2.1 Co-occurring subjects

The number of studies on climate change risk on species and ecosystems has been exponentially increased since 2000 (Figure 2a). Between 2000 and 2004, less than 25 papers are published in both fields, whereas since 2013, more than 100 papers have been published each year, and the annual published papers have exceeded 200 since 2019. Figure 2b presents the co-occurring subject categories network of topic 1, which comprises 59 nodes and 262 links. Notably, the top three categories in terms of research activity were as follows: Environmental Sciences & Ecology (1216, 0.14); Biodiversity & Conservation (551, 0.06); and Ecology (527, 0.58). The numbers in parentheses represent the number of articles and the centrality of the categories, respectively. Figure 2c displays the co-occurring subject categories network of topic 2 consisting of 73 nodes and 198 links. In this case, the top three categories were as follows: Environmental Sciences (427, 0.54); Ecology (334, 0.24); and Biodiversity & Conservation (223, 0.06). Frequent co-occurrence among subject categories indicates that the field of study is inherently multidisciplinary and interdisciplinary.

Figure 2. Annual number of publications from 2000 to 2022, divided into four stages: Stage 1 (2000–2005), Stage 2 (2006–2010), Stage 3 (2011–2015), and Stage 4 (2016–2022), showing the growth trend in research on the topics (a); Co-occurring subject categories network of topic 1 “Climate change risk on species” (b); Co-occurring subject categories network of topic 2 “Climate change risk on ecosystems” (c).

2.2 Research collaboration

The number of publications in a given field represents the level of a country's activity in that particular area, while the centrality values of nodes in the cooperation network signify the authority and leadership of countries within the field. The United States, the United Kingdom, and Australia are the top three countries in terms of the number of papers for topic 1, accounting for 42.27% of all research papers in this field (Figure 3a). However, the centrality of these three countries is relatively lower, with the values of 0.02, 0.13, and 0.18, respectively (Figure 3b). The United States, China, and the United Kingdom are the top three countries in terms of the number of papers for topic 2, accounting for 40.69% of all research papers in this area (Figure 3c). When considering centrality, France, Germany, and Finland take the lead, with the centrality values of 0.21, 0.18, and 0.18, respectively (Figure 3d). In both fields, it is evident that the top three countries in terms of the number of publications contribute to more than 40% of all publications, highlighting the significant disparity between countries in research output within these fields. The large number of links between nodes in Figures 3b and 3d further illustrates the extensive collaboration between the countries across the globe.

Figure 3. The number of records attributed to authors from each country of topic 1 (a) and topic 2 (c). Country collaboration network of topic 1 (b) and topic 2 (d).

An institution co-authorship analysis was employed to unveil academic collaborations at the institutional (Figures 4a and 4b) and author levels (Figures 4c and 4d). In the field of topic 1, there were 193 institutions involved, resulting in 412 collaborations among them. The institutions with the highest research output included the Chinese Academy of Sciences, Consejo Superior de Investigaciones Científicas, and the University of British Columbia. In the field of topic 2, the organizations with the largest research output in this field were the Chinese Academy of Sciences, the U.S. Geological Survey, and the U.S. Forest Service. The network of topic 1 comprised of 105 collaborations and 92 nodes, whereas the network of topic 2 comprised of 27 collaborations and 35 nodes. The centrality of many nodes in the network of topic 1 exceeded 0.10, particularly those associated with several authors who had the highest publication volumes. In contrast, the network of topic 2 exhibited a division into numerous isolated sub-networks, with no nodes having a betweenness centrality greater than 0.01. This suggests that authors in the field of topic 2 tended to collaborate in small teams, and there was limited collaboration between these teams.

Figure 4. Institutional collaboration network in the field of topic 1 (a) and topic 2 (b); Author collaboration network in the field of topic 1 (c) and topic 2 (d).

2.3 Influential references

Co-citation analysis allows us to gather valuable insights about the most frequently cited authors, references, and journals within a specific research area. The author co-citation network analysis in the field of topic 1 revealed a vast network comprising 2344 nodes connected by 9664 links, organized into 19 co-citation clusters. The author co-citation network analysis in the field of topic 2 comprised of 2226 nodes connected by 7238 links, grouped into 19 co-citation clusters. Authors such as Parmesan C., Thomas C.D., and Thuiller W.C. featured prominently among the top three authors in both fields. Through an analysis of citation frequency, we identified 78,096 valid references of topic 1 and 102,697 valid references of topic 2. The three most cited articles in the field of topic 1 are Urban (Reference Urban2015), Pecl et al. (Reference Pecl, Araújo, Bell, Blanchard, Bonebrake, Chen, Clark, Colwell, Danielsen, Evengård, Falconi, Ferrier, Frusher, Garcia, Griffis, Hobday, Janion-Scheepers, Jarzyna, Jennings and Williams2017), and Pacifici et al. (Reference Pacifici, Foden, Visconti, Watson, Butchart, Kovacs, Scheffers, Hole, Martin, Akçakaya, Corlett, Huntley, Bickford, Carr, Hoffmann, Midgley, Pearce-Kelly, Pearson, Williams and Rondinini2015). Three most cited articles in the field of topic 2 are IPCC (2014), Pecl et al. (Reference Pecl, Araújo, Bell, Blanchard, Bonebrake, Chen, Clark, Colwell, Danielsen, Evengård, Falconi, Ferrier, Frusher, Garcia, Griffis, Hobday, Janion-Scheepers, Jarzyna, Jennings and Williams2017), and Seidl et al. (Reference Seidl, Thom, Kautz, Martin-Benito, Peltoniemi, Vacchiano, Wild, Ascoli, Petr, Honkaniemi, Lexer, Trotsiuk, Mairota, Svoboda, Fabrika, Nagel and Reyer2017). Publications in the two fields are spread across 458 and 527 different journals, respectively (Figures 5a and 5c). Journal co-citation analysis (Figures 5b and 5d) shows that the network of cited journals in the field of topic 1 comprises 1173 nodes organized into 71 co-citation clusters (modularity Q = 0.538, weighted average silhouette = 0.479). In contrast, the network of cited journals in the field of topic 2 consists of 1169 nodes organized into 81 co-citation clusters (modularity Q = 0.7582, weighted average silhouette = 0.6111).

