Avian elevational migration patterns in the Western Ghats, as revealed by participatory science and systematic surveys

Avian elevational migration patterns in the Western Ghats, as revealed by participatory science and systematic surveys | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Avian elevational migration patterns in the Western Ghats, as revealed by participatory science and systematic surveys View ORCID Profile Ali Khan Faizee , View ORCID Profile Vijay Ramesh , View ORCID Profile V.V. Robin doi: https://doi.org/10.1101/2025.11.05.684726 Ali Khan Faizee 1 Indian Institute of Science Education and Research , Tirupati, Andhra Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ali Khan Faizee For correspondence: faizeealikhan{at}students.iisertirupati.ac.in robin{at}labs.iisertirupati.ac.in Vijay Ramesh 2 K. Lisa Yang Center for Conservation Bioacoustics , Cornell Lab of Ornithology, Ithaca, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vijay Ramesh V.V. Robin 1 Indian Institute of Science Education and Research , Tirupati, Andhra Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for V.V. Robin Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Elevational migration is short-distance migration through which species track seasonal changes in weather and resources along elevational gradients. Key identified drivers of elevational migration include climatic variability, resource availability, and predation. Although the spatiotemporal coverage of participatory science platforms like eBird can be used to quantify elevational migration patterns, such documentation is missing for most of the world’s ecozones. We investigate the extent of seasonal elevational migration for birds present year-round in the tallest mountains of the Western Ghats biodiversity hotspot - the Nilgiris. We combined rigorously curated participatory science data with systematic field surveys. We estimated the elevational shifts of 70 species of birds between the hottest and coldest quarters in the eastern Nilgiris. We ran regression models to quantify the associations between these shifts and their suspected drivers. Around 70% of the species in the region shifted in their elevational ranges, and the frequency of shifts was higher at lower and mid elevations. Downslope shifts were positively associated with a narrow thermal range and species’ lower temperature limit, while the opposite associations were seen for upslope shifts. We suspect species might be tracking their thermal regimes, and the temperature limits of species can be a major driver of elevational migration in the Nilgiris. The predominance of partial elevational migration in the region provides opportunities for testing theories on biotic drivers of these movements in the future. We highlight the combined usage of field and participatory science in examining ecological patterns in understudied parts of the world. 1. INTRODUCTION Elevational migration is a widespread phenomenon in over 10% of bird species globally (Barcante et al. 2017). Such migrations involve the seasonal movement of a species from one altitude to another annually ( Hayes, 1995 ). Several biotic and abiotic hypotheses have been proposed and tested independently to understand the drivers of elevational migration ( Vander Pluym & Mason, 2024 ) (See Table 1 for a complete list). Of these hypotheses, climatic variability, resource availability, and predation have been purported as key drivers of elevational migration ( Barçante et al., 2017 ). The climatic constraints hypothesis states that the seasonal downslope movement of birds along elevations is a result of their intolerance to climatic seasonality and harshness at higher elevations ( Boyle, 2011 ; Cox, 1985 ). Correspondingly, Menon et al. 2023 showed that birds in the mountains of the Himalaya shift elevations more to remain within narrow thermal regimes (the intra-annual variation in temperature experienced by a species) across seasons. However, species also move elevationally as a function of resource availability across seasons. Frugivores, insectivores, and nectarivores may move elevationally to avoid resource scarcity ( Boyle, 2010 ; Loiselle & Blake, 1991 ). Aligning with the hypothesis, Tsai et al. (2021) showed that seed eaters and insectivores tend to shift in elevational ranges more than other diet guilds in the mountains of Taiwan. On the other hand, factors like nest predation and competition can drive patterns of elevational migration but have remained understudied. For example, Boyle (2008a) found an overall decline in predation with increasing elevation on the Atlantic slope of Costa Rica. A recent study observed that competition with the insectivorous Oecophylla ants drives species richness of insectivorous birds along the elevational gradient globally ( Srinivasan et al., 2024 ). Nonetheless, the tropical mountains of South Asia have not been investigated for patterns of species elevational distributions or movements (but see Akshay et al., 2024 ); thus, whether these hypotheses are applicable globally is an unanswered question. View this table: View inline View popup Download powerpoint Table 1: Predicted