Vulnerability of Mnesarete williamsoni (Insecta: Zygoptera) to climate change and loss vegetation cover

doi:10.21203/rs.3.rs-7828353/v1

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Vulnerability of Mnesarete williamsoni (Insecta: Zygoptera) to climate change and loss vegetation cover

https://doi.org/10.21203/rs.3.rs-7828353/v1

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Climate change and land-use alterations pose a significant threat to biodiversity, particularly for specialist species. The Amazonian damselfly Mnesarete williamsoni, a forest-dependent species with low dispersal capacity, serves as a suitable model to investigate these impacts due to its strong ecological association with well-preserved forest streams. This study aimed to model the potential distribution of M. williamsoni across three temporal scales: the Last Glacial Maximum (LGM), the present, and a future scenario (2041–2060), testing the hypothesis that climate change and habitat loss will drastically reduce its suitable niche. We employed an ensemble modelling approach that combined Maxent, Random Forest, and Support Vector Machine (SVM) algorithms. Models were calibrated with occurrence records and climatic variables for past, present, and future scenarios (CCSM4 model, RCP 8.5), projected across South America. Our models revealed fluctuations in suitability in habitat. During the LGM, suitable distribution was restricted, suggesting confinement to forest refugia. In the current scenario, a significant expansion of suitable habitat has been observed, mainly within the Belém Endemism Centre. However, future projections indicate a severe contraction, even greater than the restrictions observed during the glacial period, driven by climate change and loss of forest cover. The results highlight the vulnerability of M. williamsoni to anthropogenic disturbances. The projected future contraction emphasizes the urgent need for conservation actions aimed at preserving and restoring forest and riparian corridors in the Belém Center of Endemism. This study reinforces the value of specialist bioindicator species as tools to guide conservation policies and mitigate the impacts of global changes on Amazonian aquatic ecosystems.

Species distribution modeling

Climate change

Bioindicator

Odonata

Amazon

Understanding how species respond to climate change represents one of the greatest challenges for conservation (Cahill et al. 2013; Aguirre-Liguori et al. 2021). The interaction between climate change and land-use alterationscan lead many species to become maladapted to local conditions, forcing them to live outside their optimal climatic niches (Bellard et al. 2012; Gougherty et al. 2021; Pereira et al. 2024). In theory, optimal environmental conditions may shift geographically as the climate changes; however, not all populations can track these shifts (Schloss et al. 2012; Antão et al. 2022). Species persistence depends on their dispersal ability at a rate equal to or faster than the pace of climate change (Carroll et al. 2015; Choi and Guo 2021), which requires the presence of ecological corridors or permeable landscapes (Lawler et al. 2013; Liang and Li 2023). In this context, species distribution models (SDMs) are fundamental tools, as they enable the estimation of ecological niches under conditions altered by climate change or human activities (Lembrechts and Lenoir, 2018; Santini et al., 2020).

To understand and predict these responses, it is also essential to consider the historical processes that shaped species distributions. In tropical ecosystems, the Pleistocene (2.6 million to 10,000 years ago) was characterised by glacial and interglacial cycles that left legacies still visible in present-day biodiversity, such as the structure of tropical rainforests (Leite 2015; Couvreur et al. 2020; Fonseca et al. 2023). The Last Glacial Maximum (LGM), in particular, transformed landscapes, reduced habitats, and imposed physiological pressures on several species (Bueno et al. 2017; Puli et al. 2024). Paleoecological evidence suggests that Quaternary climatic oscillations fragmented communities, favoured the formation of refugia, and triggered ecological and reproductive isolation processes, ultimately leading to the endemism and diversity currently observed (Haffer 2008; Costa et al. 2018).

The landscape, therefore, plays a central role in determining species’ richness and composition. Aquatic invertebrates, for instance, respond strongly to variations in temperature and humidity, which shape hydrological networks and select species with greater adaptive capacity (Callisto et al. 2001; De Marco et al. 2002; De Paul et al. 2024). Environmental impacts can alter species' behaviour, but the repercussions tend to be less severe for those that are able to adapt or migrate (Poff et al. 2002; Woodward et al. 2010; Bernatchez et al. 2023). However, human intervention in migratory corridors restricts dispersal and increases the risk of extinction (Hadadd et al. 2015; Bernatchez et al. 2023). In this scenario, Amazonian dragonflies, highly sensitive to climate variability and forest cover, stand out as bioindicators of environmental disturbances (Carvalho et al. 2018).

Among them, Mnesarete williamsoni (Garrison 2006) is considered a forest specialist, restricted to primary forest streams, with low dispersal capacity and strong selectivity for preserved habitats (Carvalho et al. 2018; Carvalho 2019). Its presence is associated with reproductive and feeding behaviours favoured in conserved environments (De Marco et al. 2015). Furthermore, the species reflects historical climate responses, adjusting its spatiotemporal patterns to Quaternary glacial cycles (Ribas et al. 2019; Haffer 2008). During this period, much of biodiversity remained isolated in forest fragments, which may explain current trends of micro endemism and vulnerability to extinction (Urban 2024).