Figure 5. Top 20 journals in term of publications in the field of “Climate change risk on species” (a) and the field of “Climate change risk on ecosystems” (c). Visualization of the journal co-citation network in the field of “Climate change risk on species” (b) and the field of “Climate change risk on ecosystems” (d).

2.4 Thematic trends

Keywords with high frequencies in a research area can effectively represent the hot topics of interest. In the field of topic 1, the top 10 keywords with the highest frequency are climate change, biodiversity, impact, extinction risk, conservation, distribution, model, risk, diversity, and response. In the field of topic 2, the top 10 keywords are climate change, impact, management, ecosystem service, biodiversity, risk, vulnerability, adaptation, conservation, and model. Table 1 presents the top 10 suddenly emerging keywords with high burst strength in the two fields. These observations illustrate that researchers are increasingly exploring novel topics such as climate change impacts, regional responses, and nature-based solutions. Among the high-frequency keywords in both fields, words such as impact, risk, vulnerability, resilience, response, adaptation, prediction, and management are prominent, signifying the growing importance of risk assessment and forewarning for the adaptation of species and ecosystems to climate change.

Table 1. Ten keywords with strongest bursts in the fields of topics 1 and 2

3.1 Exposure

Climate change exposure pertains to the extent to which organisms and ecosystems are susceptible to climate change-related threats. These threats encompass intrinsic factors such as physiological tolerances and dispersal ability, as well as extrinsic factors such as rising temperatures, shifts in precipitation patterns, changes in the frequency and intensity of meteorological events, including sea-level rise, droughts, floods, and hurricanes (Brawn et al., Reference Brawn, Benson, Stager, Sly and Tarwater2017; Cardillo et al., Reference Cardillo, Mace, Jones, Bielby, Bininda-Emonds, Sechrest, Orme and Purvis2005; Glazier & Gjoni, Reference Glazier and Gjoni2024). For instance, higher temperatures influence both abiotic disturbances such as fire, drought, wind, snow, and ice, and biotic disturbances such as insect infestations and pathogens. The complex interplay between these disturbances further compounds ecosystem disruptions (Seidl et al., Reference Seidl, Thom, Kautz, Martin-Benito, Peltoniemi, Vacchiano, Wild, Ascoli, Petr, Honkaniemi, Lexer, Trotsiuk, Mairota, Svoboda, Fabrika, Nagel and Reyer2017). The exposure of species and ecosystems to climate change varies significantly across different climate change scenarios. Under a global warming scenario of less than 2°C, it is anticipated that less than 2% of ecological assemblages will face sudden exposure events affecting over 20% of species worldwide. In contrast, if global warming reaches 4°C, 15% of assemblages will be at risk of sudden exposure (Trisos et al., Reference Trisos, Merow and Pigot2020). Within the high emissions scenario, climate change exposure for ecological assemblages is expected to commence in tropical oceans by 2030 and subsequently expand to tropical forests and higher latitudes by 2050. Ureta et al. (Reference Ureta, Ramírez-Barrón, Sánchez-García, Cuervo-Robayo, Munguía-Carrara, Mendoza-Ponce, Gay and Sánchez-Cordero2022) employed standardized Euclidean distances, considering current and future climate conditions at each grid point, which encompass annual temperature change, precipitation change, and historical records of hurricane intensity and fire occurrences, to forecast the risk of climate change exposure for species. Beyond alterations in the mean levels of climate factors, researchers are increasingly focusing on temporal shifts and structural impacts of these factors. Increased variability in winter snowmelt will intensify water shortages during the growing season and elevate the stochasticity of runoff (Wieder et al., Reference Wieder, Kennedy, Lehner, Musselman, Rodgers, Rosenbloom, Simpson and Yamaguchi2022).

Non-human primates are often considered flagship species in tropical forest ecosystems. Under the most pessimistic climate change scenario, it is estimated that 74% of primates inhabiting Neotropical forests may face exposure to a maximum upper temperature increase of up to 7°C. In contrast, primates residing in Madagascar’ s savannahs will experience less pronounced warming (Carvalho et al., Reference Carvalho, Graham, Rebelo, Bocksberger, Meyer, Wich and Kühl2019). Mammals that inhabit the same geographic ranges exhibit varying risks of climate change exposure due to differences in body size and movement patterns. Generally, larger species (>15 kg) and arboreal and semi-aquatic animals are at the highest risk. Even for sympatric species with relatively similar sensitivities, such as Disjunct plant genera, the risk of extinction differs considerably in response to various environmental exposures. The key climate change exposure factor for Disjunct plant genera in East Asia is the annual temperature range, whereas in the northeastern United States, it is annual precipitation (Song et al., Reference Song, Ordonez, Svenning, Qian, Yin, Mao, Deng and Zhang2021).

3.2 Sensitivity

Sensitivity refers to the extent to which ecosystems and species respond to climate change. When assessing the impact of environmental changes on extinction risk, one of the primary sources of uncertainty is the potential variability in biological sensitivity (Song et al., Reference Song, Ordonez, Svenning, Qian, Yin, Mao, Deng and Zhang2021). This sensitivity encompasses a range of factors, including intrinsic elements such as genetic characteristics and reproductive methods, as well as extrinsic factors such as food networks and ecological interactions.