associations between species shifts in elevational ranges and their traits according to the literature and the observed associations between them according to our phylogeny-controlled regression models. The associations were examined for upslope and downslope shifts separately. Symbols for the observed associations: + significantly positive associations under three resampling settings; − significantly negative associations under three resampling settings; (+) significantly positive associations under 1-2 resampling settings; (−) significantly negative associations under 1-2 resampling settings; ns no significant associations. * The climatic constraint and food availability hypotheses have different predictions on the associations. The spatiotemporal coverage offered by participatory science has enabled the quantification of the extent of elevational migration and the testing of several hypotheses on its drivers. The use of the world’s largest participatory science project - eBird ( Sullivan et al., 2009 ) has assisted in identifying widespread and prevalent patterns of partial elevational migration for entire bird communities and mountain ranges ( Menon et al., 2023 ; Tsai et al., 2021 ). eBird data have been used to show that climatic constraints and food availability predominantly explain occupancy and annual shifts in elevational ranges across temperate ( Menon et al. 2023 ), subtropical ( Tsai et al. 2021 ), and tropical ( Rueda-Uribe et al., 2024 ) elevational gradients. Despite its unique advantages and the ability to infer ecological patterns globally, participatory science data is associated with data quality issues, such as spatial clustering, unequal sampling in different seasons and elevations, and undersampling of protected areas (for example, Backstrom et al., 2024 ; Praveen, 2017 ). Moreover, in topographically complex regions, undersampling (lack of data from several elevational bands due to lack of access and logistical challenges) and oversampling (clustering data from specific elevations alone) can lead to biased inferences ( Johnston et al., 2021 ; Ramesh et al., 2022 ). In parts of the understudied global south, especially in the Western Ghats, the elevational gradient is not equally sampled by eBird observers (Figure S2). Recent methodological developments in ecology have proposed using and integrating various approaches to biodiversity data collection, such as systematic field surveys, acoustic monitoring, participatory science, and tracking technologies ( Miller et al., 2019 ). Pacifici et al. (2017) developed a framework of integration of unstructured (participatory science) and structured data (systematic surveys) that addresses the tradeoff between data quality and quantity. By using systematic expert surveys to fill the gaps in participatory science data, we can (i) equalize effort across the elevational bands, (ii) add sampling effort in inaccessible terrain and protected areas, and (iii) reduce biases in species detection effort. Some studies have demonstrated that combining data from participatory science with systematic surveys improves the ecological inference and predictive ability of distribution models ( Miller et al., 2019 ; Robinson et al., 2020 ; Zhao et al., 2024 ). Such an integrative approach is critical in tropical mountains where patterns of elevational migration are expected to be prevalent. Across the Nilgiri mountains of the Western Ghats, we quantify elevational migration and infer the potential drivers of these movements at a fine scale. The 2600 m elevational gradient of the Nilgiris encompasses an array of weather profiles that vary through the year, thus affecting the intra-annual variation in temperature (e.g., the highest intra-annual variation in temperature in the Nilgiris is 17, Figure S3), food resources, and precipitation experienced by a species. Using 70 species of birds present year-round in the Nilgiris, we determine the extent and patterns of shifts in elevational ranges. We tested hypotheses of drivers of elevational migration in the Nilgiris. We determined the associations between shifts in elevational ranges and diet, thermal regime, dispersal ability, and body mass. The existing literature (Girish and Srinivasan 2020; Williamson and Witt 2021 ; Barçante et al. 2017 ) has well-established that altitudinally migrating species can show two distinct patterns of range shift: (i) species may track their climatic niche through changing elevation ( Menon et al., 2023 ), (ii) species may shift to disparate niches altogether to exploit other resources ( Williamson & Witt, 2021 ). It has been seen that insectivores, frugivores, and nectarivores exhibit high tendencies of downslope movements in winter ( Boyle, 2010 ; Menon et al., 2023 ) because of seasonal abundances of these resources at higher elevations; thus, we expect a similar pattern in this tropical mountain range. Moreover, we expect species with generalist diets to have a lower tendency to shift their range as they can use various seasonally available resources ( Stephens et al., 2019 ). We do not test the predation risk hypothesis due to a lack of regional nesting and breeding data across species. 