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\begin{document}

\title{Threatened species could be more vulnerable to climate change in
tropical countries}



\author[1]{Shawan Chowdhury}%
\affil[1]{The University of Queensland - Saint Lucia Campus}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Insects are the most speciose group of animals and play a central role
in ecosystem processes and functioning, yet they are often overlooked in
conservation studies. Many studies have reported a dramatic decline of
insects globally, where climate change is considered a profound threat;
however, how climate change is impacting tropical insects is largely
unknown, given most studies are from the temperate region. Here, by
considering Bangladeshi butterflies as a case study, I developed
climatic suitability maps for 242 species and compared the suitability
under current and future (2081-2100) climatic conditions. Overall, the
projected future climate could impact 241 of 242 butterfly species
(except for Spindasis elima), where 42\% species could experience range
contraction. Alarmingly, the impact could be significantly more severe
among threatened species. While the direction of shift in habitat
suitability could be multidimensional, about 65\% of species might move
north. While the suitability range could expand for about 58\%, most
threatened species (58\%) could experience range contraction. The mean
elevation of the suitable habitat could increase by 238\%, and again the
situation could be more severe for the threatened butterflies (from 42m
to 108m). The niche-overlap analysis indicates that about 54\% of the
realised niche could be altered. Although there might be no significant
association between the shift in habitat suitability along the
elevational gradient, migratory species could experience a more
significant shift than non-migrants. I show that climate change could
have a severe impact on Bangladeshi butterflies -- depending on
Socio-Economic Pathways (ssps), the future climatic condition could be
unsuitable for 2 (ssp126) -- 34\% (ssp585) species. To mitigate insect
decline globally, and therefore, to meet the Post 2020 Biodiversity
Framework targets, I urge immediate detection of climate change impact
on tropical insects and developing effective conservation strategies.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Insects are a megadiverse group comprising an estimated 5.5 million
species globally (Stork, 2018). They play a central role in ecosystem
processes and functioning through pollination, herbivory, detritivory,
nutrient cycling, influencing the physiology and population dynamics of
plants, and providing a major food source for species from higher
trophic levels (Samways, 2007; Stork, 2018; Wagner, 2020; Dicks et al.,
2021; Seibold et al., 2021). Recent studies suggest that insect
populations are in a state of catastrophic collapse in many parts of the
world, evidenced by rapid declines in abundance as well as extinctions
(Hallmann et al., 2017; Didham et al., 2020; Wagner, 2020; Cardoso et
al., 2020; Goulson, 2021; Wagner et al., 2021). Yet the extent of the
global insect collapse is poorly understood (Wagner, 2020), given that
insects comprise only 8\% of species formally assessed against IUCN Red
List extinction risk criteria (12,100 of 142,577 species;
https://www.iucnredlist.org/; accessed on 15 April 2022).

Despite their apparent importance, insects are often neglected in
studies on ecosystem function and conservation assessments (Dunn, 2005;
Samways, 2007; Di Marco et al., 2017; Taylor et al., 2018; Geyle et al.,
2021); however, butterflies, to some extent, are an exception because
the taxonomy, geographic distribution and status of many species are
relatively well known (Schulze et al., 2004, Schultz et al., 2019).
Butterflies are good indicators of habitat quality as well as general
environmental health (Dennis et al., 2003), are sensitive to disturbance
and changes in their habitat, and can severely be affected by
environmental changes, including abiotic factors - such as temperature,
light intensity, soil composition, radiation, humidity, photoperiod, and
changes in the forest structure (Whitworth et al., 2018). Even minor
changes in their habitat can lead to either migration or local
extinction (Thomas et al., 2006; Chowdhury et al., 2017). Recent studies
have revealed dramatic declines in the richness and abundance of
butterflies worldwide, with climate change and habitat alteration
emerging as critical threats (Forister et al., 2010; Oliver et al.,
2015; Forister et al., 2019; Soroye et al., 2020; Wagner, 2020; Halsch
et al., 2021; Hill et al., 2021; Raven and Wagner, 2021).

