Introduction
Fluctuations in weather conditions, particularly precipitation and temperature, are recognised as key drivers of biodiversity patterns across the globe (Bellard et al. Reference Bellard, Bertelsmeier, Leadley, Thuiller and Courchamp2012). These variations impact both ecosystems and organisms, often triggering behavioural and phenological shifts in animal groups as a response to changing climatic conditions (Thackeray et al. Reference Thackeray, Sparks, Frederiksen, Burthe, Bacon, Bell, Botham, Brereton, Bright, Carvalho, Clutton-Brock, Dawson, Edwards, Elliott, Harrington, Johns, Jones, Jones, Leech, Roy, Scott, Smith, Smithers, Winfield and Wanless2010; Peñuelas et al. Reference Peñuelas, Sardans, Estiarte, Ogaya, Carnicer, Coll, Barbeta, Rivas-Ubach, Llusià, Garbulsky, Filella and Jump2013).
A growing body of research (e.g., Hill et al. Reference Hill, Kawahara, Daniels, Bateman and Scheffers2021; Ubach et al. Reference Ubach, Páramo, Prohom and Stefanescu2022; Von Schmalensee et al. Reference Von Schmalensee, Caillault, Gunnarsdóttir, Gotthard and Lehmann2023; Liu et al. Reference Liu, Zhang and Zong2024) demonstrates that butterflies (Lepidoptera: Papilionoidea) exhibit tight ecological relationships with their habitats. As a result, they are highly sensitive to variations in environmental conditions.
Among such conditions, temperature and precipitation have been consistently identified as key determinants of butterfly population dynamics by a number of authors (e.g., Bernardi et al. Reference Bernardi, Garcia, Ely, Zazycki, Bernardi and Finkenauer2011; Ubach et al. Reference Ubach, Páramo, Prohom and Stefanescu2022; Von Schmalensee et al. Reference Von Schmalensee, Caillault, Gunnarsdóttir, Gotthard and Lehmann2023). Consequently, many species exhibit pronounced seasonal patterns, with abundance peaks occurring at specific times of the year that are closely associated with variation in temperature (Zografou et al. Reference Zografou, Swartz, Adamidis, Tilden, McKinney and Sewall2021) and precipitation (Guedes et al. Reference Guedes, Miranda, Soares, Bacci, Antonini and Araújo2024).
Weather and climate variability, particularly variation in precipitation and temperature, are associated with many aspects of butterfly ecology, including distribution, abundance, population size (Bezerra et al. Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021; Hill et al. Reference Hill, Kawahara, Daniels, Bateman and Scheffers2021), habitat use (Dennis and Sparks Reference Dennis and Sparks2006; Martin et al. Reference Martin, Titeux and Van Dyck2021), oviposition site selection (Eilers et al. Reference Eilers, Pettersson and Öckinger2013; Martin et al. Reference Martin, Titeux and Van Dyck2021), and predator-prey cycles (Berryman Reference Berryman1996; Myers and Cory Reference Myers and Cory2013).
However, studies addressing these aspects remain critically insufficient for South America, most notably, for northeastern Brazil. Only one work performed by Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021), evaluating butterfly communities at the Arboretum of Alagoas State, suggested an intrinsic association between butterfly species and fluctuations in both temperature and precipitation patterns. This observation opens avenues for further research on the relationship between butterfly communities and their association with local weather variations.
Although different groups of butterflies may show similar patterns of response, the causal reasons behind these patterns, and the propensity or speed to respond, may differ. Therefore, understanding the response patterns of butterfly families is essential for more accurately estimating the impacts of climatic oscillations on butterfly communities in a given space (Gibbs et al. Reference Gibbs, Wiklund and Van Dyck2011).
Additionally, knowledge of butterfly communities in the Atlantic Forest (hereafter AF) of Northeastern Brazil, particularly in the State of Alagoas, where this study was conducted, remains limited. To date, only a few studies have addressed this order (Lepidoptera: Papilionoidea), notably those by 1983, Cardoso (Reference Cardoso1949) and, more recently, Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021), Melo et al. (Reference Melo, Filgueiras, Iserhard, Iannuzzi, Freitas and Leal2019a) and Tavares et al. (Reference Tavares, Bó, Melo, Freitas, Rosa, Nunes, Kaminski, Carreira, Gueratto, Mota and Barbosa2025). However, only Cardoso (Reference Cardoso1949, Reference Cardoso1983) and Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021) have specifically assessed butterfly communities along the coastline, whereas the remaining studies were conducted further inland.