Physiological characteristics that influence species and ecosystem sensitivity include traits such as temperature range tolerance, water acquisition, conservation and utilization efficiency, reproductive methods, and strategies. For example, the generation length, defined in some studies as the average age of parents in the current generation, reflects the rate at which breeding individuals in a population are renewed. Species with longer generation lengths and lower reproduction rates have demonstrated a higher risk of extinction under climate change (Pacifici et al., Reference Pacifici, Visconti, Butchart, Watson, Cassola and Rondinini2017). In comparison to species with shorter generation lengths, those with longer generation lengths exhibit relatively smaller population responses to conservation measures, such as the establishment of protected areas and translocations (Leclerc et al., Reference Leclerc, Courchamp and Bellard2020a).

Ecological characteristics that influence species and ecosystem sensitivity include habitat features (niche breadth), position within food chains, and food networks (including primary diet, foraging niche, and foraging periods), life history features (species lifespan, body size, life history strategies, migration characteristics, etc.) (Sandin et al., Reference Sandin, Schmidt-Kloiber, Svenning, Jeppesen and Friberg2014; Ureta et al., Reference Ureta, Ramírez-Barrón, Sánchez-García, Cuervo-Robayo, Munguía-Carrara, Mendoza-Ponce, Gay and Sánchez-Cordero2022). For mammals, habitat specialization and dietary specialization are vital factors for evaluating their sensitivity to climate change, as more specialized species are less likely to expand into new, suitable climate regions. Species with limited migration capabilities tend to be more sensitive to climate change. Ecosystems supported by unique species face higher risks of concurrent extinctions and critical loss of ecosystem functions due to disruptions in ecological interactions caused by climate change. In other words, such ecosystems exhibit lower ecological redundancy (similar combinations of ecological trait values) (Leclerc et al., Reference Leclerc, Villéger, Marino and Bellard2020b).

Genetic characteristics influencing species and ecosystem sensitivity encompass genetic diversity, genetic adaptability, genetic drift, and so forth (Jezkova et al., Reference Jezkova, Olah-Hemmings and Riddle2011). For example, some fish populations with high genetic diversity may be more capable of adapting to changing water temperatures and quality. Certain species may already possess adaptive genetic traits for climate change conditions, including heat tolerance genes, increased drought resistance, or enhanced immune systems (Parmesan, Reference Parmesan2006). Genetic drift refers to random changes in genetic characteristics within a population, which can lead to the emergence of new beneficial traits or the reduction of existing harmful traits. Wildlife populations with higher genetic drift are more likely to adapt to climate change across different geographical regions (Perry & Wu, Reference Perry and Wu1960).

3.3 Adaptivity

Adaptivity is a term used to describe the capacity of species and ecosystems to respond to climate change. Various factors influence the adaptivity of species and ecosystems, including intrinsic factors such as behavioral adaptation and niche flexibility, as well as extrinsic factors such as geographical isolation and the presence of protected areas. Geographical isolation restricts opportunities for species to move to other landmasses, impacting their ability to adapt to climate change. For instance, isolated islands often offer fewer potential refuges, and if these islands have limited area and minimal elevational differences, the adaptability of species can be significantly compromised. Establishing protected areas is a recognized effective method for enhancing the adaptability of species and ecosystems to climate change. Protected areas can provide suitable habitats and increase habitat continuity, facilitating species migration from non-protected areas to protected ones (Ureta et al., Reference Ureta, Ramírez-Barrón, Sánchez-García, Cuervo-Robayo, Munguía-Carrara, Mendoza-Ponce, Gay and Sánchez-Cordero2022). Phylogenetic uniqueness measures the number of close relatives of each species and their phylogenetic distance (Jansson, Reference Jansson2009). Species pools with greater phylogenetic diversity possess higher evolutionary potential in the face of climate change, making them more likely to adapt and persist.

Physiological plasticity can potentially alleviate the impact of climate warming on organisms by reducing the thermal sensitivity of life processes and increasing physiological tolerance (Seebacher et al., Reference Seebacher, White and Franklin2015; Stillman, Reference Stillman2003). Species' behavioral avoidance can also protect organisms from the effects of climate change by minimizing exposure to high-cost or lethal temperatures (Sunday et al., Reference Sunday, Bates, Kearney, Colwell, Dulvy, Longino and Huey2014). Nevertheless, the roles of plasticity and species' behavioral avoidance in safeguarding species from extinction are still debated, as climate warming may surpass the adaptive capacity of plasticity or increase the costs associated with behavioral strategies (Sears et al., Reference Sears, Angilletta, Schuler, Borchert, Dilliplane, Stegman, Rusch and Mitchell2016). The current extinction rate of species also influences their adaptivity. According to the filtering hypothesis (Balmford, Reference Balmford1996), species with high extinction rates are more likely to withstand future climate change. This is because species that have evolved and survived in highly disturbed environments are more likely to persist in the face of new disturbances, such as climate change. However, species' adaptability is effective only within a certain range of climate change scenarios. In the best-case climate scenario (RCP2.6), plant genera with similar sensitivity in eastern Asia and eastern North America show distinct differences in vulnerability. However, under the most pessimistic scenario (RCP8.5), these differences vanish, and all genera become highly vulnerable. This suggests that severe climate change (RCP8.5) may override regional buffer capacities (Song et al., Reference Song, Ordonez, Svenning, Qian, Yin, Mao, Deng and Zhang2021).

