1 Accepted version of the paper published in Biotropica

2

3 Title: Elevational distribution of birds in an Eastern African montane environment as
governed by temperature, precipitation, and habitat availability.

5

6 Authors: Yntze van der Hoek¹*, Clélia Sirami², Emmanuel Faida¹, Valens Musemakweli¹, and
7 Deogratias Tuyisingize¹

8

9 The Dian Fossey Gorilla Fund International, Karisoke Research Center, Musanje, Rwanda

10 Dynafor, Université de Toulouse, INRA, Castanet-Tolosan, France

11 *Corresponding author: yvanderhoek@gorillafund.org.

12

13

14

15

16
17 ABSTRACT

18
Tropical species, many of which have limited thermal tolerances and narrow ecological niches,
19
are likely to experience elevational shifts due to climate change. However, our ability to predict
20
these shifts is limited by a scarcity of baseline data on current distributions and a lack of
21
understanding of drivers of species ranges. To characterize the elevational distributions of a suite
22
of Afromontane birds, we conducted annual point counts between 2013 and 2018 at 297 plots
23
across habitats and elevations (2,416-4,303 m) in Volcanoes National Park, Rwanda. We
24
assessed elevational and habitat preferences via indicator analyses, and modeled abundance
25
distributions as a function of temperature, precipitation, habitat availability, and congeneric
26
competition. Although most of the 35 focal species were distributed across habitats and
elevational ranges, 20 species particularly associated with narrow elevational range (<300 m)
27
and 24 species were strongly associated with one to three habitat types. Abiotic factors
28
significantly correlated with abundance distributions of 33 species (temperature 33, precipitation
29
17), and biotic factors with distributions of 31 species (habitat 30, competition 7). Temperature
30
and habitat availability were particularly associated with upper limits (31 and 26 species
31
respectively vs. 10 and 6 at lower limits), whereas precipitation affected both limits similarly (17
32
lower, 16 upper), and competition had a limited role at either limit (4 lower, 3 upper). That the
33
elevational distribution of Afromontane birds results from a species-specific combination of
34
biotic and abiotic factors is crucial information in our effort to predict climate change effects in
35
this region.
36
37
38
39

37

38

39
40

41

42

43 **DATA AVAILABILITY STATEMENT**

44 The majority of data that supports the findings of this study are available in the supplementary material of this article. Additional raw data such as GPS coordinates of bird count locations are openly available in Dryad at http://doi.org/[doi], reference number [reference number].

47

48 **KEYWORDS**

49 Afromontane avifauna, Albertine Rift region, altitudinal distribution, elevational niche, habitat availability, range shifts, Rwanda

51

52

53

54

55

56

57

58

59

60

61

62
# 1. INTRODUCTION

Climate change may induce shifts in the distribution of biota across elevations, latitudes, and longitudes (Lenoir & Svenning, 2015). Among tropical species, which tend to have limited thermal tolerances (Laurance et al., 2011) and narrow ecological niches (Vázquez & Stevens, 2004), these distributional shifts are especially likely to occur in the form of elevational shifts given that elevational temperature gradients are stronger than latitudinal temperature gradients near the equator (Colwell et al., 2008). As a result, studies on elevational range shifts are becoming increasingly common across Afrotropical mountains, especially for birds (Dulle et al., 2016; Neate-Clegg et al., 2020; Neate-Clegg et al., 2021b). These studies show a general trend towards upslope shifts in species distribution, though the rate at which species have shifted varies considerably among regions and species; and up to a third of species have likely shifted their distribution downslope (Neate-Clegg et al., 2021b). These variations confirm that species distributions do not simply respond to changes in abiotic parameters and are likely to be influenced by a more complex set of abiotic and biotic drivers (HilleRisLambers et al., 2013). The relative importance of biotic and abiotic factors is known to vary across species and regions (Jankowski, et al., 2012; Elsen, et al., 2017). Despite the increasing number of studies on range shifts in Afrotropical mountains, we still have a limited understanding of the relative role of these factors in determining current elevational distributions in these regions, including for birds (Louthan et al., 2015; Mihoub et al., 2017; Rumpf et al., 2019).

Bird elevational distributions may be influenced by a host of factors (see review by Jankowski et al., 2013), of which three are particularly important: climate, habitat, and competition (Louthan et al., 2015; Elsen et al., 2017). First, they may be influenced by species climatic tolerance, i.e., the range of temperatures/precipitations for which basal metabolism can
be maintained, and by climatic preference, i.e., the temperature/precipitation for which species abundances peak (Carrascal et al., 2016; Sunday et al., 2012). They may also be influenced by habitat preferences, which are linked to the availability of resources such as food or potential nest sites. The availability of resources may itself be influenced by climatic factors such as temperature, precipitation, and evapotranspiration (e.g., Bhattarai & Vetaas, 2003; Hemp, 2006).

Indeed, birds are known to track temporary (e.g., interannual) variations in food availability related to short-term fluctuations in precipitation (Ferger et al., 2014; Jankowski et al., 2012; Neate-Clegg et al., 2020). As a result, climate may have both a direct effect on species elevational distribution, through climatic tolerance/preference, as well as an indirect effect, through habitat preference (HilleRisLambers et al., 2013; Jankowski et al., 2013; Louthan et al., 2015). And, finally, bird elevational distributions may be determined by biotic interactions such as competition—with predation and facilitation also playing a role for certain species (see review by Jankowski et al., 2013)—though evidence for this varies across regions. For example, a study in the Andes showed that elevational distributions of a vast majority of species were related to congeneric competition (Terborgh & Weske, 1975), but this was the case for less than 12% of species in a study in the Himalayas (Elsen et al., 2017).

Most studies on bird elevational distributions in Afrotropical mountains have focused on bird communities at relatively low elevations (~3000 m a.s.l.) or those that occur within a single habitat type (e.g., mid-elevation to montane forests; Dulle et al., 2016; Hanz et al., 2019; Hořák et al., 2019; Neate-Clegg et al., 2020, 2021b). The literature suggests that, at these lower elevations, bird species distribution are likely to be mainly driven by biotic interactions and habitat availability and only marginally influenced by abiotic factors (Ferger et al., 2014; Hanz et al., 2019; Hořák et al., 2019; Jankowski et al., 2009, 2013; Sam et al., 2019). Indeed, abiotic
factors may only become limiting for endotherms such as birds at high elevations (Jankowski et al., 2012; Spence & Tingley, 2020; Elsen et al., 2017). Unfortunately, studies conducted at lower elevations may have been unable to assess factors influencing the upper elevational limit of species, as this limit may occur beyond the upper elevational limits of the study area (Neate-Clegg et al., 2020). Similarly, these studies may have underestimated the role of biotic factors for species occurring outside of the studied forested habitat (Dulle et al., 2016). As a result, the fact that most studies on bird elevational distributions in Afrotropical mountains have focused on lower elevations and single habitat types may have limited our understanding of the respective role of biotic and abiotic factors in driving these distributions.