Hence, given that (1) more than seven decades have elapsed since Cardoso (Reference Cardoso1949) carried out the sole comprehensive survey of Lepidoptera for this region of Alagoas State, and (2) knowledge of how butterfly communities in this area are associated with local weather variability remains limited, this study was designed to document the extant butterfly fauna along the northern coast of Maceió, Alagoas, Brazil and to assess potential associations between butterfly abundance, temperature, and precipitation.
Although no previous study has specifically addressed this question for the region, we initially hypothesised that butterflies occurring in northeastern Brazil would exhibit lower sensitivity to rising temperatures because of the generally higher baseline temperatures and low thermal amplitude of tropical environments.
Materials and methods
Study area
This research was conducted along the northern coast of Maceió, the capital of Alagoas State, Brazil (Figure 1). The area is composed of a mixture of Restinga (along the ocean side, eastwards) and remnants of the Atlantic Rainforest located further inland to the west, interspersed within urbanised areas. Restinga is a type of coastal vegetation mosaic found on sandy marine deposits established on Quaternary sandy substrates of marine origin, characterised by a gradient of plant communities ranging from herbaceous and shrub vegetation to low forests (Marques et al. Reference Marques, Silva and Liebsch2015; MMA 2023).
Map of Brazil (bottom left, in red) showing the State of Alagoas (top left, in red) and the city of Maceió (right, in red). Insets indicate the position of Alagoas within Brazil and South America. Scale bar: 60km.
The sampling events were performed from the neighbourhood of Jacarecica (9.623° S, 35.698° W) to Ipioca (9.486° S, 35.580° W) (Figure 2). According to Köppen’s climate classification, the region’s climate is tropical humid coastal (As), with an average annual precipitation of 1,713 mm (Alvares et al. Reference Alvares, Stape, Sentelhas, Moraes Gonçalves and Sparovek2013). The temperature data for the region of Maceió indicates an annual thermal amplitude of 7 ∼ 8°C. The lowest and highest monthly average temperatures are observed in August (24.6°C) and February (26.5°C), respectively (SETRAND/AL 2015).
Administrative map of the city of Maceió, capital of Alagoas State, Brazil. The region highlighted in blue corresponds to the northern coastal area of the city where sampling was conducted. Three satellite images are included to illustrate the landscape composition at the time of sampling, representing the northernmost (1), central (2), and southernmost (3) portions of the study area. Scale bar: 12km.
The Atlantic Rainforest vegetation of the study area has been devastated over the years due to intense anthropogenic occupation. This process has been intensified in recent decades, resulting in even faster urban expansion, which broadens the northern enlargement of the city of Maceió (Correia-Filho et al. Reference Correia Filho, Barros Santiago, Oliveira-Júnior, Silva Junior, Silva Oliveira, Silva and Teodoro2021). The remaining vegetation on the slopes and plateaus is also heavily influenced by the coastal climate. Predominant maritime winds laden with moisture and salt shape these environments, which are composed mainly of scattered trees and shrubs adapted to sandy, low-fertility and saline soils that are subject to tidal action (SETRAND/AL 2015).
Butterfly sampling
The sampling events were carried out weekly from March 2021 to February 2022. Each session lasted 4 hours, from 8:00 AM to 12:00 PM, taking advantage of the period of highest butterfly activity. Butterflies were collected by a single collector using an entomological net, following pre-existing trails along the vegetation or conducting direct searches to capture these insects, either in flight or when they perched on flowers or leaves in the vegetation.
Specimens were placed in individual entomological envelopes and labelled. Identification was performed both in the Laboratory of Entomology and Arachnology at the Natural History Museum of the Federal University of Alagoas State (MHN/UFAL) and at the Museum of Zoology of the University of São Paulo (MZ/USP).