In light of this, the present study evaluates the distribution of M. williamsoni from the Last Glacial Maximum to the present and projects its future spatial trend. We assume that the species is strongly associated with the riparian vegetation of rivers and streams, selecting humid niches with high vegetation cover (Juen et al., 2016; He et al., 2023). Our hypotheses are: i) during the LGM, suitable niches were limited, restricting populations to small forest refugia; ii) the current scenario is favorable, due to climatic and landscape conditions; and iii) in the future, climate change and the loss of native vegetation will once again reduce suitable niches, threatening the species’ survival.

Study Area

The study area encompasses South America, a continent with remarkable climatic and topographic diversity, ranging from humid tropical forests to arid and savanna regions (Delgado et al. 2022). This environmental heterogeneity directly influences species distribution, creating suitable conditions for historical processes of speciation, isolation, and endemism (Wei et al. 2025). Moreover, South America was strongly affected by Quaternary climatic oscillations, whose effects shaped present biodiversity, particularly in ecologically significant ecosystems such as the Amazon (Silva et al. 2018).

The Amazon, the largest tropical rainforest on the planet, covers approximately 5 million km², of which about 60% lies within Brazilian territory, distributed across nine states and 553 municipalities (Fearnside 1995). It is characterized byan extensive hydrographic network formed bymajor rivers thatdefine interfluves and, consequently, centers of endemism (Silva et al. 2005). The biome is recognized for its megadiversity but is currentlysubject to intense anthropogenic pressure. It is estimated that more than 20% of its area has already been deforested, mainly in the so-called “arc of deforestation,” a region marked by agricultural expansion, cattle ranching, mining, and infrastructure development (Almeida et al. 2010; Moraes 2019). Forest degradation, combined with the effects of climate change, threatens not only local biodiversity but also global climate balance, making the Amazon a strategic setting for species distribution modeling studies.

Within this context, the Belém center of endemism stands out, located in eastern Pará (near the Tocantins River) and western Maranhão (near the Pindaré River). It is the smallest center of endemism in the Amazon, with an area of approximately 243,000 km², and is considered one of the most threatened. More than 76% of its forest cover is already under severe deforestation pressure (Almeida et al. 2010; Moraes 2019). This center of endemism is particularly relevant because it harbors the species M. williamsoni (Garrison 2006), a damselfly specialized in primary forest environments. The occurrence of this species in the region reflects not only historical isolation processes but also its current vulnerability to anthropogenic pressures, justifying its selection as a model for evaluating historical, current, and future distribution patterns.

Occurrence records

The damselfly Mnesarete williamsoni (Garrison 2006) was selected as the target species due to its strong ecological association with riparian vegetation in primary forest streams (Carvalho et al. 2018; Carvalho 2019). This specialist behavior makes it highly sensitive to environmental changes, particularly those related to climate variability and land-use conversion, such as deforestation and forest degradation (Carvalho et al. 2018; Carvalho 2019). As a result, M. williamsoni is considered an effective bioindicator of preserved ecosystems, serving as a reliable predictor of environmental changes in both climatic and landscape contexts (Sagova-Mareckova et al. 2020). In addition, it is a taxonomically stable species, with no synonymy conflicts, and it is relatively easy to sample and identify in the field (Carvalho et al. 2018; Carvalho 2019), which enhances its applicability in ecological modeling.

To build the occurrence dataset, we corroborated the taxonomic identity of the target species. We compiled presence records from personal databases, complemented by secondary data obtained from the Global Biodiversity Information Facility (GBIF.org, 2022: https://gbif.org). All available presence records of M. williamsoni were retrieved without filtering by collection date, using the download link provided by the GBIF platform (DOI:10.5962/p.210560).

We applied a geographical mask encompassing the entire South American continent, and using QGIS (version 3.14; Quantum GIS Geographic Information System; QGIS.org 2020) and the R platform (R Core Team 2020), we filtered thepresence-only occurrence dataset Records lacking geographic coordinates, duplicates, erroneous entries, or those located outside the study area were excluded. To minimize potential bias caused by spatial autocorrelation (SAC), we applied a thinning procedure, retaining only one occurrence per grid cell. Finally, to pre-assess the spatial patterns of the species distribution, we used the Epanechnikov kernel density function (KDE) through the QGIS “Heatmap” extension, with a bandwidth of 200,000 km.