We aimed to disentangle the respective roles of climate, habitat availability and competition on bird elevational distributions using a unique dataset collected in Volcanoes National Park, Rwanda, in the Albertine Rift region. This dataset allowed us to study the distribution of 35 bird species across a wide elevational range (2,416-4,303 m) and across a wide range of forested and open habitat types. First, we characterized species elevational distributions and preferences, as well as specific habitat preferences. Second, we assessed the possible effects of precipitation, temperature, the availability of preferred habitat types, and the abundance of congenerics on bird species abundance following the correlational methods developed by Elsen et al. (2017). We expected biotic factors, habitat availability and competition, to be important at both lower and upper range limits whereas we expected abiotic factors, precipitation and temperature, to play a predominant role at upper range limits (Byrne, 2016; Elsen et al., 2017; Jankowski, et al., 2012; Neate-Clegg et al., 2018).

## 2. METHODS
## 2.1. Study area

We studied the avifauna of Volcanoes National Park (VNP), Rwanda (between 1°21′–1°35′S, 29°22′–29°44′E; Figure 1a). This protected area, located in the biologically diverse Albertine region (Plumptre et al., 2007), encompasses a variety of mountainous habitats across an elevational gradient (Plumptre, 1991; Figure 1b): (1) bamboo (Yushania alpina) and mixed bamboo forests (ca. 2,500–2,800 m); (2) mixed montane forests with an often dense and Mimulopsis-dominated understory (ca. 2,500–2,700 m); (3) Hagenia-Hypericum woodland characterized by Hypericum spp. and Hagenia abyssinica (ca. 2,800–3,300 m); (4) patches of herbaceous vegetation with little to no tree cover (ca. 2,800–3,300 m); (5) brush ridge comprised of Hypericum-woodland with sparse tree cover (ca. 3,000–3,300 m); (6) Alpine meadows (ca. 3,100–3,400 m); and (7) sub-Alpine zone with stands of giant lobelia (Lobelia stuhlmannii) and Rubus spp. (ca. 3,300–3,600 m). There is a rapid decrease in tree cover above 3000 m and the average treeline in VNP is considered to occur between 3600-3800 m, the limit for most Ericoideae trees and shrubs (Jacob, et al. 2015). Finally, there are (8) wetlands at varying elevations across VNP.

Volcanoes National Park experiences an increase in annual precipitation with elevation up to 3000-3600 m and a decrease beyond 3600 m (Figure 1d). It experiences a steady decrease in minimum temperature of the coldest month with elevation, with temperatures varying from >10°C at 2400 m to 0 °C above 4000 m (Figure 1d; data obtained for all of VNP at a 30 arc-second resolution from CHELSA; Karger et al. 2017a; Karger et al. 2017b; last modified April 2020).

## 2.2. Plot selection and bird surveys
We selected 30 transects covering all habitat types occurring within VNP (Figure 1b). Each transect was separated by at least 1,000 m. Between 4 and 18 plots were selected at 200-m intervals along each transect, resulting in a total of 297 plots. Plots were located between 2,416 and 4,303 m a.s.l. For each plot, we recorded its dominant habitat type based on field observations and derived the average annual precipitation and the minimum temperature of the coldest month from the aforementioned CHELSA data. For comparative reasons, and to understand the distribution of species outside VNP, we also counted birds at 50 plots located within ~3 km downslope from the park boundaries.

Two highly trained observers (FE and VM) conducted points counts twice a year between 2013 to 2018, visiting each plot one month apart during a relatively dry time of the year between June and September. They recorded all birds heard or seen within a 25-m radius for 10 minutes between 6:30 and 12:00. We adopted the taxonomy presented in the IOC World Bird List Version 8.2 (Gill & Donsker, 2018). We provided descriptive statistics for all recorded species (lowest and highest records and elevational range width measured as the difference between these extremes) but excluded birds with < 3 records per year from further analyses (following e.g., Neate-Clegg et al. 2020), since their elevational distribution was unlikely to be accurate due to low sample size. We also excluded all Accipitridae, Falconidae, Apodidae, and Hirundinidae, since point counts are not adapted for these species. Finally, we excluded Grauer's Swamp Warbler *Bradypterus graueri*, the only wetland specialist remaining after applying the previous filters, as the distribution of this species was restricted to three wetlands in VNP. The remaining subset of species (hereafter 'subset') showed no significant correlations between the width of elevational ranges (see 2.3) and sample size (Spearman rho 7370.3, *p* = 0.461), which may serve
as coarse indication that sample sizes for these species were sufficiently adequate—for example,
small ranges were not the result of a low sample bias—for further analyses.

## 2.3. Species elevational distributions and preferences

We characterized species elevational distribution by calculating minimum/maximum elevation, elevation range, and weighted mean elevation (mean elevation of species records weighted by the number of individuals recorded at each elevation) for each species across all years. For the minimum and maximum elevations, we used the 2.5% and 97.5% percentiles of the elevational distribution to reduce the influence of outliers (Neate-Clegg et al., 2018, 2021b), and the range was estimated as the difference between these two limits. Next, per species, we plotted the distribution of abundances along elevation using kernel density plots created with 'ggridges' in R (Wilke, 2021). Finally, we visualized the elevational ranges of the subset of species in a histogram.

We characterized species elevational preferences using indicator analyses (Leach et al., 2018). More specifically, we used the point biserial (pb) correlation index rpb—recommend for abundance data (De Cáceres & Legendre, 2009; De Cáceres et al., 2010)—produced by the 'multipatt' function in the R package 'indicspecies' (De Cáceres et al., 2016) to assess whether species were preferentially associated with specific elevations. Following De Cáceres & Legendre (2009) and De Cáceres et al. (2010), we used total abundance data gathered across all years for each plot. Subsequently, we used a permutation test (9999 permutations) to compare these test variables with the distribution of statistics obtained from randomly ordered data; see De Cáceres, Legendre, and Moretti (2010) for detailed explanations of these permutation tests. We used an alpha of 0.05 to determine significant associations. We considered whether species
200 were associated with narrow elevation bands of 300 m (e.g., 2400-2700 m), or with combinations
201 of elevational bands (e.g., 2400-2700 m and 2700-3000 m). We refer to such elevation bands as
202 ‘preferred elevations’.

