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Article

Distribution and Conservation Gaps of Nautilus pompilius: A Study Based on Species Distribution Models

by
Xianshui Lai
1,2,
Linlin Zhao
2,
Wenhao Huang
3,
Lusita Meilana
4,
Tingting Li
2,
Kaiyu Liu
1,2,
Bei Wang
2,
Bailin Cong
2 and
Shenghao Liu
2,*
1
School of Advanced Manufacturing, Fuzhou University, Jinjiang 362200, China
2
Marine Ecology Research Center, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
3
College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
4
Center for Coastal and Marine Resources Studies, International Research Institute for Maritime, Ocean and Fisheries, IPB University, Bogor 16680, Indonesia
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(4), 243; https://doi.org/10.3390/d17040243
Submission received: 18 February 2025 / Revised: 23 March 2025 / Accepted: 26 March 2025 / Published: 28 March 2025
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Abstract

:
Nautilus pompilius, a ‘living fossil’ of the oceans, is crucial to the study of biological evolution and paleontology. However, the species’ habitat has been severely impacted by global climate change. Based on this, species distribution models and conservation gap analyses were conducted under current and future climate scenarios. The results revealed that the current habitats for N. pompilius were primarily located in the coastal waters of Australia, Indonesia, and the Philippines. Under the Representative Concentration Pathway (RCP) 4.5 scenario, suitable habitat is projected to decline by 4.8% in the 2050s and 5.3% in 2100s. This loss is expected to intensify under higher emission scenarios, particularly RCP 8.5, where the reduction could reach 15.4% in the 2100s. Conservation gap analysis indicates that while nearly 30% of suitable habitats fall within marine protected areas (MPAs), many vulnerable regions remain unprotected. Future MPA establishment should strategically address these conservation gaps, particularly in coastal waters such as the Gulf of Carpentaria, the Arafura Sea, and the southern edge of the Timor Sea. This study provides critical insights into the distribution patterns and conservation needs of N. pompilius, emphasizing the urgent need for targeted conservation efforts to protect this endangered species.

1. Introduction

Biodiversity is essential for ecosystem services and the survival of human societies, yet it is increasingly threatened by climate change and human activities [1]. These factors have severely impacted marine environments, leading to reduced primary productivity, increased water temperatures, hypoxia, and ocean acidification [2]. Such rapid environmental changes may exceed the physiological thresholds of many species, causing habitat loss and even extinction [3]. Moreover, climate-driven invasions and extinctions disrupt marine community structures and biodiversity, which in turn affects ecosystem functioning and services [4]. The decline of native species, in turn, reduces predator populations and destroys critical habitats, further destabilizing ecosystems [5].
Nautilus pompilius, an iconic marine mollusk, belongs to the class Cephalopoda, order Nautilida, family Nautilidae, and genus Nautilus. It plays a vital role in the Indo-Pacific region [6], providing essential ecosystem services such as habitat structure for benthic organisms, water filtration, and serving as a food source for other species [7]. As nautilids have survived for hundreds of millions of years [8], N. pompilius offers valuable insights into ecological adaptations and evolutionary responses to environmental shifts. However, its life history traits, including late maturity, long gestation periods, low reproductive rates, and a long lifespan [9,10,11,12], make it highly vulnerable to external threats. In the past, overfishing led to a significant decline in its population [13]. Although commercial fishing pressure has decreased since its inclusion in Appendix II of the Convention on International Trade in Endangered Species (CITES), N. pompilius now faces a growing challenge: global climate change. In particular, rising ocean temperatures may fragment its distribution range [14], further endangering its survival and potentially triggering cascading ecological consequences. Despite these risks, limited research exists on its regional distribution and response to climate change. Thus, evaluating the effects of climate change on N. pompilius habitats and developing long-term conservation plans is critical for both the species and ecosystem health.
Species distribution models (SDMs) are statistical and machine learning tools that predict suitable habitats by correlating species’ occurrence points with environmental variables [15]. In the past two decades, the use of SDMs has expanded, not only to predict species distributions but also to support biodiversity conservation and ecosystem management. For example, SDMs have been used to model the potential habitat distribution of Tridacna maxima, providing a scientific basis for conservation planning [16]. They have also assessed the invasion risk of the Mytella strigata in the Indian Ocean, identifying key environmental factors driving its spread [17]. Furthermore, SDM-based studies on the Thyasira tokunagai suggest potential habitat loss due to rising temperatures, while other benthic species have shown shifts in response to climate change [18]. It was discovered that integrated species distribution models have high accuracy [19]. Ensemble SDMs, which combine multiple individual models, have gained popularity for improving prediction accuracy and generalizability [20]. Biomod2, a widely used ensemble modeling tool in R, allows for more robust predictions of species distributions by incorporating diverse data inputs and improving model performance [21].
Marine protected areas (MPAs) play a vital role in preserving marine biodiversity by mitigating the impact of external factors in ecologically sensitive regions [22]. Less than 9% of the world’s oceans are protected, as of now (https://www.protectedplanet.net/; accessed on 17 December 2024). This is far below the “30 × 30 target” set by the Kunming-Montreal Global Biodiversity Framework, which aims to protect 30% of the ocean by 2030 (Convention on Biological Diversity; https://www.cbd.int/, accessed on 10 October 2024). As a result, an increasing number of studies have focused on establishing protected areas to mitigate the loss of biodiversity [23,24]. Gap analysis (a geographic approach to protect biological diversity) is a commonly used technique among methods for determining protected areas [25]. It systematically compares the coverage of existing protected areas with species distribution, allowing for the rapid identification of “gaps” in the protection network [26]. However, N. pompilius’ habitat distribution and conservation status are poorly understood.
Herein, this study used Biomod2 to map the potential habitats of N. pompilius under various climate scenarios, assessed the impact of climate change on habitat, and identified conservation gaps. The findings will provide valuable insights into the spatial ecology and conservation needs of N. pompilius, offering crucial information for its future conservation and management.