The world's temperature has risen by \textasciitilde{} 1\selectlanguage{ngerman}°C since 1880
(McGregor, 2018), and precipitation patterns are also changing (Dore,
2005). Climate change substantially impacts species' biological systems,
leading to shifts in the timing of important life-history events,
changes in geographical distribution and phenotypical features,
migration, and even extinction (Walther et al., 2002; Lenoir et al.,
2010; Parmesan, 2006; Van der Putten, 2012; Gardner et al., 2011; Lenoir
and Svenning, 2014; Furlong and Zalucki, 2017; Oostra et al., 2018;
Lindestad et al., 2019; Merckx et al., 2021). In fact, climate change is
predicted to become the dominant driver of global species decline in the
coming future (Di Marco et al., 2019). With the increasing temperature,
species need to move polewards to track suitable environmental
conditions and prevent widespread extinction (Thomas et al. 2004), which
has been documented in many taxa (Parmesan 1996; Hill et al. 1999;
Parmesan et al. 1999; Parmesan \& Yohe 2003; Wilson et al. 2005;
Hickling et al. 2006; Poyry et al. 2009; Amano et al., 2014; Garcia et
al., 2014; Warren et al., 2018). Climate-induced shifts in a species
distribution commonly involve movement toward higher latitude (with a
median rate of 1.69 km/year) and higher elevation (with a median rate of
1.1 m/year; Chen et al., 2011). Due to several anthropogenic stressors,
geographic range shifts required to track changing climate are about 2-5
times higher than the fastest range shifts observed in the fossil record
(Davis \& Shaw, 2001).

Due to a short lifespan and being sensitive to environmental conditions,
butterflies have been widely considered a model group to assess climate
change impacts on ecosystems (Dennis et al., 2003; Nadeau et al., 2017).
About 29\% of studies testing climate change impact on insects are on
butterflies (Andrew et al., 2013). Given climate change is a major
factor contributing to butterfly decline globally (Wagner, 2020; Halsch
et al., 2021), identifying the extent of climate change impact on
butterflies is a top conservation priority (Hill et al., 2021). When
climate changes, species move areas to match the suitable climate
(Thomas et al., 2004). There is mounting evidence showing species
redistribution as a response to climate change globally (Chen et al.,
2011; Taheri et al., 2021). Species distribution models (SDMs) are
widely used to assess climate change impact on species distributions and
to forecast changes in distributions under different climate change
scenarios (Pearson \& Dawson, 2003; Araújo et al., 2005; Heikkinen et
al., 2006; Elith et al., 2010; Yates et al., 2010). With SDMs, we can
predict the probable abundance of species based on resource
distributions or climatic variables (Eskildsen et al., 2013; Araújo et
al., 2019). Species with wide and variable distribution and species that
have been accidentally or deliberately introduced to a new geographic
area and eventually have established and spread fit the model
description (Guisan and Zimmermann, 2000, Elith et al., 2006, Elith et
al., 2011).

Climate change impact differs markedly both taxonomically and
geographically with the temperature (Mikkonen et al., 2015; Amano et
al., 2020; Taheri et al., 2021), yet most studies are taxonomically and
geographically biased. Of the published literature on insect
redistribution as a response to climate change, about 50\% are from
Europe (only four studies from south Asia) and 23.47\% on birds (only
0.44\% on insects; Taheri et al., 2021). A vast majority of insect
species are distributed in the tropics, yet how climate change impacts
tropical insects is largely unknown (Chen et al., 2011; García-Robledo
et al., 2020; Kwon et al., 2014; Taheri et al., 2021). Here, I assess
climate change impact on butterflies in a tropical country, Bangladesh
-- a South Asian developing country. Bangladesh is located at the
transition between the Indo-Himalayan and Indo-Chinese subregions of the
Oriental region (Reza and Hasan, 2019) and forms part of the Indo-Burma
biodiversity hotspot (Mittermeier et al., 1998). Being the most densely
populated country (among countries with \selectlanguage{english}[?]10 million people), there is
always a high demand for the limited resources, which is causing rapid
habitat clearance, and natural resource extraction is a crucial economic
activity (Mukul et al., 2008; Watson et al., 2014; Chowdhury et al.,
2021b, f). Only about 11\% of the original forest cover in Bangladesh
remains, and deforestation is continuing rapidly (Poffenberger, 2000;
Mukul and Quazi, 2009; Chowdhury et al., 2021b). About 62\% of 305
butterflies found in Bangladesh are now threatened with local extinction
(IUCN Bangladesh, 2015).

Here I collate species distribution records from a range of sources
(online repository, social media, and published literature), and i)
develop climatic niche models for 242 Bangladeshi butterfly species
using current and four future climatic scenarios, ii) compare the
suitability maps to assess the impact of climate change, iii) develop
generalised linear mixed models (GLMMs) to assess if the shift in
suitable habitat is associated with the current amount of suitable
habitat and elevational distribution, and iv) compare the results
between migratory and non-migratory species to determine if migratory
species can adjust with the shift faster than the non-migratory species.
Finally, I discuss ways to mitigate climate change impact on tropical
insects. I chose butterflies as a model group because butterflies are
the most threatened assessed taxonomic group in Bangladesh (62\% are
nationally threatened), the only assessed insect group in the most
recent national threatened species assessment (IUCN Bangladesh, 2015;
Chowdhury et al., 2021b).