3.4 Vulnerability

Vulnerability is the critical factor linking distribution, exposure, sensitivity, and adaptivity, offering a comprehensive assessment of species and ecosystems susceptibility to climate change. It also plays a key role in evaluating extinction risks (Bergstrom et al., Reference Bergstrom, Wienecke, van den Hoff, Hughes, Lindenmayer, Ainsworth, Baker, Bland, Bowman, Brooks, Canadell, Constable, DellaSala, Mackey, Manica, Possingham, Schroeter, Terauds and Shaw2021). The rate of species extinction on Earth is on the rise, with one out of every six species facing threats. Particularly noteworthy is that in South America, Australia, and New Zealand, the risk of species extinction is most pronounced (Foden et al., Reference Foden, Butchart, Stuart, Vié, Akçakaya, Angulo, DeVantier, Gutsche, Turak, Cao, Donner, Katariya, Bernard, Holland, Hughes, O'Hanlon, Garnett, Şekercioğlu and Mace2013; Malcolm et al., Reference Malcolm, Liu, Neilson, Hansen and Hannah2006; Urban, Reference Urban2015; Warren et al., Reference Warren, VanDerWal, Price, Welbergen, Atkinson, Ramirez-Villegas, Osborn, Jarvis, Shoo, Williams and Lowe2013). Islands and archipelagos, in particular, exhibit varying degrees of vulnerability to future climate change, with the Pacific region often displaying heightened vulnerability. In a comprehensive assessment by Thomas et al. (Reference Thomas, Cameron, Green, Bakkenes, Beaumont, Collingham, Erasmus, Ferreira de Siqueira, Grainger, Hannah, Hughes, Huntley, van Jaarsveld, Midgley, Miles, Ortega-Huerta, Peterson, Phillips and Williams2004), which covered approximately 20% of the world's terrestrial surface, it was found that under the mid-range warming scenario in 2050, 15–37% of species will be on the brink of extinction.

In the discourse on vulnerability and extinction risk, the concept of ‘extinction debt’ is pivotal. Several studies (Bertrand et al., Reference Bertrand, Riofrío-Dillon, Lenoir, Drapier, De Ruffray, Gégout and Loreau2016; 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, WallisDeVries and Jiguet2012) suggest that the impact of climate change on local species richness is constrained and may paradoxically forecast an augmentation in species diversity, challenging conventional acknowledge. For instance, in mountainous regions susceptible to climate warming, instances of plant extinctions are sporadic, even across a century-long time series of climate warming. In contrast, the overall richness of plant species at the local level has surged as species migrate to higher latitudes with climate warming (Dullinger et al., Reference Dullinger, Gattringer, Thuiller, Moser, Zimmermann, Guisan, Willner, Plutzar, Leitner, Mang, Caccianiga, Dirnböck, Ertl, Fischer, Lenoir, Svenning, Psomas, Schmatz, Silc and Hülber2012; Tilman et al., Reference Tilman, May, Lehman and Nowak1994). This phenomenon is expounded by the concept of ‘extinction debt’ (Rumpf et al., Reference Rumpf, Hülber, Wessely, Willner, Moser, Gattringer, Klonner, Zimmermann and Dullinger2019), which posits that although habitat destruction or other detrimental factors may have initiated biodiversity decline, the actual extinction of species may be deferred into the future, owing to a time lag. Extinction debt implies that, notwithstanding efforts to mitigate habitat loss or other stressors, compromised biodiversity may still experience a gradual decline in the ensuing decades (Arneth et al., Reference Arneth, Shin, Leadley, Rondinini, Bukvareva, Kolb, Midgley, Oberdorff, Palomo and Saito2020; Dullinger et al., Reference Dullinger, Gattringer, Thuiller, Moser, Zimmermann, Guisan, Willner, Plutzar, Leitner, Mang, Caccianiga, Dirnböck, Ertl, Fischer, Lenoir, Svenning, Psomas, Schmatz, Silc and Hülber2012; Jackson Sax, Reference Jackson and Sax2010).

3.5 Response

Responses of species and ecosystems to environmental changes can be divided into short-term, medium-term, and long-term categories. In the short term, responses include individual behavioral changes, physiological adjustments, and shifts in community composition. Over the medium term, species may exhibit phenotypic adaptations, undergo genotype selection, and experience shifts in their geographic distribution. In the long term, environmental pressures can lead to evolutionary trends, species extinction, and overall ecosystem transformation (Moilanen et al., Reference Moilanen, Lehtinen, Kohonen, Jalkanen, Virtanen and Kujala2022). Importantly, there are feedback relationships between response and other components such as exposure, sensitivity, and adaptation. For example, changes in a species' range reflect its ability to adapt to new climatic conditions, showcasing the profound influence of climate change on the survival and reproductive parameters of these species (Mahony et al., Reference Mahony, Cannon, Wang and Aitken2017; Zhou et al., Reference Zhou, Steiner, Fivash, Cozzoli, Blok, van IJzerloo, van Dalen, Ysebaert, Walles and Bouma2023). However, changes in the distribution of species with particular behaviors, such as ecosystem engineers, can create feedback loops that influence their exposure to environmental changes (Cozzoli et al., Reference Cozzoli, Shokri, da Conceição, Herman, Hu, Soissons, Van Dalen, Ysebaert and Bouma2021). A prominent feature of species redistribution driven by climate change is the rate and extent at which various species respond. This often leads to the disruption of pre-existing interactions and the formation of new ecological relationships concurrently. Such dynamics result in species either separating or engaging in novel interactions (Pecl et al., Reference Pecl, Araújo, Bell, Blanchard, Bonebrake, Chen, Clark, Colwell, Danielsen, Evengård, Falconi, Ferrier, Frusher, Garcia, Griffis, Hobday, Janion-Scheepers, Jarzyna, Jennings and Williams2017). This disruption has significant consequences, affecting predatory, competitive, commensal, and parasitical relationships (Cahill et al., Reference Cahill, Aiello-Lammens, Fisher-Reid, Hua, Karanewsky, Ryu, Sbeglia, Spagnolo, Waldron, Warsi and Wiens2013). In European countries where agriculture plays a substantial role in the GDP, climate change projections (Civantos et al., Reference Civantos, Thuiller, Maiorano, Guisan and Araújo2012), anticipate a decrease in the distribution and abundance of vertebrates responsible for controlling crop pests.