2. Materials and Methods

2.1. Occurrence Data Collection and Preprocessing

The study focused on the central Indo-West Pacific region, which is the primary distribution zone for Nautilus pompilius (112° E–162° E, 24° S–21° N; Figure S1). We used “Nautilus pompilius” to obtain occurrence data from online databases: the Ocean Biodiversity Information System (OBIS; https://obis.org; accessed on 22 May 2024), and the Global Biodiversity Information Facility (GBIF; https://www.gbif.org; accessed on 22 May 2024) [16,27]. To reduce sampling bias [28], we applied spatial thinning to the occurrence data using the R package spThin (version 0.2.0), retaining only one occurrence point per 5 × 5 arc-minute grid (~9.2 × 9.2 km), aligning with the spatial resolution of the environmental predictors [29]. After spatial thinning, we removed duplicate records, erroneous entries, and records with missing data. The final dataset retained 112 occurrence records for analysis. Given that presence–absence models generally outperform presence-only models [30,31], and that true absence data for N. pompilius are difficult to ascertain, we used a randomization method to generate 112 pseudo-absence records [32]. These pseudo-absence records were combined with the 112 retained presence records to create a balanced final dataset, which was then used for subsequent species distribution modeling analysis.

2.2. Selection of Environmental Variables

Seven environmental variables relevant to the physiology of N. pompilius were selected as predictors for the species distribution model (Table 1). To minimize the effects of multicollinearity among the environmental variables, Pearson’s correlation coefficients were first calculated for all pairwise combinations of the seven variables using the R statistical software [33]. Pairs of variables with correlation coefficients greater than 0.7 were assessed, and one variable from each pair was removed to reduce redundancy [17,34]. In cases where the correlation exceeded 0.7, the importance of each predictor was further evaluated using the random forest algorithm (Figures S2 and S3). For both current and future distribution predictions, five environmental variables were used: water temperature, primary productivity, light at bottom, slope, and distance to land (Table 1). Water temperature was selected due to its potential lethal effects on N. pompilius if it exceeds certain thresholds [4]. Primary productivity was included because regions with high productivity tend to offer abundant food sources, attracting marine organisms [35]. Light at the bottom was also considered, as N. pompilius is photosensitive. Nautilids often remain within reef slopes [36], which are a key factor in their distribution. Given the lack of detailed topographic data, distance to land was used as a proxy for topographic features, as it is generally correlated with local topography [37].
Environmental data were sourced from two key databases: Bio-ORACLE version 2.2 (https://www.bio-oracle.org; accessed on 26 April 2024) [38] provided benthic layer averages for water temperature, primary productivity, and light at the bottom, while the Global Marine Environmental Database (GMED; https://gmed.auckland.ac.nz; accessed on 18 April 2024) [39] supplied data for slope and distance to land [40,41]. All environmental variables had a spatial resolution of 5 arc minutes (~9.2 km at the equator). For current distribution models, present-day environmental conditions were used. For future predictions, temperature projections under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 were considered for the 2050s (2040–2050) and 2100s (2090–2100) [33,41]. The other four environmental variables were assumed to remain unchanged, with values consistent with present-day conditions. To evaluate temperature variations under different climate scenarios, we extracted temperature values at each occurrence point and generated boxplots to visualize their distribution patterns.

2.3. Species Distribution Modeling and Evaluation