\textbf{Methods}

\emph{Data}

First, I collected a complete checklist of butterflies of Bangladesh
from the most recent national Red List databook (305 species; IUCN
Bangladesh, 2015), which contains both threatened and non-threatened
butterflies. While collating species distribution data, I followed a
range of approaches following Chowdhury et al. (2021a, b). Initially, I
downloaded spatial occurrence records from the Global Biodiversity
Information Facility (GBIF; https://www.gbif.org/) using the `rgbif'
package (Chamberlain et al., 2017) in R (R version 4.0.4). GBIF is a
global data infrastructure network that compiles species occurrence
records from several sources, including museum specimens and citizen
science projects (Heberling et al., 2021). To avoid repetition, I did
not collect data from other biodiversity repositories. Second, I
searched for species distribution records in a Facebook group: Butterfly
Bangladesh (https://www.facebook.com/groups/488719627817749). Here, I
searched by species common name obtained from IUCN Bangladesh (2015),
double-checked the identification in each photograph, and georeferenced
the observations using the Google Map (https://maps.google.com/). I
excluded photographs if the identification was incomplete (not up to
species level) or wrong, if the photograph was not clear (from the
taxonomic viewpoint), and if the location was unspecified (Chowdhury et
al., 2021a, b). Third, I included my personal field observation records.
Finally, I scanned for species distribution records from the published
resources. Overall, the dataset contained 27,580 spatial records.

I collected both the current and the future (2081-2100) climatic data at
21.625 km2 resolution (2.5 min) from the WorldClim database
(https://worldclim.org/). For the future climatic data, I used
projections developed by Coupled Model Intercomparison Project
(CNRM-CM6-1) in four Shared Socio-Economic Pathways (ssp; ssp126,
ssp245, ssp370, and ssp585).

To compare the results between the migratory and the non-migratory
butterflies, I downloaded the list of migratory butterflies from
Chowdhury et al. (2021d).

\emph{Data cleaning}

During the spatial data cleaning process, I removed duplicate records,
precision uncertainty over 10 km, imprecise coordinates (zero
coordinates, integers, records in oceans), and invalid coordinates that
the specified locality was incompatible with the coordinates given
(Chowdhury et al., 2021a, b, e) using the CoordinateCleaner package
(Zizka et al., 2019) in R.

To control sampling bias, I followed the spatial thinning approach.
Using the spThin R package (Aiello-Lammens et al., 2015), for each
butterfly species, I considered occurrence records at 4.65 km distant
from each other, which means that there was only a single occurrence
record at 21.625 km\textsuperscript{2}. The final dataset contained
7,606 records for 285 species (Figure 1; supplementary table S1).

Before fitting the model, I checked collinearity among the WorldClim
variables and removed highly correlated (r \textgreater{} 0.75)
variables (Zurell et al., 2020). I removed 11 variables (bio2, bio4,
bio5, bio6, bio7, bio8, bio10, bio12, bio13, bio16, bio19) and had 8
variables for the model fitting. I used the same eight variables for
future model predictions.

\emph{Climatic niche model}

I developed MaxEnt climatic niche model in R using the ENMeval package
(Muscarella et al., 2014) by considering eight climatic variables. I
validated each model with 10-fold cross-validation, tuned it using six
feature class combinations (''L'', ''LQ'', ''H'', ''LQH'', ''LQHP'',
''LQHPT'') and several candidate regularisation multipliers. While
choosing the best model, I chose the one with the highest AUC (the Area
Under the Curve) score. I used the `checkerboard2' evaluation method to
handle model overinflation resulting from biased sampling (Muscarella et
al., 2014; Chowdhury et al., 2021b,e), which partitions both geospatial
records and background points into evaluation bins to reduce spatial
autocorrelation between points in the testing and training bins
(Muscarella et al., 2014). I used the best-fitted model (with the
current climatic data) and predicted the future suitability (with the
future climatic data) using the `dismo' R package (Hijmans et al.,
2017).

Based on the maximum sum and sensitivity statistics, I did threshold the
suitability map (Liu et al.,2016) and converted it into binary (1
(presence): suitability value \textgreater{} threshold; 0 (absence):
suitability value \selectlanguage{english}[?] threshold). To threshold the future suitability map,
I used the same threshold value obtained from the best-fitted model
under the current climatic condition. Overall, I obtained five
suitability maps (current, ssp126, ssp245, ssp370, and ssp585) for each
of the 242 butterfly species.