Environmental predictor variables

From the WorldClim platform (https://worldclim.org: Hijmans et al. 2005), we extracted 19 climate variables that allowed us to represent current climate dynamics (CURRENT: 1960–1990). For past and future climate scenarios, we selected global circulation models (CCSM4, MPI-ESM-P, and MIROC-ESM) to characterize past (LGM: ~22,000 years) and future (FUTURE: 2041–2060) climates scenarios. We selected these three global circulation models for their robustness and compatibility for the LGM and for the future, allowing for fidelity and correspondence when comparing predictions. For future climate projections, we selected the most extreme scenario for continued greenhouse gas emissions (Representative Carbon Pathway, RCP = 8.5). RCP 8.5 was adopted by the Intergovernmental Panel on Climate Change in 2004 and represents the most pessimistic and likely scenario for projections to the year 2100 (Frick et al. 2020; Schwalm et al. 2020; Ritchie et al. 2021). We selected the predictor variables for all climate scenarios with a spatial resolution of 5 arc minutes (~ 10 km at the equator), in order not to compromise data processing capacity and to ensure a correct representation of regional climate dynamics (Barve et al., 2011).

To avoid unequal weights between the predictor variables, from the R platform and using the 'maps' (Becker and Wilks 1993), 'rgdal' (Keitt 2010) and 'raster' (Hijmans 2015) packages, we standardized the predictor variables (z ~ Normal distribution: zero mean and standard deviation of |1|). We delimited the modelling area with a South American geographic extension to incorporate all potential climatic zones for the presence and distribution of the target species (see M Barve et al. 2011). From a cut mask with a South American extension and using the R platform, we extracted all predictor variables using the 'raster' and 'SDMTools' packages (Vanderwal et al., 2014). Finally, we performed a principal component analysis (PCA) to reduce the collinear effects of the predictor variables (De Marco and Nóbrega 2018). We chose only the first eight axes that explained more than 95% of the original environmental variation of the predictor variables considered.

Processing and evaluation of models

We applied an ensemble model approach, where the combination of different algorithms improves the approximation to the maximum and minimum thresholds of tolerance and the distribution of species, allowing better approximations to their niches (Allouche et al. 2006; Barve et al. 2011; Grenouillet et al. 2011; Alvarez et al. 2021). From the configuration of the 'ENMTML' package (from Andrade et al. 2020: https://github.com/andrefaa/ENMTML) and based on the performance of the algorithms and the flexibility with respect to input data (Elith et al. 2006), we selected the algorithms Maxent (Maximum Entropy - MXS: Phillips et al. 2006), Random Forest (RF: James et al. 2013) and Support Vector Machine (SVM: Salcedo-Sanz et al. 2014). We defined the pseudo-absences as environmentally random, spatially non-overlapping, and not coinciding with the presence-only data of the target species. We divided the presence-only logs into 70% for use as test data and 30% for use as training data. We configured MXS using linear features and with default parameterization, a thousand interactions, 10 thousand background points, and logistic-like output (Elith et al. 2006). We defined the RF parameters on 500 trees, using automatic selection for both the fit and the algorithm (Hastie et al., 2009). We configured SVM by default, using Kernel's linear fit function with probabilistic output. Following de Andrade et al. (2020), we apply ENMTML and run the different SDMs from the R platform, using the ‘dismo’ (Hijmans et al. 2017), 'randomForest' (Liaw and Wiener 2002) and ‘kernlab’ (Karatzoglou et al. 2004) packages.

We selected a randomization process for 30 replicates for each algorithm (self-initialization analysis) to reduce possible correlation errors, avoid biased evaluations, and increase the statistical power of the predictions. We cut continuous predictions using the threshold where Sorensen values are greatest. Sorensen performs better since the index values remain similar regardless of the species prevalence; therefore, it is a more appropriate metric of the model's discrimination ability (Leroy et al. 2018). To identify and define the presence/absence of the target species in the study area, we transformed continuous predictions into binary ones (Barve et al. 2011; Grenouillet et al. 2011). We concatenated the binary predictions, using the weighted average consensus, considering all predictions with Sorensen values above the average value. That is, we used the threshold that maximizes the sum of the sensitivity and specificity to transform continuous models (current or forecasted) into a binary (presence-absence) model (Velazco et al. 2019).

The model calibrated using only the current climate, estimated a potential current distribution consistent with the known distribution of M. williamsoni, suggesting that the species has a limited habitat in the Brazilian Amazon. Where the population is concentrated in the center of the Belém endemism and a small suitable area in the center of the Xingu endemism, both in the eastern Amazon (11,529 cells). Differences between the extrapolation methods were also evident in the three scenarios (LGM, CURRENT, and FUTURE). Our result predicted a difference in pixels between LGM and CURRENT of 9.713 cells for CURRENT. However, when we compare CURRENT and FUTURE, the difference was even larger at 10,311 cells for CURRENT. When we evaluate the difference in cells between the LGM and FUTURE scenarios, the difference decreases significantly, even though the FUTURE scenario is the most critical for the species (Fig. 1).