The Biomod2 package in R (version 4.3.1) was used for modeling species distributions [42], leveraging 10 different algorithms: generalized linear models (GLM), generalized boosted models (GBM), generalized additive models (GAM), classification tree analysis (CTA), multivariate adaptive regression splines (MARS), artificial neural networks (ANN), one rectilinear envelope (SRE), flexible discriminant analysis (FDA), random forests (RF), and MaxEnt (ME) [21]. Model performance was assessed using a 5-fold cross-validation method [43]. Species distribution models were constructed at the species level, with occurrence records randomly divided into five subsamples. In each iteration, four groups were used for model training, and the fifth group was reserved for testing to evaluate the model’s prediction accuracy [32]. This process was repeated ten times, and the average accuracy across these iterations was used to represent overall model performance [27].
Model evaluation metrics included the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC) for each model to assess predictive accuracy [44,45]. Models with an AUC greater than 0.8 and a TSS greater than 0.7 were considered to have superior overall performance [20]. Models meeting these criteria were selected for subsequent analyses. To assess the relative importance of each predictor variable, a randomization method was applied [46]. This involved calculating the Pearson correlation coefficient (r) between predictions based on all predictor variables and predictions where the variable under assessment was randomly permuted. The relative importance of each environmental predictor was then determined as 1 − |r| [32].
Response curves were generated to illustrate the gradient change in species occurrence probability across significant predictor variables, allowing us to examine the relationship between species occurrence and environmental factors [47]. A weighted average method was employed to integrate all eligible species distribution models (SDMs) and construct a probabilistic prediction map of N. pompilius occurrence locations [48,49]. Model results were presented through the habitat suitability index (HSI), with values ranging from 0 to 1 [50]. Values closer to 1 indicate higher habitat suitability, while values closer to 0 indicate lower suitability [51]. Values closer to 1 indicate higher habitat suitability, while values closer to 0 indicate lower suitability. For ease of interpretation, the continuous habitat suitability prediction map was converted to a binary format by maximizing the TSS probability threshold [52]. This binary output was then used to calculate changes in habitat area by comparing current and future distribution predictions, where a value of 0 indicates unsuitable habitat and a value of 1 indicates suitable habitat.

2.4. Conservation Gap Analysis

The study categorized habitat suitability indices into three classes under different climate scenarios to facilitate understanding and identification of species conservation status across varying habitat suitability levels on distribution maps [53]. It then analyzed conservation gaps in habitats at these different classification levels. The habitat suitability index (i.e., 0.6–0.936) was first extracted from Biomod2 integrated predictive distribution maps using the raster calculator in QGIS software (version 3.34.4). In many distribution modeling studies, habitat suitability classification is desirable for easy interpretation [54,55]. Based on these continuous linear values, species distribution maps for the current period and future projections (2050s and 2100s) were then categorized into three habitat suitability classes: low (0.6–0.712), medium (0.713–0.824), and high (0.825–0.936). We acquired geographic maps of marine protected areas (MPAs) from the Protected Planet (https://www.protectedplanet.net/; accessed on 13 June 2024) [26]. Using QGIS software, we superimposed the predicted distribution map of N. pompilius onto the MPAs [25]. Subsequently, we computed the percentage of suitable habitat encompassed by and outside the boundaries of the MPAs. Finally, by incorporating current and projected climate change scenarios, we conducted a comprehensive analysis to assess the level of protection the species would receive.