\emph{Changes in suitability distribution}

To assess the impact of climate change on the suitability of Bangladeshi
butterflies, I followed four steps. First, I calculated the centroid
(centre point (latitude and longitude)) and the area (summed the pixel
value (1/0) and multiplied it with the pixel resolution (21.625 km2))
for each suitability map using the `raster' R package (Hijmans et al.,
2015). Second, I calculated the distance and angle between the centroids
using the `geosphere' R package (Hijmans, 2017). Third, for each
species, I calculated the regions where the suitability range expanded,
contracted, or remained the same (overlapped) using the `raster' R
package (Hijmans et al., 2015). I stacked all the rasters (species-wise)
to determine the overall expanded, contracted, and overlapped regions.
Finally, I calculated the potential change in realised niche
distribution between the current and the future suitability
distributions using the `dismo' R package (Hijmans et al., 2017). For
all these combinations, I compared the current suitability with the four
future climatic scenarios (ssp126, ssp245, ssp370, and ssp585); however,
for the clarity purpose, I only used the results of the ssp126 in the
main manuscript, and the rest of the results are in the supplementary
section. It should be noted that I only measured the climate exposure
rather than a full measure of climatic vulnerability, given I did not
account for species sensitivity and adaptability to climatic change.

\emph{Statistical analysis}

To determine if the range shift (centroid distance: distance between the
current and future centroids) is associated with the amount of current
suitable habitat and elevational distribution, I ran two separate GLMMs
in R using the `glmmTMB' package (Magnusson et al., 2017). To calculate
whether the range shift (centroid distance) is associated with the
current suitability distribution along the elevational gradient, I added
the centroid distance as the response variable and the interaction
between mean elevation (current) and migratory status as the explanatory
variable. I used the interaction of migratory status with the response
variable to account for how migratory species can adjust to the new
habitat faster than the non-migratory species and assess if Bangladeshi
migratory butterflies follow a similar trend. To calculate whether the
range shift is associated with the amount of suitable habitat (current),
I ran a similar model considering the range shift as the response
variable and the interactive effect of the amount of suitable habitat
(current) and species migratory status as the explanatory variable.

In both cases, I used the `nbinom2' family to account for overdispersion
and butterfly families as a random factor. Finally, if any interaction
was significant, I conducted post hoc pairwise comparison tests using
the `emmeans' R package (Lenth et al., 2019).

To assess the vulnerability status of the threatened and non-threatened
species, I ran two separate GLMMs (for centroid distance and elevation
shift) in R using the `glmmTMB' package (Magnusson et al., 2017). To
test whether threatened species experienced more range shift or
elevation shift, I added centroid distance and elevation shift
periodically as the response variables and the threat status as the
explanatory variable. I used the `nbinom2' family to account for
overdispersion and butterfly families as a random factor. Given that
there were negative values in the elevation shift (species for which the
mean elevation declined), before fitting the model, I transformed the
values by adding (1+ maximum negative values) for each species.

\textbf{Results}

I obtained climatic suitability maps for 242 species of Bangladeshi
butterflies (80\% of 305 species) belonging to all the six major
butterfly families (Nymphalidae: 81; Lycaenidae: 66; Hesperiidae: 51;
Pieridae: 21; Papilionidae: 19; and Riodinidae: 3 species).

The fitted current and projected future climate suitability varied
substantially over Bangladesh (Figure 2). The current suitability peaked
in the north-east region, was marginally higher in the centre and some
parts of the south-east regions, whereas the suitability was somewhat
similar in the other parts and lowest in the north-west and southern
parts of Bangladesh (Figure 2a). While the peak suitability of the
current and the future distribution could be similar -- being highest in
the north-east (and east), the future suitability could be evenly
distributed across the country, except for some parts of the south-west
region (Figure 2b).

For both threatened and non-threatened butterflies, the centroid of the
suitability distribution could shift in different directions (Figure
2c). The mean direction of shift in habitat suitability could be 196deg,
and for about 65\% of species (152 species), the centroid could shift
towards the north. There might be no consistent direction of the shift,
although, for most species (60 species; threatened: 31, non-threatened:
29), the suitability could shift towards north-north-west (316deg-359deg)
directions (Figure 2c). For the threatened butterflies, the shift could
occur mostly towards the south-south-east (33 species; 181deg-224deg),
north-north-west (31 species), and the north-north-east (24 species;
1deg-44deg) directions, whereas it could be the north-north-west direction
for the non-threatened species.