4.1 Correlative approaches

Correlation-based methods rely on established climate and ecological data, encompassing factors such as species distribution, temperature, and rainfall (Kong et al., Reference Kong, Xu, Xiao, Pimm, Shi and Ouyang2021). These methods employ statistical analysis to assess species and ecosystems' vulnerability. These predictions involve comparing current and future climate data to identify regions where species survival and ecosystem stability may be at risk. Traditional species distribution models (SDMs) are often categorized as correlation-based methods, as they primarily examine the correlations between the species distribution and environmental variables (Song et al., Reference Song, Ordonez, Svenning, Qian, Yin, Mao, Deng and Zhang2021; Summers et al., Reference Summers, Bryan, Crossman and Meyer2012). In their description of the current distribution model of Ethiopian Arabica coffee, Moat et al. (Reference Moat, Gole and Davis2019) utilized a comprehensive SDM, employing six modeling techniques: generalized linear models (GLMs), generalized boosted regression models (GBMs), generalized additive models (GAMs), multiple adaptive regression splines (MARS), random forest (RFs), and maximum entropy (Maxent). Tagliari et al. (Reference Tagliari, Danthu, Leong Pock Tsy, Cornu, Lenoir, Carvalho-Rocha and Vieilledent2021) employed four statistical algorithms to model the bioclimatic niche and distribution of seven studied baobab species: GLMs, GAMs, RFs, and Maxent. Curd et al. (Reference Curd, Chevalier, Vasquez, Boyé, Firth, Marzloff, Bricheno, Burrows, Bush, Cordier, Davies, Green, Hawkins, Lima, Meneghesso, Mieszkowska, Seabra and Dubois2023) employed landscape metrics in species distribution modeling to characterize the internal structure and variations within species distribution areas, using four algorithms: GLMs, GAMs, RFs, and boosted regression trees (BRTs). Parametric models such as GLM and GBM, along with non-parametric models such as GAM and MARS, are well-known for their robustness and are standard regression models. In contrast, classification tree models such as RFs, BRTs, and probability distribution models such as Maxent, belonging to machine learning methods, are more complex algorithms.

Relevant models are capable of mapping across various spatial scales and a wide range of species. However, they do come with certain limitations and uncertainties, primarily originating from climate data, algorithms, and biological assumptions (Guisan & Rahbek, Reference Guisan and Rahbek2011; Pearson et al., Reference Pearson, Thuiller, Araújo, Martinez-Meyer, Brotons, McClean, Miles, Segurado, Dawson and Lees2006). Uncertainties in climate data can be attributed to general circulation models and their resolutions. Different parameters and model structures can yield diverse outcomes when simulating future climate systems (Bagchi et al., Reference Bagchi, Crosby, Huntley, Hole, Butchart, Collingham, Kalra, Rajkumar, Rahmani, Pandey, Gurung, Trai, Quang and Willis2013; Wiens et al., Reference Wiens, Stralberg, Jongsomjit, Howell and Snyder2009). Furthermore, climate data are typically less detailed compared to other data used in correlation models, such as environmental and biological data, often proving inadequate for modeling rare species or those with smaller geographical ranges (Guisan & Thuiller, Reference Guisan and Thuiller2005). Uncertainties in algorithms arise from variations in model performance and simulation outcomes resulting from the choice of different correlation methods and predictor variables. Some studies have mitigated these uncertainties by producing ensemble predictions, which involve averaging probabilities and confidence intervals from various models (Carvalho et al., Reference Carvalho, Brito, Crespo, Watts and Possingham2011). Uncertainties related to biological assumptions hinge on the presumption that the relationship between species and their environmental conditions will persist in the future (Harrison et al., Reference Harrison, Berry, Butt and New2006). As in reality, the ecological niche of some species is influenced not only by their optimal climate but also by non-biological, biological, geographic, historical, and anthropogenic factors (Guisan & Thuiller, Reference Guisan and Thuiller2005). As future climate conditions evolve, species may select different and more suitable ecological niches than their current ones. Despite criticism that correlation models lack mechanistic and causal explanations and have limited capabilities when assessing species with sparse distribution points and small geographical ranges, they have been widely used in regional and global analyses due to their relative speed and cost-effectiveness.

4.2 Mechanistic approaches

A mechanistic approach is grounded in a comprehensive understanding of biological and ecological mechanisms, which are employed to analyze species' physiological, ecological, and behavioral responses to climate change. This approach encompasses species' life history traits, physiological ecological processes, and adaptability, which typically necessitate a more substantial body of biological and experimental data. Mechanistic models such as demographic niche model, landscape and life history population model (LoLiPop), migration and climate model (MigClim) build upon correlation models such as SDMs by incorporating mechanistic components such as diffusion or population dynamics. On the contrary, process-based dynamic range models, incidence function models (IFMs), and age-structured meta-population models do not rely on traditional SDMs; they directly infer the dynamics of environment-population quantity from data, thereby delving deeper into the mechanisms governing biological processes (Zurell et al., Reference Zurell, Thuiller, Pagel, Cabral, Münkemüller, Gravel, Dullinger, Normand, Schiffers, Moore and Zimmermann2016). Riddell et al. (Reference Riddell, Odom, Damm and Sears2018) integrate experimental physiological and behavioral traits into SDMs to predict extinction risk based on individuals' ability to maintain energy balance under scenarios with and without plasticity.