203

204 **2.4. Species habitat preferences**

205 We determined whether species were preferentially associated with one specific habitat type or a
206 combination of habitat types. For this, we classified each plot as one of the seven habitats listed
207 in *Study area*, based on field observations made during bird point counts. As sub-Alpine and
208 Alpine vegetation types were difficult to distinguish in the field, we merged these two categories
209 for this analysis. To determine habitat preferences, we used the same correlation indices
210 specified above, but applied to the six habitat types instead of elevation bands. We refer to these
211 habitats as ‘indicator habitats’.

212

213 **2.4. Role of climate, habitat, and competition as drivers of species distributions**

214 We used generalized additive models (GAMs) to determine whether bird species abundance is
215 influenced by climate, habitat, or competition. We first created a set of models including all
216 predictor variables (temperature, precipitation, habitat cover, and abundance of congenerics) as
217 well as all possible interaction terms to check for potential concurvity—a non-linear form of
218 multicollinearity—and spatial autocorrelation. We found no significant concurvity nor
219 autocorrelation. Given the variable numbers of predictors associated with each driver (climate,
220 habitat, competition), we, therefore, chose to run three distinct sets of models for each driver,
221 with no autocorrelation term. Similar to Elsen et al. (2017), we chose simple and separate models
rather than more complex multi-effects models that include all variables, to allow for accurate estimations of coefficients (see also Cade, 2015).

For the climate model, we included average annual precipitation, the minimum temperature of the coldest month, and an interaction term as predictor variables. We included the interaction term to obtain more accurate coefficients for individual terms while accounting for dependent effects, but we refrain from further discussion of these interaction effects given we have no a priori hypotheses for interaction effects and the often multimodal and complex nature of these effects hinders straightforward interpretation.

For the habitat model, we included the absolute (m²) cover of preferred habitat types (15 m resolution data obtained with permission from Akayezu et al., 2019) across circular buffers around bird point count plots (hereafter ‘buffer plots’) as predictor variable. We first chose buffers with a radius of 100 m, as this is a known threshold in gap-crossing movements of tropical forest birds (Ibarra-Macias, 2011). However, as the territorial or home-range sizes of many bird species may be much larger than 100 m, we also created these habitat models with the area (m²) of preferred habitat as measured across 500 m circular buffers. Little is known about the movement ecology or home-range sizes of most of our focal species, but we chose 500 m for these buffers given that this distance is on par with the maximum home-range sizes of other Afrotropical species (see e.g., Habel et al., 2018). When available for a species, we included all preferred habitat types as predictor variables. For species that lacked indicator habitats, we used a more general classification of habitat preference (forest or open habitat) based on the Handbook of the Birds of the World (Del Hoyo et al., 2016; assessed in February 2019), and accordingly used the total amount of forest or open habitat as predictor variables.
Finally, for the competition model, we included the abundance of each congeneric
species as a predictor variable. We included the abundance of only one congeneric per model,
which implied that we created multiple models for species that co-occur in the study area with
multiple congenerics.

We created models with a zero-inflated Poisson (or binomial family structure when
records were of single individuals), using the 'mgcv' package in R (Wood, 2016). We used
smooth functions for significant variables using mgcv's *plot.gam* function to visualize the shape
of the relationship. We subsequently compared these shapes to the elevational distribution of the
relevant predictor variables (e.g., those plotted in Figure 1c, d) to determine whether variables
were likely to play a role in determining lower or upper limits.

254

## 3. RESULTS

We recorded 132 species in VNP, of which we retained 35 for further statistical analyses
(highlighted as 'subset' in Table S1).

### 3.1. Species elevational distributions

Kernel density plots show the elevational distributions of bird species and provide visual
evidence of substantial interspecific variation in the elevational distribution of species in VNP
(Figure 2). Across all species in the subset, the range of elevations varied substantially between
species (Figure 3), from ~556 m for Yellow-whiskered Greenbul *Eurillas latirostris* to ~1605 m
for Scarlet-tufted Sunbird *Nectaringia johnstoni* (Table S1).

Twenty-seven species showed a significant (at *p* < 0.05) preference for one elevation
band (20 species) and/or a combination of elevation bands (21 species; Table 1, Table S1). All
but three species—White-necked Raven *Corvus albicollis*, Cape Robin-Chat *Cossypha caffra*,
267

and Scarlet-tufted Sunbird—were significantly associated with elevation bands that span the forest-dominated parts of the study area, below the treeline at ~3600 m.

268

269

270

## 3.2 Species habitat preferences

271

We found that 24 species showed a significant preference for one habitat type (10) and/or a combination of a maximum of three (14) habitat types ($p < 0.05$; Table S1). Of these, many species were associated with forested habitats (4 species with bamboo, 12 with mixed forest, 6 with Hagenia-Hypericum) whereas fewer species were associated with open habitats dominated by herbaceous (7), meadow (4), brush ridge (6), and (sub) Alpine vegetation (3). Two species were associated with wetlands and at least one other habitat type.

272

273

274

275

276

277

278

## 3.2. Role of climate, habitat, and competition as drivers of species distributions

279

For all 35 species in the subset, we detected a significant negative effect of at least one of the four drivers at range limits, consistent with our hypotheses of range limitations (Table S2, Table S3, Figure 4, Figure 5). Abiotic climatic factors were significant predictors of range limits of 33 species (temperature for 33 species and precipitation for 17 species), and biotic factors of 31 species (habitat at 100 m radius for 26 species, habitat at 500 m radius for 30 species, and competition for 7 species). Habitat availability as measured across 100 m-radius circular buffers was a comparably similar, albeit slightly weaker, predictor of species limits as habitat availability measured across 500 m-radius buffers.

280

281

282

283

284

285

286

287

We found substantial differences between the relative importance of factors at lower and upper limits. Overall, there was a higher number of species with at least one significant predictor consistent with a constraint at upper limits (32 species) than species with significant predictors

288

289
consistent with a constraint at lower limits (24). There were also differences in the relative
importance of the four drivers at lower and upper limits, with precipitation being the most
frequent significant predictor of lower limits (for 17 species) and temperature the main predictor
of upper limits (for 31 species; Figure 4).

The spatial distribution of birds in Volcanoes National Park (VNP) is characterized by distinct elevational zonation. Although elevational ranges were broader than those of birds in some other tropical montane regions (e.g., average range widths of ~200 m in the northern Andes, Ocampo-Peñuela, & Pimm, 2015), they were consistent with ranges observed for birds in montane regions (White & Bennett, 2015). Moreover, results from indicator analyses hint at preferences for specific elevational ranges for most species, in agreement with the hypothesis that tropical species have narrow thermal tolerance (Terborgh & Weske, 1975). Our results suggest that these elevational distributions are likely to be influenced by at least one of the four following drivers: temperature, precipitation, habitat availability, and competition; this was the case for all 35 focal bird species in this study. Yet, though different drivers were likely to be limiting at lower and upper range limits, we did not find support for the broad hypothesis that biotic factors are limiting at both lower and upper elevations whereas abiotic factors are more limiting at upper limits. Instead, we found more complexity, in that 1) habitat availability and temperature were most limiting at upper limits, 2) precipitation was most limiting at lower limits, and 3) competition was limiting for only a few species at either range limit.