2. METHODS 2.1 Study Site The Nilgiri hills of the Western Ghats-Sri Lanka biodiversity hotspot offer an excellent setting to investigate elevational migration patterns and their drivers. This region possesses an elevational gradient of ~2600 m a.s.l. and a range of habitat types, from scrub forests (below 600m a.s.l.) to the tropical montane cloud forests (above 1400 m a.s.l., locally known as Shola ) and mid-elevation broadleaf forests (between 700m and 1400m a.s.l.) ( Gadgil & Homji 1986 , Pascal 1988). The Nilgiri hills possess a high diversity of about 369 bird species ( Viswanathan et al., 2024 ), of which 16 are globally threatened (IUCN 2020). The Western Ghats mountains also have 29 endemic bird species occupying diverse habitat types ( Praveen, Jayapal, & Pittie, 2016 ). We focus on the eastern slope of the Nilgiris due to the extensive participatory science observations and a road network covering the entire elevational gradient ( Figure 1 ). Our study region consisted of scrub forests, moist deciduous forests, and shola forests, which mainly included forest and open habitat species. Download figure Open in new tab Figure 1: The Nilgiris mountain range, bounded by a red line in (a), is a group of mountains in the Western Ghats of India. (a) Left: Mean daily maximum air temperature of the warmest month (May); right: mean daily minimum air temperature of the coldest month (January). The black outline marks the Nilgiris mountains of the Western Ghats, and the red square denotes the eastern slope of the Nilgiris. (b) Map showing the distribution of eBird sampling effort (checklists) along the elevational gradient of eastern Nilgiris, the orange to green color gradient of the 1 km 2 squares denotes the number of checklists within the square, and red diamonds indicate locations of our systematic surveys. 2.2 Curating eBird data We used eBird ( Sullivan et al., 2009 , data between 2013 and 2022), a global, crowd-sourced platform collecting bird observations, to extract species occurrence information across the eastern slope of the Nilgiris ( Figure 1b , Figure S1). Data in eBird consists of observations uploaded by users and stored as checklists. Each checklist contains information on bird species presence, number of individuals, observers, time spent observing birds, etc. We initially downloaded all eBird data from January 2013 to December 2022 for our study area (31,524 checklists). This dataset resulted in checklist-level information for 935 unique localities (unique GPS coordinates of checklist locations) spanning elevations ranging from 100 m to 2600 m above sea level. We determined the hottest and coldest quarters for the eastern Nilgiris mountain range using bioclimatic data at 1 km resolution from CHELSA ( Karger et al. 2021 ). We then divided the eBird dataset into two subsets based on the month of the sampling event. The summer dataset consisted of data from the hottest quarter in the study site (i.e., April to June). Similarly, the winter dataset included data from the coldest quarter (i.e., November to January). From the filtered eBird dataset, we retained unique, complete checklists with the following attributes in our dataset: distance traveled less than 2.5 km, checklist duration not more than 2 hours, and those checklists with fewer than ten observers, following Menon et al. (2023) to reduce spatial uncertainty and data quality issues. Further, we filtered out non-resident species in the Nilgiris by selecting only those species listed as resident (birds that do not show any latitudinal migration) in both the State of India’s Birds report (SoIB 2023) and Kerala Bird Atlas ( Praveen et al., 2022 ). We removed species that are prone to misidentification, including raptors (Accipitriformes), swifts (Apodiformes), swallows (Hirundinidae), and falcons (Falconidae). See Table S1 for a complete list of species included and excluded in the analysis. 2.3 Generating and integrating data from systematic surveys Since participatory data is opportunistic, sampling may be higher or lower across certain regions and seasons ( Backstrom et al., 2024 ), introducing biases and gaps. We explored the number of checklists per 100 km 2 for various elevational bins (200m, 400m, 500m). We found that the 400 m elevational bins provide the most evenly distributed checklist density across elevational bands (Figure S2). We then conducted systematic surveys across locations with limited eBird data to fill these gaps and improve downstream analysis. The study area of eastern Nilgiris was overlaid with 1km 2 grids, and the number of checklists and the mean elevation within each grid were calculated. We found the distribution of eBird checklists was clustered near major highways and in certain elevational bands ( Figure 1b ). From these grids, we selected three locations more than 400 meters apart from each other, and more than 200 meters away from a road (to avoid disturbances) in each 400-meter elevation band as spatial replicates for our systematic field surveys. In each chosen location, we carried out a 20-minute variable distance point count ( Dawson et al., 1995 ), recording