Mechanistic models are widely regarded as more explanatory, robust, and theoretically sound (Kearney & Porter, Reference Kearney and Porter2009). Unlike correlation models, which can only simulate the ecological niches that species have already occupied, mechanistic models may provide a better representation of the fundamental ecological niches of species, even those that are not currently reflected in their distribution (Kearney & Porter, Reference Kearney and Porter2009; Monahan, Reference Monahan2009). Mechanistic models can also explicitly account for significant biological factors, including evolution and physiological responses. However, the limitations and uncertainties of mechanistic models primarily arise from the lack of understanding of the processes involved and the challenges associated with parameter selection, calibration, and validation during the modeling process. Mechanistic models rely on detailed data obtained from laboratory or field experiments, such as reproductive rates and physiological tolerances (Deutsch et al., Reference Deutsch, Tewksbury, Huey, Sheldon, Ghalambor, Haak and Martin2008; Jenouvrier et al., Reference Jenouvrier, Caswell, Barbraud, Holland, Strœve and Weimerskirch2009; Radchuk et al., Reference Radchuk, Turlure and Schtickzelle2013), and many species lack these data, rendering mechanistic models less widely applicable and often confined to a few rare or endangered species (Hunter et al., Reference Hunter, Caswell, Runge, Regehr, Amstrup and Stirling2010). Similar to correlation models, mechanistic models typically do not account for interactions between the species.

4.3 Hybrid approaches

Hybrid approaches integrate both correlative and mechanistic elements, offering a comprehensive framework for understanding species' responses to climate change by considering not only statistical relationships between the variables but also biological and ecological processes (Cozzoli et al., Reference Cozzoli, Shokri, da Conceição, Herman, Hu, Soissons, Van Dalen, Ysebaert and Bouma2021). Example models, such as LoLiPop, MigClim, and IFMs, capture the complex interactions between environmental factors and species traits. Intrinsic traits encompass a species' body size (Jones et al., Reference Jones, Bielby, Cardillo, Fritz, O'Dell, Orme, Safi, Sechrest, Boakes, Carbone, Connolly, Cutts, Foster, Grenyer, Habib, Plaster, Price, Rigby, Rist and Purvis2009), dietary breadth, dispersal distance, generation length, litter size, annual reproductive rate (Jones et al., Reference Jones, Bielby, Cardillo, Fritz, O'Dell, Orme, Safi, Sechrest, Boakes, Carbone, Connolly, Cutts, Foster, Grenyer, Habib, Plaster, Price, Rigby, Rist and Purvis2009), and activity pattern (Wilman et al., Reference Wilman, Belmaker, Simpson, de la Rosa, Rivadeneira and Jetz2014). Spatial traits relate to a species' distribution range and encompass the highest temperature within the species' range, the lowest temperature, temperature seasonality, precipitation seasonality, and altitudinal range.

The key advantage of hybrid approaches lies in their flexibility and adaptability. They offer comprehensive insights into species vulnerability while being customizable to suit different research questions and datasets. However, these approaches often require high-quality, high-resolution data, and the process of selecting and calibrating multiple parameters can introduce subjectivity and uncertainty (Hunter et al., Reference Hunter, Caswell, Runge, Regehr, Amstrup and Stirling2010). Hybrid models rely on detailed data obtained from laboratory or field experiments, such as reproductive rates and physiological tolerances (Deutsch et al., Reference Deutsch, Tewksbury, Huey, Sheldon, Ghalambor, Haak and Martin2008; Radchuk et al., Reference Radchuk, Turlure and Schtickzelle2013).

4.4 Other approaches

In addition to the relevant models, mechanistic models, and trait-based methods mentioned earlier, there are several other methods for assessing climate change risks on species and ecosystems. These include criteria-based approaches, expert judgments, the paleoecological method, and combined approaches.

Criteria-based approaches typically utilize the categories and standards established by the International Union for Conservation of Nature (IUCN) Red List (Maclean & Wilson, Reference Maclean and Wilson2011; Visconti et al., Reference Visconti, Bakkenes, Baisero, Brooks, Butchart, Joppa, Alkemade, Di Marco, Santini, Hoffmann, Maiorano, Pressey, Arponen, Boitani, Reside, van Vuuren and Rondinini2015) to categorize species into different threat categories based on the risks posed by climate change. These standardized methods are applicable to a wide range of global species and consider multiple aspects of how climate change risks impact species and ecosystems. Pearson et al. (Reference Pearson, Stanton, Shoemaker, Aiello-Lammens, Ersts, Horning, Fordham, Raxworthy, Ryu, McNees and Akçakaya2014) employed a simulation approach based on general life history types and found that most variables critical for predicting extinction risk are already incorporated into the IUCN Red List criteria for species conservation assessments, suggesting that the current assessment criteria may be more effective at identifying vulnerable species and ecosystems in the context of climate change than previously thought.

Expert judgments based on their knowledge and experience are sometimes used to assess climate change risk on species and ecosystems, especially in situations with limited data. Camac et al. (Reference Camac, Umbers, Morgan, Geange, Hanea, Slatyer, McDougall, Venn, Vesk, Hoffmann and Nicotra2021) employed structured expert judgment to predict species and community responses to global change.