Species identification
The taxonomic identification of the collected individuals was carried out at the lowest possible taxonomic level through consultations of specialised references, such as the following:
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– Access to the Lepidoptera entomological collection of the Museum of Zoology, University of São Paulo (MZ/USP).
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– Books: Borboletas do Brasil (Palo Jr. Reference Palo2017).
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– Websites: Butterflies of America (Warren et al. Reference Warren, Davis, Stangeland, Pelham, Willmott and Grishin2018) and the Entomology Museum of the Luiz de Queiroz College of Agriculture – ESALQ (Silveira Neto et al. Reference Silveira Neto, Zucchi and Moraes2004).
In addition, the species were further categorised by trophic guild (TG) as: Fruit-feeding (FF) or Nectar-feeding (NF).
Climograph analysis and climatic data collection
Temperature and precipitation data for the study period were obtained from the Brazilian National Institute of Meteorology website (INMET 2022). These data provided monthly records of weather conditions, which were later used for statistical analysis. A climograph was constructed to visually represent the variation in temperature and precipitation over the course of the study period. The climograph was plotted with temperature values on the y-axis and time (months) on the x-axis, while precipitation data were represented as bars, overlaying the temperature curve. This graphical representation helped to illustrate seasonal patterns and the relationship between temperature and precipitation throughout the year, providing valuable context for the ecological analysis of the study area.
Correlation analysis
Pearson correlation tests were applied to quantify linear associations between monthly butterfly abundance and the climatic variables of temperature (°C) and precipitation (mm). The Pearson correlation coefficient (r) was tested for both relationships: (A) butterfly abundance vs. temperature and (B) butterfly abundance vs. precipitation. Statistical significance was assessed at α = 0.05. Given the single annual cycle and small number of monthly observations, these analyses were treated as exploratory summaries of seasonal association.
The strength of correlation was interpreted as follows: 0.1–0.3: Weak correlation. 0.3–0.5: Moderate correlation. >0.5: Strong correlation.
Software and implementation
The correlation analysis was performed in Python using the scipy.stats.pearsonr function from the SciPy library.
Results
Butterfly community
In 144 net-hours of sampling, we recorded 924 individuals representing 92 species and 70 genera, belonging to 18 subfamilies within six butterfly families: Nymphalidae, Pieridae, Hesperiidae, Lycaenidae, Riodinidae, and Papilionidae. The family Hedylidae, recently added to the group of butterfly families (sensu Mitter et al. Reference Mitter, Davis and Cummings2017), was not recorded in this study, likely because of its nocturnal habits and low species richness, which make it an uncommon group among Neotropical butterflies (Kawahara et al. Reference Kawahara, Breinholt, Espeland, Storer, Plotkin, Dexter, Toussaint, St Laurent, Brehm, Vargas, Forero, Pierce and Lohman2018). Only three singletons were observed, meaning species represented by a single individual (Table 1).
List of butterfly species (Lepidoptera: Papilionoidea) found on the northern coast of Maceió City – Alagoas State, Brazil, recorded from March 2021 to February 2022
Table 1. Long description
The table lists butterfly species recorded on the northern coast of Maceió City, Alagoas State, Brazil, from March 2021 to February 2022. It includes 92 species across 18 subfamilies within six butterfly families: Nymphalidae, Pieridae, Hesperiidae, Lycaenidae, Riodinidae, and Papilionidae. The table has 10 columns: Sub-family, Species, and a numerical code. Each row lists a different species with its corresponding sub-family and numerical code. The table is organized by sub-family, with each sub-family containing multiple species. The numerical codes range from 1 to 924.
Read: N : Number of specimens collected during sampling events. TG: Trophic guild.
Throughout the sampling period, notable variation in butterfly abundance was observed, particularly among the families Nymphalidae, Pieridae, Hesperiidae, and Lycaenidae. Both the overall assemblage fluctuation curve (Figure 3A) and the family-specific trends (Figure 3B) revealed one period of population increase (March to July 2021) followed by a subsequent decline (August 2021 to January 2022) in the surveyed area. In contrast, Papilionidae and Riodinidae exhibited relative stability across nearly the entire sampling period.