all visual and aural observations of bird species. We sampled 30 localities ( Figure 1b ) spanning 264m to 2200m in elevation four times, at least twice in the morning and at least once in the evening, between December 2023 and May 2024. We generated a combined dataset by collating bird species occurrences from systematic surveys and eBird. We reorganized our systematic point count surveys to resemble the structure of the checklist data from eBird. Each point count was treated as a stationary count (distance traveled = 0), and a unique sampling identifier was generated by concatenating the plot ID and date. After removing the non-relevant variables (like last edited date, subspecies, country, IBA code, media, comments, etc.), the two data sets were identical in structure, which allowed us to combine them. The resulting dataset is referred to as the combined dataset throughout the text. 2.4 Estimating the seasonal shifts in elevational ranges We extracted the elevation for each sampling locality of the combined data using the SRTM Digital Elevation Model ( NASA JPL, 2013 ). We calculated the average elevation in a 100 m radius to account for detection distance and uncertainty in GPS locations. Although we conducted systematic surveys along elevational bands, the data thus generated were not comparable to the eBird dataset. We accounted for the sampling bias due to the uneven distribution of checklists across elevations and seasons by following a resampling approach described by Tsai et al. (2021) . The resampling approach generates subsets of data with a controlled effort (number of checklists) from each elevational band by repeatedly subsampling (with replacement) a fraction of checklists from the whole dataset on a pre-defined effort parameter (1st, 2nd, and 3rd quantiles of checklists distributed across elevation bands). We achieved equal sampling effort across elevation band-season combinations by subsampling checklists with replacement 1000 times. To account for fluctuations brought about by subsampling, we performed it with three different levels of effort: the first, second, and third quantiles (500 checklists, 1673 checklists, and 4095 checklists, respectively) of the total sampling events among elevation bands and seasons. To avoid bias in elevation ranges for uncommon species and species with low detection rates, we only included species with more than 15 occurrences in all elevational bands each season for further analysis. Since 15 was randomly selected, we also repeated this for 30 occurrences. Using the resampled datasets, we calculated each species’ 5th, 50th, and 95th percentiles of elevation for the summer and winter seasons separately, representing the lower limit, center of distribution (median), and upper limit, respectively. The seasonal shifts were then calculated as differences in distribution limits (upper, lower, or median) in two seasons (summer vs. winter). We classified shifts in species distribution parameters (lower limit, median, and upper limit) into downslope shifts if the 95% confidence interval of the difference in their distribution limits (summer minus winter) was above zero, upslope shifts if this interval was below zero, and no shift if it contained zero. In total, 70 species (Table S3) had at least 15 records in both seasons in at least one resampled sampling event set and were included in the following analyses. 2.5 Determining potential drivers of elevational migration We used the shifts in elevational ranges estimated from the combined dataset to examine the hypothesis about drivers of elevational migration in the Nilgiris ( Table 1 ). To test the climatic constraint hypothesis, we tested whether the lowest (lower temperature limit), highest (higher temperature limit) temperatures a species experiences in a region, and thermal regime (the difference between the highest and lowest temperatures in an area) are associated with shifts in elevational ranges. The higher and lower temperature limits for the species had a large positive correlation (Table S2), so we only used the species’ lower temperature limit in further analysis. The mean lowest temperatures in the coldest quarter, the highest in the hottest quarter, and their differences (thermal regime) were calculated at all elevations from climatic layers downloaded from the CHELSA climatic dataset ( Karger et al., 2021 ). Since the bird occurrence data were variable distance counts, we averaged the climatic variables for 100-meter elevational bands to account for variation in GPS accuracy and detection distance. The predictions of the food availability hypothesis in the Nilgiris were explored by examining the association between species’ known diet preferences and shifts in elevational ranges. We extracted species diet preferences (frugivory, nectarivory, omnivory, herbivory, and insectivory) from the EltonTraits dataset ( Wilman et al., 2014 ). Frugivory and nectarivory were clumped together because these are seasonal, carbohydrate-rich resources, and many studies have indicated that most birds that undergo elevational migration are frugivores or nectarivores ( Boyle & Conway 2007 , Boyle 2008b ). We