Paleoecological methods can be leveraged to understand how species have responded to past climate fluctuations, providing insights into predicting potential responses of species in the future. Nolan et al. (Reference Nolan, Overpeck, Allen, Anderson, Betancourt, Binney, Brewer, Bush, Chase, Cheddadi, Djamali, Dodson, Edwards, Gosling, Haberle, Hotchkiss, Huntley, Ivory, Kershaw and Jackson2018) analyzed 594 published paleoecological records to reveal changes in the composition and structure of terrestrial vegetation since the Last Glacial Period and predict the extent of ecosystem transformations under future emission scenarios. Pineda-Munoz et al. (Reference Pineda-Munoz, Wang, Lyons, Tóth and McGuire2021) investigated whether human-induced changes in species' geographic ranges have altered their climate niches using fossil records.

Combined approaches integrate above approaches based on the related mechanisms (Ureta et al., Reference Ureta, Ramírez-Barrón, Sánchez-García, Cuervo-Robayo, Munguía-Carrara, Mendoza-Ponce, Gay and Sánchez-Cordero2022). Pearson et al. (Reference Pearson, Stanton, Shoemaker, Aiello-Lammens, Ersts, Horning, Fordham, Raxworthy, Ryu, McNees and Akçakaya2014) combined ecological niche models with population demographic models to develop a generic life history method, which represents a species' extinction risk as the probability of zero abundance by 2100, rather than the proportion of species extinctions resulting from bioclimatic envelope contractions.

5.1 Leveraging the concept of telecoupling

As a global phenomenon, the impacts of climate change on species and ecosystems in one region can reverberate across borders, affecting ecosystems and species in distant regions through various pathways such as species competition, transboundary migration, and the interconnectedness of ecosystem service supply chains. This intricate global interplay finds elucidation through the telecoupling concept, which delineates the complex interconnections among global changes, environmental impacts, and social feedbacks across different regions worldwide. Within the telecoupling framework, each system comprises agents, causes, and effects, with connections forged through the exchange of information, material, energy, people, capital, and organisms (Liu et al., Reference Liu, Hull, Batistella, DeFries, Dietz, Fu, Hertel, Izaurralde, Lambin, Li, Martinelli, McConnell, Moran, Naylor, Ouyang, Polenske, Reenberg, Rocha, Simmons and Zhu2013).

For instance, climate change may disrupt patterns of species migration, prompting some species to relocate toward northern or higher-altitude areas in response to warming climates (Hulina et al., Reference Hulina, Bocetti, Campa, Hull, Yang and Liu2017). Such migrations can introduce new species to destination areas, altering local ecosystems and potentially precipitating local species extinction or ecosystem collapse. The telecoupling framework facilitates comprehension of the ramifications of these migrations on destination-area ecosystems and their repercussions on ecosystems in the source areas (López-Hoffman et al., Reference López-Hoffman, Diffendorfer, Wiederholt, Bagstad, Thogmartin, McCracken, Medellin, Russell and Semmens2017; Schröter et al., Reference Schröter, Koellner, Alkemade, Arnhold, Bagstad, Erb, Frank, Kastner, Kissinger, Liu, López-Hoffman, Maes, Marques, Martín-López, Meyer, Schulp, Thober, Wolff and Bonn2018). Furthermore, the impacts of climate change on ecosystem services can propagate through extensive supply chains, reshaping interdependencies among disparate ecosystems. The telecoupling concept aids in discerning how climate change influences the supply and demand of ecosystem services in diverse regions, while also evaluating the overarching stability of global ecosystem services (Hulina et al., Reference Hulina, Bocetti, Campa, Hull, Yang and Liu2017). Given that the impacts of climate change often transcend national boundaries, locally oriented conservation endeavors may yield adverse spillover effects, imperiling the sustainability of remote regions (Liu, Reference Liu2014). Thus, the telecoupling concept underscores the imperative of transnational ecosystem management and cooperation, entailing facets such as resource sharing, information exchange, and policy coordination.

5.2 Empirical research on climate change risks

Despite established theoretical frameworks, there is a pressing need for large-scale experimental efforts to rigorously test hypotheses and explore factorial experimental designs. Such studies should investigate the physiological, behavioral, and ecological responses of various species under changing climatic conditions. Recent literature underscores the importance of integrating empirical approaches with existing models to provide a comprehensive understanding of climate change-mediated responses. For instance, Glazier and Gjoni (Reference Glazier and Gjoni2024) emphasize that metabolism, a key driver of biological processes, is influenced by numerous intrinsic and extrinsic factors, including body size and environmental conditions. This need for empirical research becomes particularly evident when considering species distribution. Although it is commonly believed that species will migrate to higher elevations and latitudes as temperatures rise, Tagliari et al. (Reference Tagliari, Danthu, Leong Pock Tsy, Cornu, Lenoir, Carvalho-Rocha and Vieilledent2021) reveal that mean annual temperature is not the only limiting factor in determining species distribution. Instead, species may adapt their ranges in response to a variety of climate variables. For example, in tropical regions, many species are expected to move toward the equator to avoid the impacts of seasonal temperature fluctuations. This highlights the critical role of empirical studies in uncovering adaptive strategies and understanding the nuanced responses of species to climate change.