A: Total population fluctuation and B: abundance fluctuation for each one of the six butterfly (Lepidoptera: Papilionoidea) families of the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022).
One potential factor underlying the observed patterns in population fluctuation is the climatic variations recorded throughout the sampling period. Analyses of mean precipitation indicate that population dynamics closely tracked typical rainfall patterns at the study site (Figure 4B). Population abundance peaked in June, coinciding with the highest recorded precipitation (320.8 mm in June 2021). This was followed by a pronounced decline between August 2021 and January 2022, a period characterised by reduced rainfall, reaching a minimum in December 2021 (30.7 mm). Subsequently, population levels exhibited a gradual recovery beginning in February, in parallel with the increase in rainfall.
Climograph of the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022). B-C: Average precipitation and abundance of butterflies (Lepidoptera: Papilionoidea) (B) and average temperature and abundance of butterflies (C) of the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022).
Figure 4. Long description
Panel A: A combination of a vertical bar graph and a line graph. The bar graph represents average precipitation in millimeters, with the horizontal axis spanning from March 2021 to February 2022. The vertical axis on the left measures precipitation in millimeters, ranging from 0 to 350. The line graph represents average temperature in degrees Celsius, with the vertical axis on the right ranging from 20 to 28 degrees Celsius. The bars show varying precipitation levels, with peaks in May, June, and July 2021. The line graph shows a gradual increase in temperature from March 2021 to February 2022. Panel B: A combination of a vertical bar graph and a line graph. The bar graph represents average precipitation in millimeters, with the horizontal axis spanning from March 2021 to February 2022. The vertical axis on the left measures precipitation in millimeters, ranging from 0 to 350. The line graph represents the total population fluctuation of butterflies, with the vertical axis on the right ranging from 0 to 140. The bars show varying precipitation levels, with peaks in May, June, and July 2021. The line graph shows a peak in butterfly population in June 2021, followed by a decline and a slight increase in February 2022. Panel C: A combination of a line graph and another line graph. The first line graph represents average temperature in degrees Celsius, with the horizontal axis spanning from March 2021 to February 2022. The vertical axis on the left measures temperature in degrees Celsius, ranging from 20 to 28. The second line graph represents the total population fluctuation of butterflies, with the vertical axis on the right ranging from 0 to 140. The first line graph shows a gradual increase in temperature from March 2021 to February 2022. The second line graph shows a peak in butterfly population in June 2021, followed by a decline and a slight increase in February 2022.
Conversely, population fluctuations also appeared to be inversely related to the average temperature recorded in the city during the sampling months (Figure 4C). A continuous decrease in temperature was observed from March 2021 to August 2021, reaching the lowest average of 23°C, followed by a steady increase that peaked at 27.4°C in February 2022. Thus, population fluctuation appeared to be positively associated with precipitation and negatively associated with temperature: declines coincided with reduced precipitation and rising temperatures, whereas increases coincided with higher humidity and lower temperatures. These hypotheses will be further discussed in the coming sections.
Correlation analysis
We used Pearson’s correlation analysis to quantify associations between monthly butterfly abundance and climatic variables (temperature and precipitation). Except for Riodinidae, which showed a weak and non-significant negative relationship (Pearson’s r = −0.24, p < 0.45), all families exhibited moderate to strong negative correlations with temperature. Family-level analyses were treated as exploratory because of the small number of monthly observations and the lack of correction for multiple comparisons.
Our results suggest that increasing temperatures are associated with a decline in butterfly abundance within the studied area. Nymphalidae, Lycaenidae, and Pieridae exhibited strong negative correlations (r ≤ −0.70), with Pieridae showing the strongest association (Pearson’s r = −0.83, p < 0.0008). In contrast, Hesperiidae and Papilionidae showed comparatively weaker, though still significant, negative relationships (r = −0.65, p < 0.0221; r = −0.69, p < 0.0130, respectively) (Figure 5).
Correlation analysis between the butterfly (Lepidoptera: Papilionoidea) population fluctuation and the average temperature in the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022).