calculated the dietary diversity of a species by calculating the Shannon-Wiener diversity index from the proportions of food types in the diet listed in the EltonTraits dataset, which we used as a proxy for a generalist diet ( Fontaine et al., 2008 ). We used body mass on a base 10 logarithm scale (to reduce the huge variation between species) to test whether body size correlates with shifts in elevational ranges. Finally, we used the hand-wing index from the Sheard trait dataset ( Sheard et al., 2020 ) as a proxy for dispersal ability ( Arango et al., 2022 ) to test whether it is correlated with shifts in elevational ranges. We constructed phylogenetic generalized least squares (PGLS) regression models ( Martins and Hansen 1997 ) to determine the associations between shifts in either of the three measures of elevational distribution (lower limit, median, and upper limit) and the traits mentioned above. Using our combined dataset, we calculated the elevational shift for each species (lower limit, median, and upper limit). Further, resident species whose sub-populations are involved in latitudinal migrations were also removed from the PGLS analyses, because these range shifts may be due to the migratory populations moving into the Nilgiris in winters. We classified a species as showing an upslope shift or downslope shift if the lower limit, median, or upper limit shifted upslope or downslope, respectively, without any other limits shifting the other way. We argue that if a species is showing downslope shifts in one or more limits without any indication of upslope shift in any limit, then statistically it will mean that there are fewer (or equal) detections of the species at the elevations where they were in the first season (summer) than in the second season (winter). Thus, the population is probably moving down the elevation, and vice versa for upslope shifts. The drivers of elevational migration have different impacts for species showing upslope vs. downslope movement and hence different expectations for the associations between traits and shifts in elevational ranges ( Table 1 ) ( Vander Pluym & Mason, 2024 ). Similarly, frugivores and nectarivores can move in either direction to track the seasonal blooming of flowers and fruits, irrespective of the summer or winter season ( Boyle, 2011 ). Hence, we ran two PGLS models - one for species that showed an upslope shift and one for downslope shifts, separately. We note that our diet assessment was not based on field data, but was a trait in the analyses. Splitting the species movements into upslope and downslope reduces the number of species and thereby the statistical power of the analysis, because the difference between the number of predictors and instances of significant shifts (also referred to as cases in regression terminology) is reduced, i.e., there is not enough data to test the effect of all the predictors. Thus, we removed predictors that strongly correlated with each other. Further, for the upslope shift model, the species’ parameter (one from the lower limit, median, or upper limit) that showed the largest significant upslope shift (95% confidence interval of the shift below zero) was selected. For the downslope shift model, the species’ parameter (one from the lower limit, median, or upper limit) that showed the largest significant downslope shift (95% confidence interval of the shift above zero) was selected. We iterated over the different levels of sampling effort (first, second, and third quartiles) and occurrence threshold datasets (15 and 30 detections) to build models. A consensus tree was obtained with the least-squares edge lengths method from the 1000 trees (downloaded from the Bird Tree of Life ( Jetz et al., 2012 ) and was pruned to the species present in the analysis, according to the requirements of the PGLS algorithm. For the categorical diet variable, omnivory was chosen as the reference group. We assumed that a Brownian Motion evolution model could explain the error structure in the residuals, and these errors correlate with the species’ phylogenetic closeness. All analyses were carried out in R version 4.3.3 with the help of the phytools ( Revell, 2024 ), nlme (ver. 3.1; Pinheiro et al., 2013), and ape ( Paradis & Schliep, 2019 ) packages. 3. RESULTS We recorded 1685 observations of 91 resident species from our systematic field surveys. We recorded data from 132 species across 19234 checklists for the same region. Using the combined dataset, we retained 70 species having enough occurrences (at least 15 in each season). We found that 54 (77%) birds seasonally shifted at least one of their upper, median, or lower elevational limits across seasons ( Figure 2 , Table S3). The elevational breadth reduced for 24 species from summer to winter, while it expanded for 28. Thus, a pattern of partial elevational migration could be prevalent, with no apparent changes in elevational distribution for 16 species. The magnitude and prevalence of upslope and downslope shifts were not significantly different. We found a median downslope shift of 153m (± 214), 282m (± 161), and 287m (± 187) and a median upslope shift of −129m (± 