Many species showed a decline in abundance with a decrease in temperature at upper
limits. This result is consistent with some literature highlighting that temperature may influence
the upper limits of distributions of tropical species (Ghalambor et al., 2006; Janzen, 1967), but
314 contrasts with other studies (e.g., Neate-Clegg et al., 2020). Contrasts with other studies may
315 partially be explained by the fact that our study area included a much higher elevation range
316 (>1000 m) than those included in many other studies of tropical avifauna (e.g., Campos-
317 Cerqueira et al., 2017; Forero-Medina et al., 2011; Neate-Clegg et al., 2018; Neate-Clegg et al.,
318 2020). And only at such high elevations may we find the low temperatures that can induce
319 thermoregulatory costs (i.e., the energetic requirements to maintain body temperatures within
320 critical limits) that affect survival or behavior (Londoño et al., 2017).

321 However, and more likely, declines in abundance at certain temperatures or levels of
322 precipitation may also stem from indirect effects of climate on habitat quality or food availability
323 within habitat types (Ferger et al., 2014; Jankowski et al., 2012; Santillán, et al., 2018). For
324 example, avian communities are known to be shaped by habitat structure, which is, in turn,
325 correlated with elevation and associated environmental factors (Hořák et al., 2019). This would
326 also explain the possible role that precipitation could play in defining both upper (too much
327 precipitation) and lower (too little precipitation) limits for some species. We found that
328 precipitation was a particularly important predictor of lower range limits in our study area,
329 though we note that this stems partially from a decline in precipitation towards lower elevations
330 that may be particular to our study area and the studied southern slopes of the Virunga Massif.
331 Yet, though the role of precipitation may be site-specific, largely indirect and mediated via
332 habitat and resource availability (Neate-Clegg et al., 2020), and can be challenging to interpret
333 due to the largely non-linear relationship of precipitation with elevation (see Figure 1c), it is
334 clear that precipitation is a factor that needs to be included in studies that aim to track species
335 range distributions or that aim to model future suitable habitat under climate scenarios—
especially as the effects of precipitation and temperature are often interacting and future shifts in both factors as a result of climate change may be discordant (McCain & Colwell, 2011).

By encompassing a wide range of elevation and habitat types, our study confirmed that habitat availability is also likely to be a significant driver of species distributions (Elsen et al., 2017). It confirms that habitat availability may be limiting for many species at upper limits, in particular for forest-associated species which cannot occur above the treeline, as was previously shown for temperate montane birds (Altamirano et al., 2020). Indeed, most of the species in our study with habitat as a potential driver of upper limits were forest associated and limited by the decline in forest availability at or near the treeline ecotone. And of the few (5) species with a possible effect of habitat at lower limits, four may be considered generalist species that favor largely or partially open habitats (Cape Robin-Chat, Scarlet-tufted Sunbird, White-necked Raven, Chubb's Cisticola *Cisticola chubbi*; the first three are also associated with open habitat at relatively high elevations above the treeline).

The overall slightly weaker effect of habitat as a predictor as compared to temperature (this study and Elsen et al., 2017) could be due to the rather general habitat preferences of many species in our study (only a third of species had one indicator habitat) or specific characteristics of our study area such as the presence of tall vegetation (e.g., Ericaceae, Lobelia) above elevations where tree cover becomes scarce. Moreover, that we did not detect a strong effect of habitat availability at lower elevations may be because the lower limits of our study area are found at relatively high elevations well above the lower limits of species' distributions of especially forest associated species. For some of the species included in the present study, declines in abundances as a result of declines in habitat availability may only become apparent at lower elevations, outside VNP.
Finally, our findings contrast with earlier suggestions that congeneric competition is an important driver of elevational zonation among tropical birds (Terborgh & Weske, 1975; Freeman et al., 2019) and instead align with studies that found only scarce evidence for congeneric competition among montane bird communities (Barve & Dhondt, 2017; Dehn & Christiansen, 2001; Elsen et al., 2017; Jankoswki et al. 2010). We found that all congeneric species overlapped to some extent in their elevational ranges (see ranges in Table S1) and it is likely that other forms of niche partitioning (e.g., regarding diets or the location of reproductive sites) account for these levels of sympatry (Elsen et al., 2017). Yet, there were some species for which congeneric competition could play a role, notably warblers of the genus *Phylloscopus*, a finding in line with e.g., Elsen et al. (2017). And we acknowledge that there could be other forms of biotic interactions not considered in this study that play a role in shaping elevational distributions, such as habitat avoidance, facilitation, or predation (Jankowski et al., 2012). For example, birds that nest or roost in tree cavities could be limited in their distribution by nest substrates, and preliminary studies in VNP show that cavity density drops rapidly above 3300-3400 m. These elevations coincide with the upper elevational limit for cavity-nesting species such as Olive Woodpecker *Dendropicos griseocephalus*.

Our analyses provide baseline data on elevational distributions of Afromontane birds (similar to e.g., Kittelberger et al., 2021) that hint at elevational zonation driven by a complex and species-specific combination of biotic and abiotic factors. We note that we looked at changes in abundances within a predefined elevational range and that the elevational distribution of some species extends below the park boundaries. As such, we caution that the estimated range limits only apply within the context of VNP, and that most of our focal species are also found outside the park at lower elevations (see Table S1). Our approach was also correlative, which does not
382 imply that drivers significantly associated with declines in species' abundances were also causal
383 to elevational limits. Many confounding, interacting, and partially multicollinear effects could
384 also play a role in shaping observed correlations. For example, although a test for concurvity
385 showed no significant correlation between the four variables tested in our separate models, they
386 may have

387 For example, habitat availability seemed a limiting factor at upper limits for the Stripe-
388 breasted Tit *Melaniparus fasciiventer* but this could also be linked to a more specific resource
389 such as the availability of the tree cavities for nest sites. These tree cavities are, in turn, known to
390 be products of interactions between temperature, precipitation, habitat type, and the presence of
391 certain keystone species such as the also mentioned Olive Woodpecker (Remm & Lõhmus,
392 2011).