Analysis of the relationship between butterfly families and precipitation revealed consistently strong positive correlations across all six families (r ≥ 0.70). Among them, Hesperiidae had the strongest correlation (r = 0.93, p < 0.0001), suggesting that increased precipitation levels are associated with higher butterfly abundance (Figure 6).
Correlation analysis between the butterfly (Lepidoptera: Papilionoidea) population fluctuation and the average precipitation in the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022).
Across all families combined, the analysis was consistent with these patterns. A pronounced negative relationship was observed between butterfly population fluctuations and temperature (Pearson’s r = −0.72, p < 0.0083), whereas population fluctuations were positively correlated with precipitation (Pearson’s r = 0.88, p < 0.0002) (Figure 7A and B).
Correlation analysis between the butterfly (Lepidoptera: Papilionoidea) population fluctuation and the average temperature and B: average precipitation in the northern coast of Maceió, Alagoas State, Brazil, during one year of sampling events (March 2021 – February 2022).
Discussion
Richness & diversity
This study documented the remaining butterfly fauna of a degraded AF remnant on the northern coast of Maceió, Alagoas State, Brazil, and assessed its association with local climatic oscillations. The species diversity found in this study was considerably higher than previously documented by Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021), who surveyed butterfly communities at the Arboretum of Alagoas State, an urban green area strongly influenced by anthropic activities, and recorded only 44 species. This lower number may be attributed not only to anthropic influence but also to the small size of the evaluated area (4.2 hectares) and the shorter sampling period (seven months) compared with this study.
The species richness found with our sampling efforts was also lower than what was documented by other recent work in the AF remnants of Alagoas State for fruit-feeding assemblages, for instance, Melo et al. (Reference Melo, Filgueiras, Iserhard, Iannuzzi, Freitas and Leal2019a), who centred their analysis on an old and fragmented AF landscape, comprising the coastal forests to the north of the São Francisco River. Melo et al. (Reference Melo, Filgueiras, Iserhard, Iannuzzi, Freitas and Leal2019a) found 61 species of fruit-feeding butterflies belonging to four Nymphalidae subfamilies, whereas we found as few as 21 species in this guild.
Consistent with our results, studies assessing butterfly fauna in AF remnants that are heavily influenced by urban areas in northeastern Brazil, have reported significantly reduced species richness. These numbers were as low as 89 species reported by Medeiros et al. (Reference Medeiros, Gualberto, Rodrigues and Kerpel2021) and 40 species recorded by Guedes et al. (Reference Guedes, Miranda, Soares, Bacci, Antonini and Araújo2024). To explain this reduced richness, Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021); Guedes et al. (Reference Guedes, Miranda, Soares, Bacci, Antonini and Araújo2024); and Santos et al. (Reference Santos, Sobral-Souza, Brown, Vancine, Ribeiro and Freitas2020) suggest that butterflies are strongly affected by human disturbance, with habitat modification, fragmentation, and degradation emerging as the primary drivers of species loss and changes in assemblage structure.
This disparity likely arises because anthropogenic influence negatively affects butterfly species richness, often leading to local extinctions of species more sensitive to modifications in their native habitats (Ramírez-Restrepo and MacGregor-Fors Reference Ramírez-Restrepo and MacGregor-Fors2017). Moreover, other factors commonly observed in landscapes within or surrounded by anthropic zones, such as vegetation homogenisation and reduced resource availability, directly affect butterfly communities and can significantly reduce species richness in these locations (Habel et al. Reference Habel, Schmitt, Ulrich, Gros, Salcher and Teucher2023).
However, comparably low species richness has not been reported in studies assessing AF remnants situated within or adjacent to urban areas, with values exceeding 200 species (see, e.g., Paluch et al. Reference Paluch, Mielke, Linhares and Silva2016; Melo et al. Reference Melo, Duarte, Mielke, Robbins and Freitas2019b; Vila-Verde and Paluch, Reference Vila-Verde and Paluch2020; Gualberto et al. Reference Gualberto, Medeiros and Kerpel2024). Species richness reported in these studies is sometimes three-fold higher than that observed in urban-influenced environments. Although butterfly communities appear to be strongly affected by anthropogenic disturbance, we suggest that they tend to remain relatively stable in large remnant areas not directly impacted by human activities.