The model calibrated using current conditions was also largely consistent with current land cover. Our results suggest that the population of M. williamsoni has a close relationship with the native vegetation of the endemic center of Belém. In addition, it was possible to see that the species has a very strong relationship with wetlands, because the Tocantins River crosses the binary suitability of this species, which may be due to two aspects: the strong relationship that the species has with riparian vegetation, and also because it is associated with the wettest environments.

Table

LGM
Parameter	Estimate	SE	P
Temperature Seasonality	-0.028	0.013	0.01
Precipitation Seasonality	-0.137	0.083	0.05
CURRENT
Mean Temperature of Coldest Quarter	0.750	0.428	0.05
Annual Mean Temperature	-0.799	0.469	0.05
FUTURE
Mean Temperature of Coldest Quarter	0.750	0.428	0.05
Annual Mean Temperature	-0.799	0.469	0.05

Our results reinforce that Mnesarete williamsoni is an endemic species of the Belém Endemism Center and is highly specialized in forest environments, particularly in streams within primary forests. This dependence on well-preserved habitats, combined with its low dispersal capacity, explains the species’ high vulnerability to environmental changes (Carvalho et al. 2018; Brito et al. 2024). Ecological specialization, although favoring its suitability in specific environments, drastically reduces its plasticity under changing scenarios, making the species both an accurate indicator of ecosystem health and highly susceptible to degradation processes (Colles et al. 2009; Goldiner et al. 2023). Specialist species such as M. williamsoni have fewer adjustment alternatives when their habitats are modified, reinforcing the urgency of targeted conservation strategies.

Spatial projections for the Last Glacial Maximum (LGM), the current, and future scenarios reveal important fluctuations in habitat suitability. During the LGM, cooling and warming cycles imposed unfavorable environmental conditions, restricting the species to low-amplitude niches and possibly to isolated forest refuges. This historical pattern helps explain the microendemism currently observed in the Amazon, resulting from isolation processes in climatic refugia (Arruda et al. 2018; Cavalcante et al. 2024). In the present, however, we have observed an expansion of climatic and ecological suitability, reflecting more stable conditions that are compatible with the species’ requirements, allowing for the maintenance and even expansion of populations in preserved forest areas. Nevertheless, future scenarios indicate significant contractions in suitable areas, driven both by projected climate change and ongoing vegetation loss. This contrast reveals that, although the present represents a moment of greater suitability, the future projects a critical scenario of population decline, with a high risk of local extinction unless mitigation actions are taken.

The strong association of the species with humid areas and riparian forests highlights its importance as a bioindicator of environmental quality (Juen et al. 2014; Oliveira-Junior et al. 2017). The presence of M. williamsoni directly reflects the integrity of riparian forests, which are crucial for regulating microclimates, stabilising soils, and maintaining water quality. The loss of these environments triggers cascading impacts, reducing the availability of breeding and feeding sites and compromising ecological processes essential for other associated species (Virgilio et al. 2018; Santiago and Beasley 2023). Therefore, the conservation of this species extends beyond its individual value to encompass its functional role within trophic networks and as a sentinel organism for environmental changes. This characteristic makes M. williamsoni particularly relevant for monitoring strategies and territorial planning.

The Belém Endemism Center, the main area of occurrence of the species, is simultaneously one of the most biodiverse and most threatened regions of the Amazon. Estimates indicate that more than 70% of its original forest cover has already been degraded or converted to alternative uses, mainly through agricultural expansion, livestock, and infrastructure development (Almeida and Vieira 2010; Castro et al. 2020). This degree of anthropogenic pressure endangers not only M. williamsoni but also the entire fauna associated with primary forest environments. The intensification of deforestation in the last decade, with rates exceeding 10,000 km² in 2019 and exacerbated by large-scale forest fires (Junior et al. 2021), projects an even more challenging future for specialist species. Habitat fragmentation compromises population connectivity, limits dispersal, and increases the probability of local extinction, creating a scenario in which restoration measures and active management become indispensable.

In light of this scenario, it is urgent to promote research that expands the understanding of bioindicator species sensitive to environmental changes, such as Odonata. These species provide robust information for assessing anthropogenic impacts and delineating priority areas for conservation. Ecological niche and spatial distribution models, such as those applied in this study, have proven to be valuable tools not only for predicting future trends but also for guiding public policies and adaptive management actions (Schwartz 2012; Vasconcelos et al. 2024). Investments in comparative studies with other Amazonian macroinvertebrate and vertebrate communities are equally necessary to fill knowledge gaps and ensure a more integrated view of the processes shaping regional biodiversity.

In summary, M. williamsoni represents an emblematic case of how specialized Amazonian species face the challenges imposed by climate change and accelerated deforestation. The present still offers favorable conditions for its persistence, but future projections indicate drastic habitat contractions. In this sense, the conservation of the Belém Endemism Center must be regarded as a strategic priority, not only to guarantee the survival of this species but also to preserve the ecological functionality and resilience of the Amazon biome as a whole. Maintaining forest connectivity, protecting aquatic systems, and strengthening conservation policies are key elements to securing the future of M. williamsoni and countless other species that share its vulnerability.