393 Despite these caveats of our correlative methods, it is apparent that temperature seems
394 particularly important in determining elevational distributions in our study area, though we also
395 found precipitation and habitat availability to be potential predictors of elevational distributions
396 of many species. This is important when considering possible effects of future climate change,
397 which in our study area may come in the form of increased temperatures, increased variability in
398 annual precipitation patterns (IPCC, 2013a,b; Ngoma et al., 2021), and upslope shifts in
399 vegetation (Ponce-Reyes et al., 2017; Ayebare et al., 2018; Bagchi et al., 2018). Indeed, our
400 study confirms the need to include variables other than temperature, precipitation in particular, in
401 projections and explanations of shifts of animal distributions (Tingley et al., 2012; Neate-Clegg
402 et al., 2020). Future changes in precipitation and temperature may have contrasting effects on
403 animal distributions and may further interact in the way they impact factors such as food
404 resource availability.
Next steps would be to consider interactions between drivers of elevational distributions and to monitor temporal and spatial shifts in elevational distributions, for which we need to commit to long-term monitoring. Only with such long-term data can we effectively guide our efforts to forecast and mitigate climate change effects on Afromontane bird species. Meanwhile, we may look at management strategies available to mitigate possible negative climate change effects on birds in our study area. Such strategies do little to counter effects at high elevations, but we can look at ways to provide additional habitat to birds at elevations below current park boundaries. After all, about a third of all species recorded in our study were found up to the lowest edges of the park (Table S1), and abiotic conditions do not seem to be limiting the lower tails of distributions of most species. Thus, downslope addition of habitat may provide the opportunity for downslope range shifts of certain species and may overall provide more space for bird populations, especially of forest-associated species, which could provide some buffering from negative climate change effects.

## ACKNOWLEDGMENTS

We thank the Rwanda Development Board for granting us access to conduct fieldwork in Volcanoes National Park and field staff for aiding our data collection. We thank two anonymous reviewers for constructive comments on earlier versions of this article.

## AUTHOR CONTRIBUTIONS

EF and VM performed the majority of the fieldwork with contributions by YVDH and DT; YVDH analyzed the data, YVDH and CS wrote the manuscript, and all authors commented on
427 the final draft and approved the final manuscript. We thank two anonymous reviewers for their

428 comments on an earlier version of this manuscript.

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465
466
467
468

<table><caption>Table 1. The number of species (out of 35) significantly ($p < 0.05$) associated with a particular narrow (300 m) or broader (>300 m) elevational band.</caption><thead><tr><th>Elevation (m)</th><th>Total</th></tr></thead><tbody><tr><td>2400-2700</td><td>7</td></tr><tr><td>2700-3000</td><td>3</td></tr><tr><td>3000-3300</td><td>6</td></tr><tr><td>3300-3600</td><td>1</td></tr><tr><td>3600-3900</td><td>3</td></tr><tr><td>Total</td><td>20</td></tr><tr><td>2400-3000</td><td>6</td></tr><tr><td>2400-3300</td><td>5</td></tr><tr><td>2400-3600</td><td>3</td></tr><tr><td>2700-3300</td><td>4</td></tr><tr><td>3600-4200</td><td>2</td></tr><tr><td>Total</td><td>21</td></tr></tbody></table>

469
470 **Figure legends.**

471 **Figure 1.** Study area and bird point count locations relative to elevation, vegetation cover, mean annual temperature. A. Map of location Volcanoes National Park (VNP) in northern Rwanda. B. Map of vegetation cover of VNP and the location of bird point count plots. C. Loess plot of the distribution of open habitat and forest habitat along elevation (Grey shaded area = ± SE). The vertical yellow column shows the approximate location of the treeline at 3600-3800 m. D. Graphs showing the annual precipitation (mm) and mean temperatures (°C) across elevations; dots correspond to values at bird point count plots and the line and grey shaded area represent a visualization of a default loess regression (span = 0.75) ± SE. Loess regressions in panels C and D are conducted using the geom.smooth() command in R's 'ggplot2' package (Wickham, 2006).

480
481 **Figure 2.** Kernel density distributions of 35 bird species across elevation in Volcanoes National Park, Rwanda.
482

483 **Figure 3.** Elevational range widths of 35 bird species recorded in Volcanoes National Park, Rwanda. Elevational range widths are the difference between the 2.5% and the 97.5% percentiles of the elevational distribution, e.g., a width of 300 m indicates that a species was recorded across a ~300 m elevational range. The sample size is given in parentheses behind the species name. Vertical shading refers to: green, the approximate location of a forest-dominated zone (<3000 m a.s.l.); blue, the transition zone from forest to open habitat (~3000-3600 m); yellow, the treeline ecotone (3600-3800 m); gray, the open Alpine zone (> 3800 m). s

490 **Figure 4.** Number of species whose Generalized Additive Models showed a significant negative association of abundance with temperature, precipitation, habitat, or competition at elevational limits. The sum of these numbers do not equal the total number of bird species (n = 35) considered for these analyses as many species had multiple significant predictors of abundance. For the bar related to habitat, we used the habitat availability as measured across 500 m-radius circular buffers around bird point count plots.

491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508

**Figure 5.** Examples of plots of component smooth functions that make up generalized additive models. These effects are significant when the shaded area (95% confidence interval) does not overlap with zero. For Red-faced Woodland Warbler *Phylloscopus laetus*, this implies a negative trend in abundances at low temperatures, corresponding to upper elevational limits; for Rwenzori Hill Babbler *Pseudoalcippe atriceps* there are negative trends at low amounts of precipitation, which for this species correspond to both its lower and upper elevational limits; for White-starred Robin *Pogonocichla stellata* there is a negative effect on abundance at low amounts of bamboo—here we plot abundance against bamboo availability in a 100 m-radius circular buffer—, a scenario which occurs at its upper elevational limits; and high abundances of the congeneric Brown Woodland Warbler *Phylloscopus umbrovirens* pose a possible constraint on the abundances of the Red-faced Woodland Warbler at the latter species' upper limits.
<figure><img src="image_1.png" /></figure>

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>Figure 1.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 2.</figcaption></figure>

510

511

512

513

514

515
<figure><img src="image_6.png" /><figcaption>Figure 3.</figcaption></figure>

516

517

518

519

520

521

522

523

524
<figure><img src="image_7.png" /><figcaption>Figure 4.</figcaption></figure>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>Figure 5.</figcaption></figure>

537

538

539

540

541

542

543

544

545
546 REFERENCES

547 Akayezu, P., van Duren, I. C., Groen, T. A., Grueter, C. C., & Robbins, M. M. (2019).

548 Abundance and spatial distribution of the main food species for mountain gorillas in the

549 Virunga Massif, Rwanda. *Biodiversity and Conservation*, 28(13), 3597-3620.

550 Altamirano, T. A., de Zwaan, D. R., Ibarra, J. T., Wilson, S., & Martin, K. (2020). Treeline
551 ecotones shape the distribution of avian species richness and functional diversity in south
552 temperate mountains. *Scientific Reports*, 10(1), 1-13.