In some directions, I obtained striking differences in the climatic
shift between the threatened and non-threatened butterflies. For
example, the suitability could shift in the south-south-west direction
for 33 threatened but three non-threatened butterfly species (Figure
2c). The centroid shift could be similar in the other directions between
the threatened and the non-threatened species.

Overall, compared to the current suitability distribution, there could
be marked variation in the suitability distribution of Bangladeshi
butterflies in the future climatic scenario (Figure 3). The pixels, for
which the suitability might remain the same in the present and the
future climatic conditions, would be distributed in the central and the
eastern part (north-east, east, and southeast), and there might be no
such overlap in most of the northern (mostly north and north-west) and
southern (mostly south and south-west) parts of Bangladesh (Figure 3a).
The pixels for which the suitability range might expand due to climate
change would be distributed along the northeast and southeast, whereas
there might be no range expansion in the central part of Bangladesh
(Figure 3b). In contrast, pixels for which the suitability range could
contract in the future climatic scenario would be primarily distributed
in the central, some parts of the north-east and east, and the southeast
part of Bangladesh (Figure 3c).

In future, there might be no climatically suitable habitat for six
butterfly species currently found in Bangladesh: \emph{Belenois aurota,
Ixias pyrene, Junonia orithya, Tajuria cippus, Virachola Isocrates, and
Aeromachus stigmata} ; five of these species are currently listed
threatened in the national Red List (IUCN Bangladesh, 2015). However, in
other climatic scenarios, there might be no suitable climatic habitat
for many more species: 38 species (16\% species) in ssp245, 59 species
(24\% species) in ssp370, and 73 species (30\% species) in ssp585 (see
the supplementary table S2 for more details).

While comparing the centroid shift in habitat suitability among
Bangladeshi butterflies, the mean shift was larger for non-threatened
butterflies (77km2) than the threatened ones (55km2; Figure 4a). There
were 38 threatened butterfly species, for which the range could shift by
100 km, whereas six non-threatened species could experience such a shift
(Figure 4a). There might be no change in the centroid location for one
butterfly species (\emph{Spindasis elima} ), which is non-threatened.

In the future climatic scenario, the amount of suitable habitat could
change for 241 (threatened: 153 species; non-threatened: 88 species)
Bangladeshi butterfly species, of which 102 species (42\% species) could
experience contraction and 139 species (58\% species) could experience
expansion (Figure 4b). The suitable habitat could contract by
\textgreater{}10,000 km2 for 64 butterfly species {[}threatened: 50
species; non-threatened: 14 species{]} and could expand by
\textgreater{}10,000km2 for 116 species {[}threatened: 44 species,
non-threatened: 72 species{]}. While the suitable habitat might contract
for 58\% of threatened Bangladeshi butterfly species, 16\% of
non-threatened species would experience such a situation. When comparing
migratory and non-migratory species, the climatically suitable area
could increase for most migrants (62\%), while the suitability area
could decrease for most of the non-migrants (55\%; supplementary table
S2).

The mean elevation of the suitable habitat for Bangladeshi butterflies
could increase by 238\% (from 34m to 81m), and the condition could be
more severe for the threatened butterflies (from 42m to 108m) than the
non-threatened species (from 22m to 34m; Figure 4c). While the mean
elevation could increase for 204 species (threatened: 127 species;
non-threatened: 77 species), it could decrease for 30 species
(threatened: 21 species; non-threatened: 9 species). There might be no
change in mean elevation for one species (\emph{Spindasis elima} ).
There were 25 species for which the mean elevation could increase by
\textgreater{}150m, and all these species are threatened (Figure 4c).

The mean overlap of suitable niches in the current and the future
climatic condition of Bangladesh butterflies could be 46\% (Figure 4d),
which means that 54\% of suitable habitats could be altered due to
climate change. Although the amount of niche overlap could be pretty
similar between the threatened and the non-threatened butterfly species,
there might be substantial species-wise differences. For 44 species, the
niche could shift by \textgreater{}75\%, whereas \textless{}25\% niche
shift for 23 species (Figure 4d).

Overall, there might be no significant effect of species elevational
distribution (\selectlanguage{greek}χ\selectlanguage{english}2 = 0.0306, df = 1, p-value = 0.8611) and migratory
status (\selectlanguage{greek}χ\selectlanguage{english}2 = 2.2197, df = 1, p-value = 0.1363), or their interaction (\selectlanguage{greek}χ\selectlanguage{english}2
= 0.1406, df = 1, p-value = 0.7077) on the range shift. In contrast,
species current suitable habitat and their migratory status could have
both direct (\selectlanguage{greek}χ\selectlanguage{english}2 = 7.275, df = 1, p-value = 0.007; and \selectlanguage{greek}χ\selectlanguage{english}2 = 4.1614, df =
1, p-value = 0.04, respectively) and interactive effect (\selectlanguage{greek}χ\selectlanguage{english}2 = 6.3078, df
= 1, p-value = 0.01) on species range shift. Post hoc analysis revealed
that migratory species could experience a larger range shift compared to
the non-migratory species (mean difference= 1.26, p= 0.04).