In light of the results presented, it is evident that Mnesarete williamsoni represents a notable example of how specialised Amazonian species are particularly vulnerable to climate change and forest cover loss. The contrast between the relatively favorable current suitability and the future scenarios of significant habitat contraction reinforces the urgency of conservation strategies aimed at maintaining forest connectivity and protecting riparian environments. The species’ role as a bioindicator further enhances its relevance, as its persistence directly reflects the integrity of Amazonian ecosystems. Therefore, the conservation of the Belém Endemism Center should be regarded as a strategic priority, not only to ensure the survival of M. williamsoni but also to safeguard the ecological functionality and resilience of the Amazon biome in the face of increasing anthropogenic impacts.

Conflicting interests

The authors declare that they have no conflicts of interest.

Author Contribution

A.B. (Ana Balata): Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft, Writing – Review & Editing.F.G.C. (Fernando Geraldo de Carvalho): Conceptualization, Formal Analysis, Methodology, Writing – Original Draft, Writing – Review & Editing.F.A. (Facundo Alvarez): Formal Analysis, Methodology, Writing – Original Draft.C.R. (Cintia Ribeiro): Writing – Review & Editing, Validation.L.J. (Leandro Juen): Writing – Review & Editing, Supervision, Project Administration.

Abell R, Harrison IJ (2020) A boost for freshwater conservation. Science 370:3.
Aguirre-Liguori J, Ramírez-Barahona S, Gaut B (2021) The evolutionary genomics of species’ responses to climate change. Nat Ecol Evol 5:1350–1360. https://doi.org/10.1038/s41559-021-01526-9
Aleixo A, Albernaz AL, Grelle CEV, Vale MM, Rangel TF (2010) Mudanças climáticas e a biodiversidade dos biomas brasileiros: passado, presente e futuro. Natureza e Conservação.
Almeida AS de, Vieira ICG (2010) Centro de endemismo Belém: status da vegetação remanescente e desafios para a conservação da biodiversidade e restauração ecológica. Rev Estud Univ 36:3.
Alvarez F, Gerhard P, Silva DP, Godoy BS, Montag LFA (2019) Effects of different variable sets on the potencial distribution of fish species in the Amazon Basin. Ecol Freshw Fish.
Amigo I (2020) The Amazon’s Fragile Future. Nature 578:3.
Andrade AFA, Velazco SJE, De Marco P (2020) ENMTML: Na R package for straightforward construction of complex Ecological Niche Models. Environ Model Softw.
Antão L et al. (2022) Climate change reshuffles northern species within their niches. Nat Clim Chang 12:587–592. https://doi.org/10.1038/s41558-022-01381-x
Arruda DM, Schaefer CE, Fonseca RS, Solar RR, Fernandes‐Filho EI (2018) Vegetation cover of Brazil in the last 21 ka: New insights into the Amazonian refugia and Pleistocenic arc hypotheses. Glob Ecol Biogeogr 27:47–56.
Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222:1810–1819.
Bernatchez L, Ferchaud A, Berger C, Venney C, Xuereb A (2023) Genomics for monitoring and understanding species responses to global climate change. Nat Rev Genet 25:165–183. https://doi.org/10.1038/s41576-023-00657-y
Bonaccorso EB, Koch I, Peterson AT (2006) Pleistocene fragmentation of Amazon species ranges. Divers Distrib 12.
Brauer C, Unmack P, Smith S, Bernatchez L, Beheregaray L (2018) On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol Ecol 27:3484–3497. https://doi.org/10.1111/mec.14808
Brito JS et al. (2024) Main drivers of dragonflies and damselflies (Insecta; Odonata) metacommunities in streams inside protected areas in the Brazilian Amazon. Environ Monit Assess 196:281.
Bruno D et al. (2019) Structural and functional responses of invertebrate communities to climate change and flow regulation in alpine catchments. Glob Change Biol 25:1612–1628. https://doi.org/10.1111/gcb.14581
Bueno M et al. (2017) Effects of Quaternary climatic fluctuations on the distribution of Neotropical savanna tree species. Ecography 40:403–414. https://doi.org/10.1111/ecog.01860
Callisto M, Morretti M, Goulart M (2001) Macroinvertebrados bentônicos como ferramenta para avaliar a saúde de riachos. Rev Bras Recur Hidr 6:71–82.
Calvão L et al. (2025) Oviposition strategies of Amazonian dragonflies in response to anthropogenic gradients. Oecologia 207:105. https://doi.org/10.1007/s00442-025-05743-y
Carroll MW et al. (2015) Temporal and spatial analysis of the 2014-2015 Ebola vírus outbreak in West Africa. Nature.