553 Aragón, P., Lobo, J. M., Olalla-Tárraga, M. Á., & Rodríguez, M. Á. (2010). The contribution of
554 contemporary climate to ectothermic and endothermic vertebrate distributions in a glacial
555 refuge. *Global Ecology and Biogeography*, 19(1), 40-49.

556 Ayebare, S., Plumptre, A. J., Kujirakwinja, D., & Segan, D. (2018). Conservation of the endemic
557 species of the Albertine Rift under future climate change. *Biological Conservation*, 220,
558 67-75.

559 Bagchi, R., Hole, D.G., Butchart, S.H., Collingham, Y.C., Fishpool, L.D., Plumptre, A.J.,
560 Owiunji, I., Mugabe, H. & Willis, S.G., (2018). Forecasting potential routes for
561 movement of endemic birds among important sites for biodiversity in the Albertine Rift
562 under projected climate change. *Ecography*, 41(2), pp.401-413.

563 Barve, S., & Dhondt, A. A. (2017). Elevational replacement of two Himalayan titmice: interspecific competition or habitat preference? *Journal of Avian Biology*, 48(9), 1189-1194.

566 Bhattarai, K.R., & Vetaas, O.R. (2003). Variation in plant species richness of different life forms
567 along a subtropical elevation gradient in the Himalayas, east Nepal. *Global Ecology and
568 Biogeography*, 12(4), pp.327-340.
569 Birdlife International (2020). Bird life data zone. Available at:
570 http://datazone.birdlife.org/species/search [Accessed December 1, 2020].
571 Byrne, J. G. D. (2016). *The importance of biotic interactions and climate change on avifaunal range limits of the Albertine rift*. Masters thesis. University of York.
572
573 Cade, B. S. 2015. Model averaging and muddled multimodel inferences. *Ecology*, *96*, 2370–2382.
574
575 Campos-Cerqueira, M., Arendt, W. J., Wunderle Jr, J. M., & Aide, T. M. (2017). Have bird distributions shifted along an elevational gradient on a tropical mountain? *Ecology and Evolution*, *7*(23), 9914-9924.
576
577
578 Carrascal, L. M., Villén-Pérez, S., & Palomino, D. (2016). Preferred temperature and thermal breadth of birds wintering in peninsular Spain: the limited effect of temperature on species distribution. *PeerJ*, *4*, e2156-e2156. doi: 10.7717/peerj.2156
579
580
581 Chamberlain, D. E., Negro, M., Caprio, E., & Rolando, A. (2013). Assessing the sensitivity of alpine birds to potential future changes in habitat and climate to inform management strategies. *Biological Conservation*, *167*, 127-135.
582
583
584 Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C., & Longino, J. T. (2008). Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. *Science*,
585
586 322(5899), 258-261.
587 De Cáceres, M., Jansen, F., & De Cáceres, M. M. (2016). Package ‘indicspecies’. Relationship between species and groups of sites. *R package version 1*(6).
588
589 De Cáceres, M. D., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. *Ecology*, *90*(12), 3566-3574.
590
591 De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. *Oikos*, **119**(10), 1674-1684.

593 Dehling, D. M., Fritz, S. A., Töpfer, T., Päckert, M., Estler, P., Böhning-Gaese, K., & Schleuning, M. (2014). Functional and phylogenetic diversity and assemblage structure of frugivorous birds along an elevational gradient in the tropical Andes. *Ecography*, **37**(11), 1047-1055.

597 Dehn, M., & Christiansen L. (2001). Altitudinal distributions of congeneric montane forest bird species along an elevational gradient in the Rwenzori Mountains National Park, western Uganda. *Scopus*, **22**, 29-35.

600 Del Hoyo, J., Elliott, A., Sargatal, J., Christie, D., & de Juana, E. (2016). Handbook of the birds of the world alive. Barcelona, Spain: Lynx Edicions.

602 Derhé, M. A., Tuyisingize, D. E. O., Eckardt, W., Emmanuel, F., & Stoinski, T. (2019). Status, diversity and trends of the bird communities in Volcanoes National Park and surrounds, Rwanda. *Bird Conservation International*, 1-20. doi: 10.1017/S0959270919000121

605 Dulle, H. I., Ferger, S. W., Cordeiro, N. J., Howell, K. M., Schleuning, M., Böhning-Gaese, K., & Hof, C. (2016). Changes in abundances of forest understorey birds on Africa's highest mountain suggest subtle effects of climate change. *Diversity and Distributions*, **22**(3), 288-299.

609 Elsen, P. R., Tingley, M. W., Kalyanaraman, R., Ramesh, K., & Wilcove, D. S. (2017). The role of competition, ecotones, and temperature in the elevational distribution of Himalayan birds. *Ecology*, **98**(2), 337-348.

612 Ferenc, M., Fjeldså, J., Sedláček, O., Motombi, F. N., Nana, E. D., Mudrová, K., & Hořák, D. (2016). Abundance-area relationships in bird assemblages along an Afrotropical
614 elevational gradient: space limitation in montane forest selects for higher population densities. *Oecologia*, 181(1), 225-233.

616 Ferger, S. W., Schleuning, M., Hemp, A., Howell, K. M., & Böhning-Gaese, K. (2014). Food resources and vegetation structure mediate climatic effects on species richness of birds. *Global Ecology and Biogeography*, 23(5), 541-549.

619 Forero-Medina, G., Terborgh, J., Socolar, S. J., & Pimm, S. L. (2011). Elevational ranges of birds on a tropical montane gradient lag behind warming temperatures. *PLoS One*, 6(12), e28535.

622 Freeman, B. G. (2016). Thermal tolerances to cold do not predict upper elevational limits in New Guinean montane birds. *Diversity and Distributions*, 22, 309-317.

624 Freeman, B. G., & Beehler, B. M. (2018). Limited support for the “abundant centre” hypothesis in birds along a tropical elevational gradient: implications for the fate of lowland tropical species in a warmer future. *Journal of Biogeography*, 45(8), 1884-1895.

627 Freeman, B. G., & Freeman, A. M. C. (2014). Rapid upslope shifts in New Guinean birds illustrate strong distributional responses of tropical montane species to global warming. *Proceedings of the National Academy of Sciences*, 111, 4490-4494.

630 Freeman, B. G., Tobias, J. A., & Schlüter, D. (2019). Behavior influences range limits and patterns of coexistence across an elevational gradient in tropical birds. *Ecography*, 42(11), 1832-1840.

633 Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J., & Wang, G. (2006). Are mountain passes higher in the tropics? Janzen's hypothesis revisited. *Integrative and comparative biology*, 46(1), 5-17.