While comparing the range shift and elevation shift between the
threatened and non-threatened species, I found that threatened species
could experience higher range shift (\selectlanguage{greek}χ\selectlanguage{english}2 = 10.947, df = 1, p-value =
0.0009) and higher elevation shift (\selectlanguage{greek}χ\selectlanguage{english}2 = 46.163, df = 1, p-value =
\textless{}\textless{}0.001) compared to the non-threatened ones.

\textbf{Discussion}

Climate change is considered one of the most critical threats to
biodiversity, yet literature documenting climate change impact on
tropical insects remains scarce (Taheri et al., 2021). Given that
tropics contain enormous insect diversity, understanding climate
vulnerability to tropical species is essential. Here, comparing the
suitability distribution for 242 Bangladeshi butterfly species, I
revealed that climate warming could severely impact many butterfly
species. While the suitability might remain similar in some parts of the
country (e.g., north-east), the future suitability could expand (e.g.,
towards north-east) and contract (e.g., from the central) in other
parts. Although the direction of the shift in climatic suitability could
be multi-dimensional, most species might shift towards the north. This
finding is similar to VanDerWal et al. (2013), who analysed the
distribution changes of 464 Australian bird species and found that both
temperature and precipitation influence changes in species'
distributions, such that the resulting patterns of range shift are
complex. In addition, many Bangladeshi butterflies could shift towards
the north-east and south-east direction, mostly mountainous and forested
habitats, whereas the remaining parts of the country mainly consist of
grasslands (Chowdhury et al., 2021b). This finding supports that i)
there could be a decrease in grassland species and an increase in forest
species (Kwon et al., 2021) and that ii) elevational shifts are more
pronounced than poleward shifts for tropical butterflies (Colwell et
al., 2008; Chen et al., 2009).

Due to climate warming, the suitable habitat could primarily shift for
all the Bangladeshi butterfly species (except for \emph{Spindasis elima}
); however, there could be a striking difference between the threatened
and non-threatened species. While the suitable habitat could increase
for 58\% of species and decrease for 42\%, most of the threatened
species could experience range contraction. The centroid shift for the
threatened species (55km2) could be lower than the non-threatened
species (77km2); therefore, threatened butterfly species could be more
vulnerable to climate change if they cannot adapt to the changing
environment. This finding is similar to Mattila et al. (2011), where
authors analysed the distribution shift among the threatened and
non-threatened Finnish butterflies. Although the range of Finnish
butterflies shifted substantially, threatened species moved very little
(Mattila et al., 2011). Although Finnish threatened butterflies did not
show any consistent direction of the shift (Mattila et al., 2011), I
obtained contrasting result that most threatened Bangladeshi butterflies
could shift toward a south-south-west direction. Additionally, the mean
elevation of Bangladeshi butterflies could increase by about 2.4 times
in the future climatic condition, which could be more extreme for the
threatened butterflies. To safeguard the existential status of
Bangladeshi butterflies, especially threatened species, the conservation
managers, planners, and policymakers should take this into account in
their efforts.

Climate warming will lead many species to move towards higher elevations
to track suitable climatic niches (Walther et al. 2002). Nevertheless,
the ability to track climatic niche differ markedly depending on the
migratory behaviour of the species (Warren et al., 2001; Hill et al.,
2002). By analysing 46 British butterflies, Warren et al. (2001) showed
that the distribution range expanded for half of the mobile and
generalist butterfly species, whereas the other generalists and 89\% of
the habitat specialists declined in distribution size. In another study,
Chowdhury et al. (2021a) showed that while expanding its geographic
range, the migratory tawny coster butterfly maintains its native
climatic niche. Here I found that the amount of suitable habitat for the
migratory species could increase significantly and that migratory
butterflies could experience a more pronounced shift in suitability in
the future climatic conditions comparing to the non-migrants. This
result supports the hypothesis that migratory species perform better in
tracking climatic niches (Warren et al., 2001).