Climatic responses
Butterflies are widely recognised for their sensitivity to fluctuations in climate patterns (Zografou et al. Reference Zografou, Swartz, Adamidis, Tilden, McKinney and Sewall2021; Ubach et al. Reference Ubach, Páramo, Prohom and Stefanescu2022). For decades, they have served as a model group for studying the effects of weather and climate change on insect populations (see e.g., Parmesan Reference Parmesan2007; Devictor et al. Reference Devictor, Van Swaay, Brereton, Brotons, Chamberlain, Heliölä, Herrando, Julliard, Kuussaari, Lindström, Reif, Roy, Schweiger, Settele, Stefanescu, Van Strien, Van Turnhout, Vermouzek, Wallis De Vries, Wynhoff and Jiguet2012; Ubach et al. Reference Ubach, Páramo, Prohom and Stefanescu2022; Liu et al. Reference Liu, Zhang and Zong2024). Numerous approaches have demonstrated how annual population fluctuations are influenced by climatic conditions (e.g., Gibbs et al. Reference Gibbs, Wiklund and Van Dyck2011; Boggs and Inouye Reference Boggs and Inouye2012; Checa et al. Reference Checa, Rodriguez, Willmott and Liger2014, Reference Checa, Levy, Rodriguez and Willmott2019; Grøtan et al. Reference Grøtan, Lande, Chacon and DeVries2014; Bezerra et al. Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021) and how such variations can lead to population declines or outbreaks (e.g., McDermott Long et al. Reference McDermott Long, Warren, Price, Brereton, Botham and Franco2017; Checa et al. Reference Checa, Levy, Rodriguez and Willmott2019; Bezerra et al. Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021; Zografou et al. Reference Zografou, Swartz, Adamidis, Tilden, McKinney and Sewall2021; Ubach et al. Reference Ubach, Páramo, Prohom and Stefanescu2022).
Our results are consistent with the aforementioned studies in suggesting an association between butterfly communities and climatic oscillations in the studied area. These findings also align with patterns previously suggested by Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021), who examined butterfly communities in a nearby area (15–20 km away), the Arboretum of Alagoas State, and identified associations between butterfly abundance and fluctuations in both temperature and precipitation. Bezerra et al. (Reference Bezerra, Gonzaga, Dalla Lana, Breda and Duarte2021) described variation in butterfly abundance as directly correlated with precipitation and inversely correlated with temperature. Declines in butterfly abundance across all families were recorded from November to January as temperatures increased and precipitation dropped sharply. The inverse scenario was observed from January to March as temperatures decreased and rainfall increased.
Similarly, butterfly communities along the coast seem to follow the same pattern. Our results indicate a strong positive correlation with precipitation for the entire assemblage and across family-specific populations (overall Pearson’s r = 0.88, Figure 7), suggesting that high rainfall volumes are associated with greater butterfly abundance on the northern coast of Maceió. The rainfall range recorded in this study was 292.1 mm, from 30.7 mm in December to 320.8 mm in June, and this variation coincided with substantial changes in butterfly abundance.
Relationships between rainfall and butterfly communities vary broadly depending on forest characteristics and species composition. In tropical areas, some studies have reported abundance peaks during the wet season, although this pattern can vary (Valtonen et al. Reference Valtonen, Molleman, Chapman, Carey, Ayres and Roininen2013). In northeastern Brazil, butterfly abundance peaks during months of high precipitation may be linked to increased availability of floral resources and other food supplies as a result of higher rainfall incidence. These conditions may reduce competition and facilitate species coexistence within a given area (Ivanov et al. Reference Ivanov, Oliveira, Leite, Vale, Karasinski and Fernandes2022). Furthermore, studies conducted by Checa et al. (Reference Checa, Levy, Rodriguez and Willmott2019) indicate that butterflies tend to adjust critical stages of their life cycle to align with more favourable conditions for survival. A similar pattern may occur along the coastline of Maceió, where we hypothesise that butterflies synchronise the peak of adult abundance with the wet season to take advantage of increased food resource availability.