636 Gill, F., & Donsker, D. (2018). IOC World Bird List (v 8.2). doi: 10.14344/IOC. ML. 8.2.
637 Habel, J. C., Tobias, J. A., & Fischer, C. (2019). Movement ecology of Afrotropical birds: Functional traits provide complementary insights to species identity. *Biotropica*, 51(6), 894-902.

640 Hanz, D. M., Böhning-Gaese, K., Ferger, S. W., Fritz, S. A., Neuschulz, E. L., Quitián, M., Santillán, V., Töpfer, T. and Schleuning, M. (2019). Functional and phylogenetic diversity of bird assemblages are filtered by different biotic factors on tropical mountains. *Journal of Biogeography*, 46(2), 291-303.

644 Hemp, A. (2006). Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro. *Plant Ecology*, 184(1), 27-42.

646 HilleRisLambers, J., Harsch, M. A., Ettinger, A. K., Ford, K. R., & Theobald, E. J. (2013). How will biotic interactions influence climate change-induced range shifts? *Annals of the New York Academy of Sciences*, 1297(1), 112-125.

649 Hořák, D., Ferenc, M., Sedláček, O., Motombi, F. N., Svoboda, M., Altman, J., Albrecht, T., Djomo Nana, E., Janeček, Š., Dančák, M. & Majeský, Ľ. (2019). Forest structure determines spatial changes in avian communities along an elevational gradient in tropical Africa. *Journal of Biogeography*, 46(11), 2466-2478.

653 Ibarra-Macias, A., Robinson, W.D. & Gaines, M.S., (2011). Experimental evaluation of bird movements in a fragmented Neotropical landscape. *Biological Conservation*, 144(2), 703-712.

656 Illán, J. G., Thomas, C. D., Jones, J. A., Wong, W. K., Shirley, S. M., & Betts, M. G. (2014). Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds. *Global Change Biology*, 20(11), 3351-3364.
659 IPCC (2013a). Summary for policymakers. In: Stocker TF et al (eds) Climate change 2013: the
660 physical science basis. Contribution of Working Group I to the Fifth Assessment Report
661 of the Intergovernmental Panel on Climate Change. Cambridge University Press,
662 Cambridge and New York, 1-30.

663 IPCC (2013b). Annex I: atlas of global and regional climate projections. In: Stocker TF et al
664 (eds) Climate change 2013: the physical science basis. Contribution of Working Group I
665 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
666 Cambridge University Press, Cambridge and New York, 1311-1394.

667 Jankowski, J. E., Ciecka, A. L., Meyer, N. Y., & Rabenold, K. N. (2009). Beta diversity along
668 environmental gradients: implications of habitat specialization in tropical montane
669 landscapes. *Journal of Animal Ecology*, 78(2), 315-327.

670 Jankowski, J. E., Londoño, G. A., Robinson, S. K., & Chappell, M. A. (2012). Exploring the role
671 of physiology and biotic interactions in determining elevational ranges of tropical
672 animals. *Ecography*, 36(1), 1-12.

673 Jankowski, J. E., Merkord, C. L., Rios, W. F., Cabrera, K. G., Revilla, N. S., & Silman, M. R.
674 (2013). The relationship of tropical bird communities to tree species composition and
675 vegetation structure along an Andean elevational gradient. *Journal of Biogeography*,
676 40(5), 950-962.

677 Jankowski, J. E., Robinson, S. K., & Levey, D. J. (2010). Squeezed at the top: interspecific
678 aggression may constrain elevational ranges in tropical birds. *Ecology*, 91(7), 1877-1884.

679 Janzen, D. H. (1967). Why mountain passes are higher in the tropics. *The American Naturalist*,
680 101(919), 233-249.
681 Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017a) Climatologies at high resolution for the earth's land surface areas. *Scientific Data* 4, 170122.

684 Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N.E., Linder, H.P., Kessler, M. (2017b) Data from: Climatologies at high resolution for the earth's land surface areas. Dryad Digital Repository, https://doi.org/10.5061/dryad.kd1d4

688 Kittelberger, K. D., Neate-Clegg, M. H., Buechley, E. R., & Hakkı Şekercioğlu, Ç. (2021). Community characteristics of forest understory birds along an elevational gradient in the Horn of Africa: A multi-year baseline. Ornithological Applications, 123(2), duab009.

691 Laurance, W. F., Useche, D. C., Shoo, L. P., Herzog, S. K., Kessler, M., Escobar, F., Brehm, G., Axmacher, J. C., Chen, I. C., Gámez, L. A., & Hietz, P. (2011). Global warming, elevational ranges and the vulnerability of tropical biota. *Biological Conservation*, 144(1), 548-557.

695 Leach, E. C., Burwell, C. J., Jones, D. N., & Kitching, R. L. (2018). Identifying avian indicators of elevation in the Gondwanan rainforests of Australia. *Pacific Conservation Biology, 25*, 143-150.

698 Lenoir, J., Gégout, J. C., Guisan, A., Vittoz, P., Wohlgemuth, T., Zimmermann, N. E., Dullinger, S., Pauli, H., Willner, W., & Svenning, J. C. (2010). Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. *Ecography*, 332, 295-303.

702 Lenoir, J., & Svenning, J. C. (2015). Climate-related range shifts–a global multidimensional synthesis and new research directions. *Ecography*, 38(1), 15-28.
704 Londoño, G. A., Chappell, M. A., Jankowski, J. E., & Robinson, S. K. (2017). Do

705 thermoregulatory costs limit altitude distributions of Andean forest birds? *Functional

706 *ecology*, 31(1), 204-215.

707 Louthan, A. M., Doak, D. F., & Angert, A. L. (2015). Where and when do species interactions

708 set range limits? *Trends in ecology & evolution*, 30(12), 780-792.

709 McCain, C.M., & Colwell, R.K. (2011). Assessing the threat to montane biodiversity from

710 discordant shifts in temperature and precipitation in a changing climate. *Ecology letters*,

711 *14*(12), 236-1245.

712 Mihoub, J.-B., Henle, K., Titeux, N., Brotons, L., Brummitt, N. A., & Schmeller, D. S. (2017).

713 Setting temporal baselines for biodiversity: the limits of available monitoring data for

714 capturing the full impact of anthropogenic pressures. *Scientific Reports*, 7, 41591.

715 Neate-Clegg, M. H., Jones, S. E., Burdekin, O., Jocque, M., & Şekercioğlu, Ç. H. (2018).

716 Elevational changes in the avian community of a Mesoamerican cloud forest park.

717 *Biotropica*, 50(5), 805-815.

718 Neate-Clegg, M. H., O'Brien, T. G., Mulindahabi, F., & Şekercioğlu, Ç. H. (2020). A disconnect

719 between upslope shifts and climate change in an Afrotropical bird community.