3. Results

3.1. Model Performance and Predictor Variable Analysis

The varying AUC and TSS values demonstrated distinct predictive performances among the 10 modeling algorithms (Figure 1a). The artificial neural network (ANN) algorithm outperformed all others, exhibiting the highest predictive accuracy, while the stepwise regression (SRE) algorithm performed the least effectively. Based on the designated cut-off values for TSS and AUC, seven modeling algorithms were chosen for integrated modeling and descriptive analysis, specifically ANN, FDA, GAM, GBM, GLM, RF, and MARS. The ensemble models yielded TSS and AUC values of 0.792 and 0.907, respectively, signifying a high level of predictive accuracy. Among the five predictor variables, temperature had a significant impact on species distribution (0.600 ± 0.020), followed by distance to land (0.260 ± 0.014). Bottom light (0.069 ± 0.013), slope (0.061 ± 0.008), and primary productivity (0.048 ± 0.009) contributed relatively less to the model’s predictive performance (Figure 1b). Temperature response curves varied across algorithms, suggesting different upper temperature limits, but generally indicated that the species is most likely to survive within a temperature range of 11 °C to 24 °C (Figure 2a). Future projections under both RCP 4.5 and RCP 8.5 scenarios indicate a rise in median temperature in the 2050s, with a more pronounced warming trend in the 2100s, especially under RCP 8.5. Moreover, temperature variability increases, leading to more frequent extreme high values (Figure S4). Additionally, the distance to land response curves revealed a preference for habitats located within 4 km of the shoreline (Figure 2b).

3.2. Current and Future Potential Distribution

Projections suggest that the coastal waters of Australia, Indonesia, the Philippines, Papua New Guinea, and the Solomon Islands offer suitable habitats for N. pompilius (Figure 3). Species distribution models (SDMs) consistently indicate a decline in suitable habitat under future climate scenarios. Specifically, under the RCP 4.5 and RCP 8.5 scenarios for 2050, there is a respective decrease in suitable habitat area by 9.5% and 9.7%, with minor increases of 4.7% and 4.4%. The overall loss of habitat area by 2050 is estimated at 4.8% and 5.3%, respectively. By 2100, under the RCP 4.5 scenarios, there is a projected loss of 5.3% in habitat area. Notably, the RCP 8.5 scenarios for 2100 anticipate a substantial reduction of over 475,007 km2 of suitable habitat, equating to a 20.5% decrease and an overall loss of 15.4% (Table 2). Additionally, future habitat projections under various RCP scenarios indicate that the southern regions of the Gulf of Carpentaria, the Arafura Sea, and the Timor Sea are likely to become unsuitable for the species (Figure 4).

3.3. Analysis of the Conservation Status

In regions classified as low suitability, the current protected area covers 17.0% of the N. pompilius range. This proportion is projected to range between 17.2% and 23.2% under future climate scenarios. Within the medium suitability category, the current protected area spans 155,100.6 km2, representing 18.1%, and is expected to increase to between 26.1% and 29.0% in the future. For areas deemed highly suitable, the current protected area is 535,758.0 km2, equivalent to 46.6%, and is anticipated to surpass 47% under future climate scenarios. Overall, the proportion of protected areas for N. pompilius is forecasted to remain stable at about 29% in future climate scenarios, compared to the current coverage of 28.3% (Table 3). The overlaid map reveals significant overlaps between the Great Barrier Reef, oceanic shoals, and Kimberley protected areas with N. pompilius habitats. While the Coral Sea and Palau boast large protected areas, they cover fewer suitable habitats (Figure 5).