Carvalho FG, De Oliveira Roque F, Barbosa L, De Assis Montag LF, Juen L (2018) Oil palm plantation is not a suitable environment for most forest specialist species of Odonata in Amazonia. Anim Conserv 21:526–533.
Carvalho FG (2019) Métodos comparativos filogenéticos para avaliar a distribuição de Odonata (Insecta) na Amazônia brasileira. Dissertation, Universidade Federal do Pará.
Castro RB, Pereira JLG, Albernaz ALK, Zanin M (2020) Connectivity, spatial structure and the identification of priority areas for conservation of Belém area of endemism, Amazon. An Acad Bras Cienc 92:e20181357.
Catling PM (2005) A potential for the use of Dragonfly (Odonata) diversity as a bioindicator of the efficiency of sewage laggons. Can Field-Nat 119.
Cavalcante T, Barnett AA, Van Doninck J, Tuomisto H (2024) Modelling 21st century refugia and impact of climate change on Amazonia's largest primates. Ecography e06988.
Choi W, Giletti T, Guo J (2021) Persistence of species in a predator-prey system with climate change and either nonlocal or local dispersal. J Differ Equ. https://doi.org/10.1016/j.jde.2021.09.017
Clarke L et al. (2007) Model Documentation for the MiniCAM Climate Change Science Program Stabilization Scenarios. Rep Pacific Northwest National Laboratory, Richland, Washington.
Colles A, Liow LH, Prinzing A (2009) Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches. Ecol Lett 12:849–863.
Correia FWS, Manzi AO, Cândido LA, Santos RMN, Pauliquevis T (2007) Balanço de umidade na Amazônia e sua sensibilidade às mudanças na cobertura vegetal. Cienc Cult 59:3.
Couvreur T et al. (2020) Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biol Rev Camb Philos Soc 96:16–51. https://doi.org/10.1111/brv.12644
Cowling R, Potts A, Franklin J, Midgley G, Engelbrecht F, Marean C (2020) Describing a drowned Pleistocene ecosystem: Last Glacial Maximum vegetation reconstruction of the Palaeo-Agulhas Plain. Quat Sci Rev 235:105866. https://doi.org/10.1016/j.quascirev.2019.105866
De Marco P, Vianna DM (2005) Distribuição do esforço de coleta de Odonata no Brasil: subsídios para escolha de áreas prioritárias para levantamentos faunísticos. Lundiana 6:13–26.
De Marco P Jr, Resende DC (2002) Activity patterns and thermoregulation in tropical dragonfly assemblage. Odonatologica 31:129–138.
De Marco P, Nóbrega CC, De Souza RA, Neiss UG (2015) Modeling the distribution of a rare Amazonian odonate in relation to future deforestation. Freshw Sci 34.
De Paul M, Gleiser R, Villafañe J (2024) Macroinvertebrate assemblage variations among aquatic habitat types across the arid Central Andes (Northwest Argentina). J Arid Environ. https://doi.org/10.1016/j.jaridenv.2024.105266
De Resende B et al. (2021) Impact of environmental changes on the behavioral diversity of the Odonata (Insecta) in the Amazon. Sci Rep 11. https://doi.org/10.1038/s41598-021-88999-7
Delgado R, De Santana R, Gelsleichter Y, Pereira M (2022) Degradação dos biomas sul-americanos: o que esperar para o futuro?. Rev Aval Impact Ambient. https://doi.org/10.1016/j.eiar.2022.106815
Donn E, Rosen S (2001) Systematic Zoology. Soc Syst Biol Stab 24:431–464.
Fearnside PM (1995) Amazonian deforestation and global warming: carbono stocks in vegetation replacing Brazil’s Amazon forest. National Institute for Research in the Amazon (INPA), Amazonas, pp 21–34.
Fonseca E et al. (2023) Pleistocene glaciations caused the latitudinal gradient of within-species genetic diversity. Evol Lett 7:331–338. https://doi.org/10.1093/evlett/qrad030
Garner HF (1959) Stratigraphic-sedimentary significance of contemporary climate and relief in four regions of the Andes Montains. GSA Bull.
Garrison RW, Ellenrieder NV, Louton JA (2010) Damselfly genera of the new world. Johns Hopkins.
Garzón-Orduña IJ, Benetti-Longhini JE, Brower AVZ (2014) Timing the diversification of the Amazonian biota: butterfly divergences are consistente with Pleistocene refugia. J Biogeogr 41.
Goldner JT, Holland JD (2023) Wing morphology of a damselfly exhibits local variation in response to forest fragmentation. Oecologia 202:369–380.
Gougherty A, Keller S, Fitzpatrick M (2021) Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat Clim Chang 11:166–171. https://doi.org/10.1038/s41558-020-00968-6
Haffer J (1997) Alternative models of vertebrate speciation in Amazonia: an overview. Biodivers Conserv.
Haffer J (2008) Hipóteses para explicar a origem das espécies na Amazônia. Braz J Biol 68:1083–1090.
Haffer J (1969) Speciation in Amazonian forest birds: most espécies probably originated in forest refuges during dry climatic periods. Science 165.