720 *Conservation Science and Practice*, e291.

721 Neate-Clegg, M. H., Jones, S. E., Tobias, J. A., Newmark, W. D., & Şekercioğlu, Ç. H. (2021a).

722 Ecological correlates of elevational range shifts in tropical birds. *Frontiers in Ecology*

723 *and Evolution*, 9, 215.

724 Neate-Clegg, M. H., Stuart, S. N., Mtui, D., Şekercioğlu, Ç. H., & Newmark, W. D. (2021b).

725 Afrotropical montane birds experience upslope shifts and range contractions along a
726 fragmented elevational gradient in response to global warming. *Plos one*, 16(3),
727 e0248712.

728 Ngoma, H., Wen, W., Ojara, M., & Ayugi, B. (2021). Assessing current and future
729 spatiotemporal precipitation variability and trends over Uganda, East Africa, based on
730 CHIRPS and regional climate model datasets. *Meteorology and Atmospheric Physics*, 1-
731 21.

732 Ocampo-Peñuela, N., & Pimm, S. L. (2015). Elevational ranges of montane birds and
733 deforestation in the Western Andes of Colombia. *PloS one*, 10(12), e0143311.

734 Ponce-Reyes, R., Plumptre, A. J., Segan, D., Ayebare, S., Fuller, R. A., Possingham, H. P., &
735 Watson, J. E. (2017). Forecasting ecosystem responses to climate change across Africa's
736 Albertine Rift. *Biological Conservation*, 209, 464-472.

737 Plumptre, A. J. (1991). *Plant-herbivore dynamics in the Birungas*. PhD dissertation. University
738 of Bristol.

739 Plumptre, A. J., Davenport, T. R., Behangana, M., Kityo, R., Eilu, G., Ssegawa, P., Ewango, C.,
740 Meirte, D., Kahindo, C., Herremans, M., & Peterhans, J. K. (2007). The biodiversity of
741 the Albertine Rift. *Biological Conservation*, 1342, 178-194.

742 Romdal, T. S., & Rahbek, C. (2009). Elevational zonation of afrotropical forest bird
743 communities along a homogeneous forest gradient. *Journal of Biogeography*, 36(2), 327-
744 336.

745 Rumpf, S. B., Hülber, K., Zimmermann, N. E., & Dullinger, S. (2019). Elevational rear edges
746 shifted at least as much as leading edges over the last century. *Global Ecology and
747 Biogeography*, 28(4), 533-543.
748 Sagarin, R. D., & Gaines, S. D. (2002). The “abundant centre” distribution: To what extent is it a
749 biogeographic rule? *Ecology Letters*, 5, 137–147.

750 Santillán, V., Quitián, M., Tinoco, B. A., Zárate, E., Schleuning, M., Böhning-Gaese, K., &
751 Neuschulz, E. L. (2018). Spatio-temporal variation in bird assemblages is associated with
752 fluctuations in temperature and precipitation along a tropical elevational gradient. *PloS
753 one*, 13(5), e0196179.

754 Santillán, V., Quitián, M., Tinoco, B. A., Zárate, E., Schleuning, M., Böhning-Gaese, K., &
755 Neuschulz, E. L. (2020). Direct and indirect effects of elevation, climate and vegetation
756 structure on bird communities on a tropical mountain. *Acta Oecologica*, 102, 103500.

757 Schumm, M., White, A. E., Supriya, K., & Price, T. D. (2020). Ecological limits as the driver of
758 bird species richness patterns along the east Himalayan elevational gradient. *The
759 American Naturalist*, 195(5), 802-817.

760 Seoane, J., Bustamante, J., & Díaz-Delgado, R. (2004). Competing roles for landscape,
761 vegetation, topography and climate in predictive models of bird distribution. *Ecological
762 Modelling*, 171(3), 209-222.

763 Seoane, J., & Carrascal, L. M. (2008). Interspecific differences in population trends of Spanish
764 birds are related to habitat and climatic preferences. *Global Ecology and Biogeography*,
765 17(1), 111-121.

766 Spence, A. R., & Tingley, M. W. (2020). The challenge of novel abiotic conditions for species
767 undergoing climate-induced range shifts. *Ecography*, 43(11), 1571-1590.

768 Srinivasan, U., Elsen, P. R., Tingley, M. W., & Wilcove, D. S. (2018). Temperature and
769 competition interact to structure Himalayan bird communities. *Proceedings of the Royal
770 Society B: Biological Sciences*, 285(1874), 20172593.
771 Sunday, J. M., Bates, A. E., & Dulvy, N. K. (2012). Thermal tolerance and the global

772 redistribution of animals. *Nature Climate Change*, 2(9), 686.

773 Terborgh, J., & Weske, J. S. (1975). The role of competition in the distribution of Andean birds.

774 *Ecology* 56:562–576.

775 Tingley, M. W., Koo, M. S., Moritz, C., Rush, A. C., & Beissinger, S. R. (2012). The push and

776 pull of climate change causes heterogeneous shifts in avian elevational ranges. *Global*

777 *Change Biology*, 18(11), 3279-3290.

778 Vázquez, D. P., & Stevens, R. D. (2004). The latitudinal gradient in niche breadth: concepts and

779 evidence. *The American Naturalist*, 164(1), E1-E19.

780 White, R. L., & Bennett, P. M. (2015). Elevational distribution and extinction risk in birds. *PloS*

781 *one*, 10(4), e0121849.

782 Wickham. H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York.

783 Wilke, C. O. (2021). ggridges: Ridgeline Plots in 'ggplot2'. R package version 0.5.3.

784 https://CRAN.R-project.org/package=ggridges

785 Wood, S. (2016). package "mgcv". R package version 1.8.33. Retrieved from http://cran.r-

786 project.org/web/packages/mgcv/

787 Wood, S. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman

788 and Hall/CRC Press.

789

790

791

792

793
794 Supporting Information

795 Table S1. Overview of 132 bird species recorded in Volcanoes National Park, Rwanda, and their
796 elevational distributions, elevational and habitat preferences, and whether the study area
797 contained congeneric species. Included are also miscellaneous data on dietary guild, the
798 inclusion of each species in statistical analyses ('subset'), and the status as Albertine Rift
799 Endemic (ARE) or globally threatened species (following IUCN categories), and the presence of
800 a given species in plots in the ~3 km 'buffer' downslope from the park's boundaries.

801 Table S2. Interpretation of Generalized Additive Models for 35 bird species with at least one
802 significant negative relationship between abundance one of the biotic (habitat availability,
803 competition) or abiotic (temperature, precipitation) at lower or upper range limits.

804 Table S3. Overview of raw output of Generalized Additive Models of abundance for 35 bird
805 species.

806

807

808

809

810

811

812

813

814

815

816