183), 369m (± 311), and −225m (± 151) in the lower boundary, median, and upper limits, respectively. Download figure Open in new tab Figure 2: Elevational range comparisons in the Nilgiris (threshold = 15) between summer and winter. Yellow and blue bars represent the elevational ranges estimated during summer and winter. The gray represents the overlap in range between these two seasons, and arrows indicate a significant difference in the median (the length of the arrow is proportional to the difference). We found that 26 of these 70 species had a significant downslope shift (95% confidence interval of shift estimate completely below zero) and no upslope shift in either the lower elevational limit, upper elevational limit, or median, indicating downslope movements during winter ( Table S3). Similarly, 23 species had a significant upslope shift (95% confidence interval of shift estimate completely above zero) during winter and no downslope shift in the lower elevational limit, upper elevational limit, or median, indicating upslope movements. We found that species shifted their lower elevation limits more often than their upper ones. Specifically, 43 species shifted their lower limits, 22 shifted their midpoints, and 15 shifted their upper limits, either upward or downward. This pattern matched the temperature range (the difference between the highest and lowest temperatures experienced in that elevational band), which was greater at lower elevations (Figure S3). The birds that show the largest shifts in their lower limit, median, and upper limit of distribution are Lonchura punctulata (855m down in winter), Acridotheres tristis (1121 m up in summer), and Psilopogon zeylanicus (625 m down in winter), respectively; it is possible that such large movements for these particular species could be an artifact of sampling, because of their generalist nature and commonality. Culicicapa ceylonensis and Turdus simillimus show an expansion in their winter range (i.e., upper limit moved higher and lower limit moved down). For the PGLS model that examined downslope shift, we discovered consistent and large negative associations between shifts in elevational ranges and the range of temperatures experienced by the species (relative effect = −142 m ± 7.1, P < 0.05). In addition, significant positive associations were detected between the lowest temperature experienced by a species (lower temperature limit) and downslope shifts (relative effect = 15 m ± 4.8) ( Figure 3a ). Across diet guilds, with omnivory as a reference, we did not find any significant associations with downslope shifts ( Figure 3a ). For the PGLS model that examined species showing upslope shifts, we recovered a positive association between the range of temperatures experienced by a species and upslope shifts in elevational ranges (relative effect = 134 m ± 7, P < 0.05). We also found a significant negative effect of the lowest temperature experienced by a species (lower temperature limit) on upslope shifts (relative effect = −15.1 m ± 2.6, P < 0.05). Our results showed that the species eating more fruits and nectar had a lower tendency to make upslope shifts than omnivores (the reference category) ( Figure 3 , relative effect = −76.1 m ± 23.4, P < 0.05), but only in one of the models. Species dietary diversity, species dispersal ability (hand-wing index), and size (body mass) did not seem to have significant effects on the extent of elevational shift (P > 0.05, Table S 4) in any of the models. Download figure Open in new tab Figure 3: Associations between birds’ traits. (a) Downslope shifts in their elevational distribution, (b) upslope shifts in their elevational distributions, in the Nilgiris. Asterisks denote significant effect sizes (P < 0.05). Dots are the model estimates, and error bars represent standard errors for PGLS models. The dot color indicates the sampling effort (light gray to black: low to high). Estimated from the eBird + systematic surveys dataset. 4. DISCUSSION In this study, we characterized the patterns and drivers of shifts in elevational ranges of 70 species of resident birds in the eastern Nilgiris mountain range of the Western Ghats biodiversity hotspot. We show that the traditional survey methods ( Dobkin & Rich, 1998 ), which are financially and logistically complicated at large spatial scales, can be integrated with eBird data to test rigorous hypotheses even for topographically complex areas. Our analysis suggests that 70% of these selected birds from the resident bird community show seasonal shifts in their elevational ranges. However, this pattern was not recovered uniformly across all species, as overlaps in summer and winter ranges were common, and most species had specific ways of shifting their ranges between seasons. This lack of non-overlap in seasonal elevational ranges in the Nilgiris can be attributed to the smaller elevational gradient (0 to 2400 meters), limited temperature seasonality, and the tropical climate promoting year-round availability of resources, as compared to other montane ecosystems such as the Himalayas or the Andes ( Imfeld et al., 2021 ; Karuppusamy, 2023 ; Yadav et al., 2021 ). Our