Hamli H (2025) The vulnerability of aquatic invertebrates to climate change: A threat to ecosystem stability. Aquat Invertebr Ecol Res. https://doi.org/10.69517/aier.2025.02.01.0001
Haseyama KLF, Carvalho CJB (2011) Padrões de distribuição da biodiversidade Amazônica: um ponto de vista evolutivo. Rev Biol 6:35–40.
Hayes FE, Sewlal JN (2004) The Amazon River as a dispersal barrier to passerine birds: effects of river width, habitat and taxonomy. J Biogeogr 31:1809–1818.
He R et al. (2023) The extremely small body size of Williamson’s mouse deer (Tragulus williamsoni) allows coexistence with sympatric larger ungulates through temporal avoidance. Front Ecol Evol. https://doi.org/10.3389/fevo.2023.1125840
IPCC (2021) Mudanças climáticas 2021: a base da ciência física. In: Masson-Delmotte V et al. (eds) Contribuição do Grupo de Trabalho I para o Sexto Relatório de Avaliação do Painel Intergovernamental sobre Mudanças Climáticas. Imprensa da Universidade de Cambridge, Cambridge.
Juen L (2011) Grandes rios e a distribuição de Odonata na Amazônia: Similaridade de composição, limitação à dispersão e endemismo. Tese de Doutorado, Universidade Federal de Goiás.
Juen L, Oliveira-Junior JMB, Shimano Y, Mendes TP, Cabette HSR (2014) Composição e riqueza de Odonata (insecta) em riachos com diferentes níveis de conservação em um ecótone Cerrado-Floresta Amazônica. Acta Amaz 44.
Juen L et al. (2016) (Referência incompleta/vaga - mantida sem alteração).
Junior CHLS et al. (2021) The brazilian Amazon deforestation rate in 2020 is the greatest of the decade. Nat Ecol Evol 5:2.
Keppel G et al. (2014) Refugia: identifying and understandins safe havens for biodiversity under climate change. Glob Ecol Biogeogr 23:393–404.
Lawler JJ, Ruesch AS, Olden JD, McRae BH (2013) Projected climate-driven faunal movement routes. Ecol Lett 16.
Leal CG et al. (2020) Integrated terrestrial fresh-water planning doubles conservation of tropical aquatic species. Science 370.
Leite JC (2015) Do mistérios das eras do gelo às mudanças climáticas abruptas. Sci Stud 13.
Lembrechts J, Nijs I, Lenoir J (2018) Incorporating microclimate into species distribution models. Ecography. https://doi.org/10.1111/ECOG.03947
Liang G, Niu H, Li Y (2023) A multi-species approach for protected areas ecological network construction based on landscape connectivity. Glob Ecol Conserv 48. https://doi.org/10.1016/j.gecco.2023.e02569
Merengo JA, Souza-Junior C (2018) Mudanças Climáticas: impactos e cenários para a Amazônia. Greenpeace, São Paulo.
Miranda GS, Dias PHS (2012) Biogeografia de vicariancia: histórico e perspectivas da disciplina que lançou um novo olhar sobre a diversidade na Terra. Filos Hist Biol 7:215–240.
Monteiro-Junior CS, Juen L, Hamada N (2015) Analysis of urban impacts on aquatic habitats in the central Amazon basin: adult odonates as bioindicators of environmental quality. Ecol Indic 48.
Moraes KF (2019) O efeito das mudanças climáticas sobre as aves endêmicas do cento de endemismo Belém. Dissertation, Universidade Federal do Pará.
Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika: London 37:17–23.
Nobre CA, Sampaio G, Salazar L (2007) Mudanças Climáticas e Amazônia. Cienc Cult 59:3.
Pearson R, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Glob Ecol Biogeogr 12.
Pereira H et al. (2024) Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 384:458–465. https://doi.org/10.1126/science.adn3441
Percy S, Lubchenco J (2005) Ecossystems and human well-being: opportunities and challenges for Business and Industry, pp 1–36.
Puli G, Santos S, Zimmermann B, Gonçalves A, Bartholomei-Santos M (2024) Impact of Pleistocene Climate on Diversification and Distribution of Congeneric Freshwater Crustacean Species. J Biogeogr 52. https://doi.org/10.1111/jbi.15065
Relatório-Síntese da Avaliação Ecossistêmica do Milênio. (n.d.) Disponível em: https://millenniumassessment.org/documents/document.446.aspx.pdf
Riahi K, Grubler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Change.
Riahi K et al. (2011) RCP 8.5: A scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57.
Ribas CC, Aleixo A (2019) Diversidade e evolução de aves amazônicas: implicações para conservação e biogreografia. An Acad Bras Cienc 91.
Richardson VA, Peres CA (2016) Temporal decay in timber species composition and value in Amazonian Logging Concession. PLoS ONE 11.
Rivero S, Almeida O, Ávila S, Oliveira W (2009) Pecuária e desmatamento: uma análise das principais causas diretas do desmatamento na Amazônia. Nova Econ 19:41–66.