Our results also indicate a decrease in butterfly populations from September to January as conditions became drier and warmer, followed by population growth from January to August in response to increased rainfall and lower temperatures. The correlation analyses performed in this study further support this pattern, revealing a strong negative relationship between overall butterfly abundance and temperature (overall Pearson’s r = −0.72, Figure 7), suggesting that abundance decreases as temperatures rise. This relationship varied among families; Riodinidae, for instance, had a very weak negative correlation with temperature (r = −0.24). Notably, the temperature variation of just 4°C (23°C to 27°C) at the study site coincided with a decline in species abundance.
The negative correlation between temperature and butterfly abundance may be related to physiological characteristics. Among animals, insects (ectotherms) typically exhibit more pronounced and rapid responses to temperature variation compared to endotherms (Devictor et al. Reference Devictor, Van Swaay, Brereton, Brotons, Chamberlain, Heliölä, Herrando, Julliard, Kuussaari, Lindström, Reif, Roy, Schweiger, Settele, Stefanescu, Van Strien, Van Turnhout, Vermouzek, Wallis De Vries, Wynhoff and Jiguet2012), likely due to the direct influence of rising temperatures on their metabolic rates, activity patterns, and developmental processes (Zografou et al. Reference Zografou, Swartz, Adamidis, Tilden, McKinney and Sewall2021).
Although we initially hypothesised that butterflies occurring in the AF of Northeastern Brazil might exhibit less sensitivity to rising temperatures due to the tropical context, our findings suggest a more complex scenario. While tropical regions are characterised by high mean temperatures and low thermal amplitude, extensive empirical evidence indicates that such climatic stability is associated with narrower thermal safety margins and heightened susceptibility to warming (see, e.g., Deutsch et al. Reference Deutsch, Tewksbury, Huey, Sheldon, Ghalambor, Haak and Martin2008; Sunday et al. Reference Sunday, Bates, Kearney, Colwell, Dulvy, Longino and Huey2014; García-Robledo et al. Reference García-Robledo, Kuprewicz, Staines, Erwin and Kress2016; Shah et al. Reference Shah, Gill, Encalada, Flecker, Funk, Guayasamin, Kondratieff, Poff, Thomas, Zamudio and Ghalambor2017; Polato et al. Reference Polato, Gill, Shah, Gray, Casner, Barthelet, Messer, Simmons, Guayasamin, Encalada, Kondratieff, Flecker, Thomas, Ghalambor, Poff, Funk and Zamudio2018). In fact, climatological data for the northern coast of the Alagoas State indicate a relatively narrow annual thermal amplitude of only 7°C. The lowest and highest mean monthly temperatures occur in August (24.6°C) and February (26.5°C), respectively (SETRAND/AL 2015), suggesting a relatively stable thermal regime throughout the year to which butterfly species in this area are potentially synchronised.
Nevertheless, certain taxa possessing behavioural thermoregulatory strategies or occupying localised microrefugia may exhibit comparatively reduced sensitivity to temperature increase (Deutsch et al. Reference Deutsch, Tewksbury, Huey, Sheldon, Ghalambor, Haak and Martin2008; Sunday et al. Reference Sunday, Bates, Kearney, Colwell, Dulvy, Longino and Huey2014; García-Robledo et al. Reference García-Robledo, Kuprewicz, Staines, Erwin and Kress2016; Polato et al. Reference Polato, Gill, Shah, Gray, Casner, Barthelet, Messer, Simmons, Guayasamin, Encalada, Kondratieff, Flecker, Thomas, Ghalambor, Poff, Funk and Zamudio2018). Therefore, future research should prioritise assessing species-specific responses to weather fluctuations. A key concern regarding the correlation with rainfall patterns is how potential shifts in precipitation regimes driven by global change could impact butterfly life cycles and the phenological responses they trigger.