results support the climatic constraints hypothesis while offering limited support for the food availability hypothesis in these tropical mountains. We find subtle differences in the patterns and drivers inferred in this study, focused on the Nilgiris and another one which targeted the entire Western Ghats (Akshay et al., 2024a), and speculate that these differences could arise due to geographic scale and the pipelines of analyzing participatory science data. The proportion of species in the eastern Nilgiris involved in elevational migration is much higher (70%) than the reported estimates (44%) from the neotropics (Akshay et al., 2024b; Barçante et al., 2017 ), but comparable to estimates in temperate Himalayas (65 %) and other subtropical regions (70%) ( Menon et al., 2023 ; Tsai et al., 2021 ). A recent study that used eBird data alone across the entire Western Ghats showed that at least 57 species (35% of the community analyzed) displayed upslope movement during summer (winter to summer) ( Akshay et al. 2024 ). We suspect that the differences in the proportion of species involved in migration and the median elevational shift estimates between our study and Akshay et al. 2024 could be because of the geographic scale at which the analysis has been carried out (i.e., the Nilgiris vs. the Western Ghats). When shifts in elevational ranges are calculated at the scale of a large latitudinal gradient (e.g., the Western Ghats), mountain-specific patterns in elevational migration may go undetected due to averaging across multiple mountain ranges and movements across mountains. Moreover, the variations of area in elevational bands, sample size for each bird species, and uneven distribution of biases can contribute to the differences between these studies. We tested two of the four hypotheses concerning the extrinsic drivers of elevational migration ( Vander Pluym & Mason, 2024 ), i.e., the climatic constraints and food availability hypotheses. In agreement with studies in various latitudinal zones, our results support the climatic constraints hypothesis ( Table 1 , Figure 4b ) ( Hahn et al., 2004 ; Menon et al., 2023 ; Tsai et al., 2021 ). We found that birds with a narrower thermal regime have a higher tendency to shift their distribution downslope in winters and vice versa for upslope shifts. The high elevations (> 1400 meters) experience the lowest temperatures and lower seasonality in the Nilgiris (Figure S3), and we found many mid-elevation species (species with medians of distribution around 1000 meters) to be using these elevations only in the summer ( Figure 2 ), taken together with the overall positive association of shifts with the species’ lower temperature limit, it appears that birds in the Nilgiris are tracking their thermal niche. Moreover, the positive association of the lower temperature limit with downslope shifts (implying birds that often shift their ranges downslope experience higher minimum temperatures) and the opposite association with upslope shifts (implying birds which often shift their ranges upslope experience lower minimum temperatures), suggests that some species might be shifting downslope to avoid low temperatures in winters. In contrast, others might move up in the winters to exploit seasonal resources in certain elevational bands. Moreover, given that the low elevations in the Nilgiris (0 to 1000 meters asl) experience the highest temperatures and temperature seasonality (Figure S3), the finding that several birds in these elevations do not show large elevational shifts in their lower distributional limits ( Figures 2 and 3 ); suggests that their lower temperature limit may more strongly determine the distribution of low-elevation species. Also, the moderate negative correlation (Pearson’s correlation of −0.4) between thermal regime and the medians of the species distributions implies that species have wider thermal regimes in the lower elevations and narrower ones in the higher elevations. Download figure Open in new tab Although existing literature has found seasonal downslope movement of most birds along elevations is forced by their intolerance to harsh climates, some birds may move upslope in winters to exploit the reduced competition during winters ( Boyle & Conway, 2007 ; Menon et al, 2022). We found two patterns for upslope shifts: (i) generalist human-associated species (like Passer domesticus, Columba livia, and Pavo cristatus ) making large upslope shifts, which could be to exploit less competition at higher elevations during winters; (ii) locally adapted species (like Chrysocolaptes socialis and Iole indica ) may be moving upslope to breed in extensive tree forests of mid and high elevations during retreating monsoons in the eastern slope of the Nilgiris (December to February). We also note that the high-elevation endemic species (shola species, e.g., Montecincla cachinnans, Ficedula nigrorufa ) do not show large shifts in elevational ranges in the eastern Nilgiris, except for the significant downslope shift for Sholicola major . Lastly, we note among the several species making downslope shifts, the reasoning for downslope movements in summer could be