Rubenstein M et al. (2023) Climate change and the global redistribution of biodiversity: substantial variation in empirical support for expected range shifts. Environ Evid 12. https://doi.org/10.1186/s13750-023-00296-0
Santiago L, Beasley CR (2023) Benthic Macroinvertebrates Associated with Riparian Habitat Structural Diversity in an Eastern Amazon Stream Urbanization Gradient. Floresta Ambient 30:e20220092.
Sato H et al. (2021) Dry corridors opened by fire and low in Amazonian rainforest during the Last Glacial Maximum. Nat Geosci 14:578–585.
Schloss CA, Nuñez TA, Lawler J (2012) Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. PNAS 109.
Shimano YF (2015) Ephemeroptera (insecta) no Brasil: estado da arte, amostragem, influência e distribuição. Tese de Doutorado, Universidade Federal do Pará.
Sholihah A et al. (2021) Impact of Pleistocene Eustatic Fluctuations on Evolutionary Dynamics in Southeast Asian Biodiversity Hotspots. Syst Biol. https://doi.org/10.1093/sysbio/syab006
Shwartz MW (2012) Using niche models with climate projections to inform conservation management decisions. Biol Conserv.
Siepielski A et al. (2017) Precipitation drives global variation in natural selection. Science 355:959–962. https://doi.org/10.1126/science.aag2773
Silva EC, Rocha TS, Oliveira Junior JMB (2017) Efeitos de variações temporais e microclimáticas diárias sobre a riqueza de espécies de zigoptera (Insecta: Odonata) em igarapés no município de Santarém-PA. In: VIII Congresso Brasileiro de Gestão Ambiental, Campo Grande. IBEAS.
Silva GL (2014) Modelagem Ecológica: Modelos de Distribuição de Espécies Como Ferramenta Para Estudos de Ecologia, Evolução e Conservação. In: Anais do IV Simpósio de Ciências Biológicas e sua Multidisciplinaridade, Frederico Westphalen-RS. URI-FW.
Silva G, Antonelli A, Lendel A, Moraes E, Manfrin M (2018) The impact of early Quaternary climate change on the diversification and population dynamics of a South American cactus species. J Biogeogr 45:76–88. https://doi.org/10.1111/jbi.13107
Silva LB et al. (2022) Como as futuras mudanças climáticas e o desmatamento podem afetar drasticamente as espécies de macacos endêmicos do leste Amazônico e prioridades para a conservação. Biodiversidade e Conservação.
Thomson AM et al. (2011) RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change 109:77–97.
Thuiller W et al. (2007) Predicting global change impacts on plant species distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152.
Torres NM, Vercillo UE (2012) Como Ferramentas de Modelagem de Distribuição de Espécies Podem Subsidiar Ações de Governo?. Natureza & Conservação 10:3.
Tyukavina A et al. (2017) Types and rates of forest disturbance in Brazilian Legal Amazon, 2000-2013. Sci Adv 3.
Urban M (2024) Climate change extinctions. Science 386:1123–1128. https://doi.org/10.1126/science.adp4461
Van Der Hammen T (1974) The Pleistocene changes of vegetation and climate in tropical South America. J Biogeogr 1.
Vasconcelos RN et al. (2024) Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review. Earth 5:963–989.
Velazco SJE, Villalobos F, Galvão F, De Marco P (2019) A dark scenario for Cerrado plant species: effects of future climate, land and use protected áreas ineffectiveness. Divers Distrib.
Virgilio LR et al. (2018) Does riparian vegetation affect fish assemblage? A longitudinal gradient analysis in three Amazonian streams. Acta Sci Biol Sci 40:1–10.
Waltari E et al. (2007) Localizando refúgios do Pleistoceno: comparando previsões do Modelo de Nicho Filogeográfico e Ecológico. PLoS ONE 2.
Wei J et al. (2025) Spatial Distribution and Intraspecific and Interspecific Associations of Dominant Tree Species in a Deciduous Broad-Leaved Forest in Shennongjia, China. Diversity 17:335. https://doi.org/10.3390/d17050335
Whittaker RJ et al. (2005) Conservation Biogeography: assessment and prospect. Divers Distrib 11.
Yates CJ et al. (2010) Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: opportunities and challenges. Austral Ecol 35:374–391.
Zhao Y et al. (2018) Contributions of precipitacion and temperature to the large scale geographic distribution of fleshy-fruited plant species: growth form matters. Sci Rep 8.
Zhang Z et al. (2021) Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish. Divers Distrib 27:684–695. https://doi.org/10.1111/ddi.13225

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Vulnerability of Mnesarete williamsoni (Insecta: Zygoptera) to climate change and loss vegetation cover

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