5.3 Harnessing the potential of geography

Geography studies have a critical role to play in addressing challenges such as climate change, biodiversity loss, and the provision of essential ecosystem services. The advent of remote-sensing technologies, geographic information systems, and the emergence of machine learning have revolutionized risk assessment of climate change on species and ecosystems (White et al., Reference White, Gregovich and Levi2018; Zamora-Gutierrez et al., Reference Zamora-Gutierrez, Rivera-Villanueva, Martínez Balvanera, Castro-Castro and Aguirre-Gutiérrez2021). Integrated mapping and modeling have proven to be invaluable tools for monitoring and assessing species and ecological changes on a large spatial scale (Yu et al., Reference Yu, Du, Dong, Zheng, Tu, Chen, Hao, Zhong, Peng, Zhao, Li, Yang, Fu, Yang and Gong2022). By incorporating these technologies into risk assessments, we can attain a comprehensive understanding of ecosystem dynamics and enhance the accuracy of predictions and warnings (Du et al., Reference Du, Yu, Chen, Li, Peng, Zheng, Hao, Yang, Guo and Gong2023; Ni et al., Reference Ni, Yu, Gong, Li and Zhao2023). Incorporating SDMs into comprehensive assessment models and establishing connections between species redistribution due to climate change and ecosystem integrity through large-scale multi-generational experiments are critical for a deeper understanding of the adaptive responses of organisms and ecosystems to environmental changes, presenting a central challenge (Cabral et al., Reference Cabral, Mendoza-Ponce, Pinto, Oberpriller, Mimet, Kieslinger, Berger, Blechschmidt, Brönner, Classen, Fallert, Hartig, Hof, Hoffmann, Knoke, Krause, Lewerentz, Pohle, Raeder and Zurell2023; Pecl et al., Reference Pecl, Araújo, Bell, Blanchard, Bonebrake, Chen, Clark, Colwell, Danielsen, Evengård, Falconi, Ferrier, Frusher, Garcia, Griffis, Hobday, Janion-Scheepers, Jarzyna, Jennings and Williams2017).

Harnessing the potential of geography also involves integrating the outcomes of climate change risk assessments on species and ecosystems into the design of new nature reserves and the formulation of conservation strategies. As the latest framework developed under the Convention on Biological Diversity, the Kunming–Montreal Global Biodiversity Framework was advanced by China during its presidency in 2022 (Shen et al., Reference Shen, Liu, Hanson, Wang, Locke, Watson, Ellis, Li and Ma2023). To achieve these ambitious objectives, it is necessary to integrate climate change considerations into biodiversity and ecosystem conservation and restoration efforts. Researchers have demonstrated that the inclusion of various aspects of vulnerability significantly influences spatial conservation priorities (Carvalho et al., Reference Carvalho, Brito, Crespo and Possingham2010; Crossman et al., Reference Crossman, Bryan and Summers2012; Thuiller et al., Reference Thuiller, Lavorel, Araújo, Sykes and Prentice2005). For example, incorporating the adaptability of species and ecosystems when determining priority conservation areas can enhance the representation of a wide range of species. However, prioritizing vulnerable species may reduce the overall representation of priority conservation areas for other species (Summers et al., Reference Summers, Bryan, Crossman and Meyer2012). Hence, in making decisions regarding conservation planning aimed at reducing the vulnerability of species and ecosystems to climate change, it is essential to fully acknowledge the sensitivity of spatial conservation priorities to different vulnerability components. In addition to identifying priority areas for climate change adaptation, there should be a focus on promoting sustainable land management and fostering international cooperation.

5.4 Developing early warning mechanism

Sudden changes in the structure, function, and composition of ecosystems occurring with little to no warning can have irreversible and far-reaching consequences for biodiversity and human societies (Newton et al., Reference Newton, Britton, Davies, Diaz, Franklin, Herbert, Hill, Hodder, Jones, Korstjens, Lamb, Olley, Pinder, Roberts and Stafford2021). Pressures from global climate change manifest in the form of chronic ‘presses’ and/or acute ‘pulses’, leading to ecosystem collapses. Responses to climate change pressures on ecosystems can be categorized into four collapse profiles: abrupt, smooth, stepped, and fluctuating. Predicting which species and ecosystems are most susceptible to the effects of climate warming is crucial for guiding conservation strategies to minimize species extinctions and ecosystem collapses (van Heerwaarden & Sgrò, Reference van Heerwaarden and Sgrò2021). Concerning climate change risk warnings for ecosystems, polar regions, semi-arid areas, and small islands are widely acknowledged as the habitats most susceptible to influence. Regarding climate change risk warnings for species, forecasts (Clusella-Trullas et al., Reference Clusella-Trullas, Blackburn and Chown2011; Kellermann et al., Reference Kellermann, Overgaard, Hoffmann, Fløjgaard, Svenning and Loeschcke2012; Sunday et al., Reference Sunday, Bates and Dulvy2012) indicate that tropical/mid-latitude species face the highest risks because they already reside near their upper critical thermal limits.

However, complex systems often yield unforeseen outcomes and thresholds. Assessing trends before and after climate change at the species and ecosystem levels typically requires decades of continuous data, and acquiring long-term datasets for species and biological systems can be challenging. Fossil records offer valuable insights into how species and ecosystems have responded to climate change (Finnegan et al., Reference Finnegan, Anderson, Harnik, Simpson, Tittensor, Byrnes, Finkel, Lindberg, Liow, Lockwood, Lotze, McClain, McGuire, O'Dea and Pandolfi2015). Nevertheless, understanding their recent ongoing responses necessitates the collection of various environmental and biological parameters, real-time data streams, and high-quality near-real-time data (Pettorelli et al., Reference Pettorelli, Laurance, O'Brien, Wegmann, Nagendra and Turner2014). With ongoing advancements in atmospheric science, ecology, and computer science, it is imperative to enhance climate change warning methods and tools and integrate this information into decision support frameworks for species and ecosystems. The above-mentioned early warning efforts also require substantial policy, financial support, and international collaboration to establish the necessary monitoring plans to record and respond to climate change. Even with these efforts, nature's response will remain dynamic, and the mechanisms by which species and ecosystems respond to climate change may not be fully understood or predictable now and in the future. This uncertainty calls for flexible, dynamic management to swiftly adapt to changing conditions within limited timeframes, seize opportunities, and mitigate adverse impacts.