The existing biodiversity is facing an existential crisis due to several
anthropogenic stressors; however, none is as pervasive as climate change
(Halsch et al., 2021). The current climate change has already impacted
27 million km2 (18.3\% of land), which took place in all biomes (Elsen
et al., 2021). Climate change has negatively impacted the distribution
of 47\% of 873 terrestrial non-volant threatened mammal species and
23.4\% of 1.272 threatened birds species (Pacifici et al., 2017). Here,
I showed that the future climate could be unsuitable for 2-30\% of
Bangladeshi butterfly species depending on the climate change scenarios.
If the climate changes at a similar rate -- as it is now, many butterfly
species will go extinct. Wallace's understanding of tropical climate as
``genial'' dismisses the view that tropical climates can be vulnerable
to climate change (Colwell et al., 2008). While safeguarding tropical
insects, several steps need to be taken urgently.

Our current understanding of how climate change impacts tropical insects
is limited: Detailed studies are required to assess climate change
vulnerability (Basset \& Lamarre, 2019; Janzen \& Hallwachs, 2019;
Montgomery et al., 2020; Wilson and Fox, 2021) --- identifying
species-level details on how climate change impacts physiology, life
history and evolutionary traits; diversity, abundance, and distribution.
Understanding the key drivers of species sensitivity and how it varies
among different groups (e.g., threatened and non-threatened; migratory
and non-migratory) is essential, where natural history collections can
provide important historical information on climate change impact
(Scheper et al., 2014; Kharouba et al., 2019; but see Ries et al.,
2019). Further, developing decisive conservation strategies for tropical
insects are needed, where spatial conservation prioritisation approaches
can be beneficial and identifying factors limiting species' capabilities
to colonise areas beyond their historical limits is vital (Lewthwaite et
al., 2018). Developing efficient conservation strategies requires
long-term biodiversity data, yet such data is vastly unavailable from
most tropics. To solve the biodiversity data gap, citizen science
activities can be highly beneficial. A two-week citizen science project,
for instance, could generate comparable spatial coverage for social wasp
species as four decades of recording by expert amateurs (Sumner et al.,
2019; Wilson and Fox, 2020). Assessing climate change's impact on
Bangladeshi butterflies was only possible because of utilising the
citizen science data, given a vast proportion of the collated species
distribution records were from Facebook. Scientists can engage the wider
community to document insect distribution data from the tropics;
however, developing standardised monitoring protocol with detailed
guidelines before organising such surveys.

The climatic niche evolution rate of species is slower compared to the
climate change rate (Quintero \& Wiens, 2013), and failure to track
climatic conditions may lead to range contractions and/or local
extinctions (Moritz \& Agudo, 2013; Wiens, 2016; He et al., 2018).
Besides, climate change is not just a future threat but impacts many
species in the current time (McCarty et al., 2001). Using the
niche-overlapping analysis, I showed that about 54\% of the climatic
niche of Bangladeshi butterflies could shift in the future. Immediately
detecting climate change response is vital to improve (or initiate)
management strategies, counter species loss, and mitigate biodiversity
impacts. Here, specific management strategies should include
prioritising species-specific requirements. For example, butterflies
mostly are tied to specific habitat types, and therefore, to survive,
they will require coordinated movement of climatic conditions habitat
types (including vegetation structure) to track conditions spatially
(Peterson et al., 2004). Further, the adverse effects of climate warming
on pollinators can exacerbate by homogenous and fragmented landscapes,
limiting range shift opportunities and reducing micro-climatic buffering
(Vasiliev and Greenwood, 2021), which emphasises the importance of
considering landscape connectivity and habitat heterogeneity in
conservation planning (Vasiliev and Greenwood, 2021). Similarly,
increasing the number of floral resources could attract diverse insect
groups, creating connectivity between forest patches could support range
expansion, and protected habitat over elevational and vegetational
gradients could help range-restricted species.

Protected area (PA) coverage in the tropics is relatively low. For
example, only 4.6\% of terrestrial land in Bangladesh is now protected,
and most are too small to support viable biota (Chowdhury et al.,
2021f). Increasing PA coverage could insulate species from human-induced
climate change and meet the Post 2020 biodiversity framework targets,
aiming to achieve 30\% PA coverage by 2030 (Convention on Biological
Diversity, 2020). When proposing new PAs, scientists need to include
connectivity planning emphasising climate change impact, ensuring
landscape permeability, creating the network of habitats, future habitat
conditions, and prioritising intact habitats. In tropical mega-populated
countries like Bangladesh, many people live inside or surrounding PAs;
therefore, scientists need to engage local people in conservation.