varying: e.g., to exploit fruiting ( Copsychus malabaricus, Alcippe poioicephala ), seasonal insect abundances ( Acridotheres fuscus, Pycnonotus luteolus ), tourist abundances ( Corvus splendens ), in search for breeding sites ( Columba elphinstonii, Psilopogon zeylanicus ), and to avoid cold ( Leptocoma minima, Dicaeum erythrorhynchos ). These intricate associations between species’ thermal niches and movement patterns suggest that global warming could disrupt species phenologies, even in tropical montane systems. The food availability hypothesis predicts, and field survey-based studies have shown, that frugivores and insectivores have a higher tendency to shift downslope in winter ( Boyle, 2010 ; Loiselle & Blake, 1991 ). Our finding of diet guilds having no significant associations with elevational range shifts (Table S3), and the conflicting evidence from other similar studies ( Menon et al., 2023 ; Tsai et al., 2021 ), suggest that the abundance of food resources along this elevational gradient may not vary drastically between seasons, or birds may have variable food preferences. A detailed investigation of seasonal resource availability, foraging opportunities, and food preferences along the elevational gradient of the Nilgiris remains a promising avenue for future research. While our approach can be relied on to study patterns of elevational migration, there are a few limitations. We acknowledge that biases in eBird data can persist even after strict filtering ( Backstrom et al., 2024 ), and participatory science datasets at small scales are prone to fluctuations due to outliers ( Weisshaupt et al., 2021 ). We tried to address these limitations using complete checklists, repeated resampling, excluding the data with species identification uncertainties, and filling spatial sampling gaps with systematic surveys. We also recognize that multiple abiotic and biotic processes often act in conjunction to determine migration patterns ( Janzen, 1987 , 1988); however, testing such intricate processes was beyond the scope of our study. We also note that our tests of foraging and thermal niches are from secondary data, as traits in the model, and direct measurements may provide detailed insights. In conclusion, we demonstrate the use of participatory science data and systematic surveys to infer patterns and potential drivers of elevational migration. By analyzing seasonal changes in the elevational center and the lower and upper range limits of each species, this study provides a window into the distributional shifts and speculates on the potential drivers of elevational migration in the Western Ghats biodiversity hotspot. 5. DISCLOSURE STATEMENTS The authors declare no conflicts of interest. The corresponding author confirms on behalf of all authors that no involvements might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated. No specific ethical clearances were sought for this project, as it did not involve working with people or invasive research about plants/animals. 6. DATA AVAILABILITY STATEMENT The data supporting the study’s findings will be made openly available in Figshare upon acceptance of the manuscript for publication. The corresponding DOI will be shared thereafter. ACKNOWLEDGEMENTS We thank the Ministry of Environment, Forest and Climate Change (MoEFCC), Government of India (through their Long-Term Ecological Observatories programme), and the National Geographic Society for financial support of this study. We extend our thanks to Ashwin Warudkar, Vinay KL, Chiti Arvind, Dr. Jobin Varughese, Naman Goyal, and Archita Sharma for their insightful feedback and constructive criticism, which have played a crucial role in shaping the direction of this project. We thank the Tamil Nadu Forest Department for providing us with permits and support to carry out the field component for this research. Furthermore, we appreciate M. Mubeen, Kovaithambi, Subramanian, Chandrashekhar, and the Keystone Foundation, whose assistance has been indispensable in completing the fieldwork. We also thank Dr. Ramana Athreya, Harikrishnan CP, Namitha JP, Meera MR, Aravind P.S., Zeba Madani, and Dev Bagdi for their timely guidance and advice. Lastly, we would like to sincerely thank the anonymous reviewers, whose constructive feedback and thoughtful suggestions greatly enhanced the quality of this manuscript. Additionally, we acknowledge the Indian Institute of Science Education and Research, Tirupati, for their support in this research. Funder Information Declared Ministry of Environment, Forest and Climate Change (MoEFCC), Government of India National Geographic Society Footnotes https://doi.org/10.6084/m9.figshare.30061018.v1 REFERENCES 1. ↵ Akshay , V. A. , C. J. Campbell , B. Loiselle , and R. Guralnick . 2024 . Avian elevational migrants in India’s Western Ghats challenge climatic constraint theory through broader environmental tolerances . bioRxiv . biorxiv;2024.12.14.628488v2. https://www.biorxiv.org/content/10.1101/2024.12.14.628488v2 2. ↵ Arango , A. , J. Pinto-Ledezma , O. Rojas-Soto , A. M. Lindsay , C. D. Mendenhall , and F. Villalobos . 2022 . 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