 2018. Bird behaviour during prey-predator interaction in a tropical forest in México. Pages 29-50 in H. Mikkola, editor. Owls. IntechOpen, London, UK. https://doi.org/10.5772/intechopen.82882

Rojas-Soto, O. R., V. Sosa, and J. F. Ornelas. 2012. Forecasting cloud forest in eastern and southern Mexico: conservation insights under future climate change scenarios. Biodiversity and Conservation 21:2671-2690. https://doi.org/10.1007/s10531-012-0327-x

Rzedowski, J. 2006. Vegetación de México. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico City, México.

Sánchez-Ramos, L. E., A. Gordillo-Martínez, C. Gutiérrez-Arellano, T. Kobelkowsky-Vidrio, C. A. Ríos-Muñoz, and A. G. Navarro-Sigüenza. 2018. Bird diversity patterns in the nuclear Central American highlands: a conservation priority in the northern Neotropics. Tropical Conservation Science 11:1-17. https://doi.org/10.1177/1940082918819073

Şekercioglu, C. H., R. B. Primack, and J. Wormworth. 2012. The effects of climate change on tropical birds. Biological Conservation 148(1):1-18. https://doi.org/10.1016/j.biocon.2011.10.019

Şekercioglu, C. H., S. H. Schneider, J. P. Fay, and S. R. Loarie. 2007. Climate change, elevational range shifts, and bird extinctions. Conservation Biology 22(1):140-50. https://doi.org/10.1111/j.1523-1739.2007.00852.x

Sexton, J. P., P. J. McIntyre, A. L. Angert, and K. J. Rice. 2009. Evolution and ecology of species range limits. Annual Review of Ecology, Evolution, and Systematics 40:415-436. https://doi.org/10.1146/annurev.ecolsys.110308.120317

Sierra-Morales, P., O. Rojas-Soto, C. A. Ríos-Muñoz, L. M. Ochoa-Ochoa, P. Flores-Rodríguez, and R. C. Almazán-Nuñez. 2021. Climate change projections suggest severe decreases in the geographic ranges of bird species restricted to Mexican humid mountain forests. Global Ecology and Conservation 30:e01794. https://doi.org/10.1016/j.gecco.2021.e01794

Soberón, J., and A. T. Peterson. 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2:1-10. https://doi.org/10.17161/bi.v2i0.4

Thuiller, W., S. Lavorel, and M. B. Araújo. 2005. Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecology and Biogeography 14(4):347-357. https://doi.org/10.1111/j.1466-822X.2005.00162.x

Urban, M. C. 2015. Accelerating extinction risk from climate change. Science 348(6234):571-573. https://doi.org/10.1126/science.aaa4984

Valencia-Herverth, J., R. Ortiz-Pulido, and P. L. Enríquez. 2012. Riqueza y distribución espacial de rapaces nocturnas en Hidalgo, México. Huitzil 13(2):116-129. https://doi.org/10.28947/hrmo.2012.13.2.158

Vázquez-Pérez, J. R., and P. L. Enríquez. 2016. Factores temporales y ambientales asociados a los llamados de los búhos en la reserva Selva El Ocote, Chiapas, México. Hornero 31(2):83-88.

Watanabe, M., T. Suzuki, R. O’Ishi, Y. Komuro, S. Watanabe, S. Emori, T. Takemura, M. Chikira, T. Ogura, M. Sekiguchi, K. Takata, D. Yamazaki, T. Yokohata, T. Nozawa, H. Hasumi, H. Tatebe, and M. Kimoto. 2010. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. Journal of Climate 23(23):6312-6335. https://doi.org/10.1175/2010JCLI3679.1

Wiens, J. J., D. D. Ackerly, A. P. Allen, B. L. Anacker, L. B. Buckley, H. V. Cornell, E. I. Damschen, T. J. Davies, J. A. Grytnes, S. P. Harrison, B. A. Hawkins, R. D. Holt, C. M. McCain, and P. R. Stephens. 2010. Niche conservatism as an emerging principle in ecology and conservation biology. Ecology Letters 13(10):1310-1324. https://doi.org/10.1111/j.1461-0248.2010.01515.x

Wootton, A., P. L. Enríquez, and D. Navarrete-Gutiérrez. 2022. Regional patterns of vegetation, temperature, and rainfall trends in the coastal mountain range of Chiapas, Mexico. Atmósfera 36(1):91-122. https://doi.org/10.20937/ATM.53026

Zappa, G., and T. G. Shepherd. 2017. Storylines of atmospheric circulation change for European regional climate impact assessment. Journal of Climate 30(16):6561-6577. https://doi.org/10.1175/JCLI-D-16-0807.1

Zurell, D., and J. O. Engler. 2019. Ecological niche modelling. Pages 60-73 in P. O. Dunn, and A. P. Møller, editors. Effects of climate change on birds. Second edition. Oxford University Press, Oxford, UK. https://doi.org/10.1093/oso/9780198824268.003.0006