This is the first study highlighting how climate change could impact the
suitability distribution of South Asian butterflies; however, these
results need to be interpreted cautiously. For example, the spatial
distribution data of Bangladeshi butterflies are highly biased and are
mostly distributed in the major cities (Chowdhury et al., 2021b, f),
which might impact findings. However, I followed two approaches to
handle sampling bias: i) spatial thinning process, where I filtered
single occurrence record from each raster pixel (Aiello-Lammens et al.,
2015), and ii) used the `checkerboard2' evaluation method, which handles
overinflation of model performance, at least that portion resulting from
biased sampling (Muscarella et al., 2014; Chowdhury et al., 2021e).

\textbf{Conclusion}

While human-induced climate change is impacting biodiversity globally in
a dreadful way, literature documenting the impact on tropical species
remains vastly scarce. Using the climatic niche model, I compared the
suitability under different climatic conditions. I showed that the about
42\% species could undergo range contraction in the future climatic
condition. Depending on the climatic scenario (different Socio-Economic
Pathways), the future suitability could be entirely unsuitable for
2-34\% of butterfly species. Rapid detection of how climate change
impacts tropical insects is vital, especially to initiate management
strategies, counter species loss, mitigate impacts on biodiversity,
arrest insect decline globally, and meet the Post 2020 Biodiversity
Framework targets.

\textbf{Acknowledgements}

I thank Dr Upama Aich (Postdoctoral Research Fellow, School of
Biological Sciences, Monash University), Dr Tatsuya Amano (ARC Future
Fellow, School of Biological Sciences, University of Queensland), Dr
Moreno Di Marco (RL Montalcini Research Fellow, Dept of Biology and
Biotechnologies, Sapienza University of Rome), and Dr Myron Zalucki
(Emeritus Professor, School of Biological Sciences, University of
Queensland) for providing important feedbacks in developing the
manuscript.

\textbf{Code availability}

All the R scripts are available in the following public GitHub
repository:
https://github.com/ShawanChowdhury/bd\_butterfly\_clim\_change.

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\textbf{List of Figures}

\textbf{Figure 1.} Map of Bangladesh showing the species occurrence
records (blue) and the distribution of protected areas, where each
`blue' dot is representing one occurrence record. The inset map is
showing the geographic location of Bangladesh in comparison to other
South Asian countries. Some protected areas are difficult to spot
because of their smaller size and too many distribution records around
those areas.

\textbf{Figure 2.} Current (a) and future (b) potential suitability
distribution, and the direction of shift in habitat suitability (c) of
Bangladeshi butterflies. This figure is showing only the results of one
future climatic scenario (ssp126, see the supplementary figure S3 and
table S2 for the other climatic scenarios).

\textbf{Figure 3.} Changes in the suitability distribution of
Bangladeshi butterflies. Here, these maps are showing regions where: the
suitability remained same (a), expanded (b), and contracted (c). For
each map, the colour bar is representing the number of species in each
pixel. This figure is showing only the results of one future climatic
scenario (ssp126, see the supplementary figures S4, S5, and S6 for the
other climatic scenarios).

\textbf{Figure 4.} Changes in current and the future predicted
suitability distribution of Bangladeshi butterflies. Here, a) is showing
the centroid shift, b) is the range shift, and c) is the amount of niche
overlap between the current and the future climatic suitability. This
figure is showing only the results of one future climatic scenario
(ssp126, see the supplementary table S2 for the other climatic
scenarios).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Map of Bangladesh showing the species occurrence
records (blue) and the distribution of protected areas, where each
`blue' dot is representing one occurrence record. The inset map is
showing the geographic location of Bangladesh in comparison to other
South Asian countries. Some protected areas are difficult to spot
because of their smaller size and too many distribution records around
those areas.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Current (a) and future (b) potential suitability
distribution, and the direction of shift in habitat suitability (c) of
Bangladeshi butterflies. This figure is showing only the results of one
future climatic scenario (ssp126, see the supplementary figure S3 and
table S2 for the other climatic scenarios).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Changes in the suitability distribution of
Bangladeshi butterflies. Here, these maps are showing regions where: the
suitability remained same (a), expanded (b), and contracted (c). For
each map, the colour bar is representing the number of species in each
pixel. This figure is showing only the results of one future climatic
scenario (ssp126, see the supplementary figures S4, S5, and S6 for the
other climatic scenarios).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Changes in current and the future predicted
suitability distribution of Bangladeshi butterflies. Here, a) is showing
the centroid shift, b) is the range shift, and c) is the amount of niche
overlap between the current and the future climatic suitability. This
figure is showing only the results of one future climatic scenario
(ssp126, see the supplementary table S2 for the other climatic
scenarios).

\selectlanguage{english}
\FloatBarrier
\end{document}