Understanding variation in phenological response is important for conservation because phenological changes may determine which species are harmed by environmental change. Potential mechanisms include trophic mismatches, such as desynchronisation with host plants (Schweiger et al. Reference Schweiger, Settele, Kudrna, Klotz and Kühn2008), insufficient time to complete a final generation (the ‘lost generation’ hypothesis; Glazaczow et al. Reference Glazaczow, Orwin and Bogdziewicz2016; Van der Kolk et al. Reference Van der Kolk, WallisDeVries and Van Vliet2016), or limitations on species’ capacities to mitigate extreme temperatures or drought through aestivation or diapause (Dapporto et al. Reference Dapporto, Cini, Menchetti, Vodă, Bonelli, Casacci, Dincă, Scalercio, Hinojosa, Biermann, Forbicioni, Mazzantini, Venturi, Zanichelli, Balletto, Shreeve, Dennis and Vila2017). Conversely, some species may respond rapidly and effectively to environmental change by modifying phenology to correspond to changing climatic conditions (Visser Reference Visser2008; Kharouba et al. Reference Kharouba, Paquette, Kerr and Vellend2014; Zografou et al. Reference Zografou, Kati, Grill, Wilson, Tzirkalli, Pamperis and Halley2014; Roy et al. Reference Roy, Oliver, Botham, Beckmann, Brereton, Dennis, Harrower, Phillimore and Thomas2015).
Nonetheless, phenological changes in response to climate change are far from uniform (Devictor et al. Reference Devictor, Van Swaay, Brereton, Brotons, Chamberlain, Heliölä, Herrando, Julliard, Kuussaari, Lindström, Reif, Roy, Schweiger, Settele, Stefanescu, Van Strien, Van Turnhout, Vermouzek, Wallis De Vries, Wynhoff and Jiguet2012), in which opposing patterns might occur even at local scales (Checa et al. Reference Checa, Levy, Rodriguez and Willmott2019) and can vary in magnitude and direction even among species experiencing similar environmental conditions (Diez et al. Reference Diez, Ibáñez, Miller-Rushing, Mazer, Crimmins, Crimmins, Bertelsen and Inouye2012).
Because this study covers a single annual cycle, the correlations reported here should be interpreted as initial evidence of seasonal association rather than causal estimates of independent climatic effects. Temperature and precipitation covary seasonally in the study region, and other unmeasured factors, including host-plant phenology, floral resource availability, detectability, and land-use heterogeneity, may also contribute to the observed abundance patterns. Future studies using multi-year sampling and models that jointly evaluate climatic variables will be necessary to separate these effects more rigorously.
Conclusions
Over one year of sampling efforts along the northern coast of Maceió City, Alagoas State, Brazil, we recorded 92 butterfly species, representing the remnants of a community that has withstood decades of anthropogenic influence.
This study found a strong association between butterfly abundance and both temperature and precipitation in the evaluated area, reinforcing previous research on butterfly communities in a nearby region. Correlation analysis revealed a positive relationship between butterfly abundance and precipitation, while temperature exhibited a negative association. These findings suggest that butterfly abundance in the evaluated region increases with higher precipitation and decreases as temperatures rise.
Future research should prioritise assessing species-specific responses to weather fluctuations. A critical implication of this relationship is that ongoing changes in precipitation regimes driven by global change may profoundly influence butterfly life cycles and the phenological responses that determine species’ vulnerability and resilience. Given that these insects may synchronise aspects of their life cycles with annual precipitation patterns, significant alteration of rainfall regimes could disrupt developmental timing and resource availability.
Acknowledgements
We would like to express our gratitude to the Natural History Museum of the Federal University of Alagoas for providing the necessary tools and equipment essential for the successful execution of this project. We also extend our thanks to the Laboratory of Agricultural and Forest Entomology (LEAF) at the Federal University of Alagoas State for providing additional tools and equipment crucial for the development of this work.
Our sincere appreciation goes to the Zoology Museum of São Paulo University (MZ/USP) and especially to Prof. Dr Marcelo Duarte da Silva for granting us access to the Lepidoptera collection, which was invaluable for consulting and comparing specimens to accurately identify the species involved in this study. Our gratitude goes also to Renato de Oliveira e Silva for his essential help in identifying the specimens.
Financial support
Lucas Augusto Kaminski acknowledges support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 175508/2023-2) and Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL APQ2022021000087).
No specific funding was received for this research by the remaining authors.
Competing interests
The authors declare that they have no competing interests.
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