4. Discussion

4.1. Vulnerability to Temperature

We constructed ensemble species distribution models for N. pompilius to forecast its potential distribution under current and future climate change scenarios. The model incorporated five environmental factors, and the relative significance of each predictor variable and the identified relationships indicate that the model is biologically robust [56]. The suitable habitat distribution for N. pompilius under current environmental conditions is primarily in the nearshore waters of northern Australia, the Philippines, Brunei, and Indonesia. The response curves for distance to land revealed a preference for habitats within 4 km of the shoreline, likely due to the availability of suitable marine features like reefs and seamounts that support foraging and shelter [36,57]. Furthermore, due to the risk of their shells imploding below 800 m, habitats near the coastline are crucial for accessing favorable conditions [58]. The predicted habitat temperature range is 11~24 °C, which is close to the known habitat temperatures [59]. The loss in suitable habitat areas of N. pompilius owing to climate change in our study agrees with earlier findings that highlight the increasing risks to biodiversity due to temperature change [59,60]. Temperature accounted for 60% of the contribution to N. pompilius’s distribution, as indicated by our study. The ensemble models that describe the temperature preferences of this species highlight the unique ecological requirements and the potential risks of exceeding physiological limits. We found that the temperature range of 24 °C to 28 °C may not be suitable for this species, suggesting that extreme thermal conditions could severely impact populations [61]. However, the precise ecological tipping point, beyond which populations can no longer sustain themselves, requires further investigation to confirm.
Deep-sea organisms, including nautiluses, are highly sensitive to temperature fluctuations due to the stable thermal conditions of their habitats [59,62]. Furthermore, N. pompilius inhabits the tropical Indo-Pacific [6], where it may already be near its physiological temperature limit, leaving it with minimal tolerance for further warming [63]. Extreme temperatures can disrupt metabolic processes and constrain species distribution by exceeding thermal tolerance thresholds [64]. For N. pompilius, rising temperatures accelerate metabolism and increase oxygen demand, yet ocean warming is often accompanied by declining dissolved oxygen levels [65], posing a severe threat to its aerobic respiration. Additionally, elevated temperatures may impair larval development and reproductive success [66], ultimately reducing population renewal capacity and heightening extinction risk. Temperature response curves indicate that N. pompilius occurrence probability increases with rising temperatures, stabilizing between 11 °C and 24 °C before declining sharply beyond 24 °C. This aligns with its known thermal limits, as the genus’s maximum survival temperature is approximately 25 °C [62]. Future climate projections suggest increasing temperature variability, particularly under the RCP 8.5 scenario by 2100, where extreme warming could impose significant physiological stress on N. pompilius. These findings highlight its vulnerability to ocean warming, underscoring the need to incorporate thermal constraints into habitat conservation planning. To mitigate these impacts, conservation efforts should integrate climate resilience measures, while broader strategies such as advancing carbon neutrality policies and reducing greenhouse gas emissions are essential for slowing global warming and preserving suitable habitats.
These findings are essential to comprehending the ecological requirements of the species and can guide conservation plans to guarantee N. pompilius’s long-term existence in facing the changing climate circumstances. Moreover, in the future, further research into the specific geographic and oceanographic factors shaping N. pompilius distribution is critical to informing conservation strategies for these ancient marine organisms [67,68].

4.2. Changes in Suitable Habitat

Thermal tolerance limits are one of the factors affecting species distribution [69]. Species may persist by moving to climatically favorable regions [70]. Consequently, the geographical distribution of the species may extend to other regions that provide optimal climatic conditions for their physiology. As future temperatures rise, N. pompilius tends to move towards cooler waters, with an anticipated increase in suitable habitat in the coastal sea between Port Hedland Bay and Broome in Western Australia. This shift may be influenced by factors such as food availability, optimal local temperatures, and geographic features that serve as natural refuges for the species [35,71].
In addition, if local environmental conditions become more favorable for a species, its abundance in these areas will also increase [72]; for instance, in the South China Sea, this may occur because the current temperature is below the survival limit of N. pompilius, but as the temperature increases, the area becomes a suitable habitat for the species. This suggests that in the context of global warming, specific species are not spreading singularly from low to high latitudes but are expanding into areas with suitable environmental conditions for their growth [73].
However, if climatically suitable areas are reduced and species are unable to change their distribution, their native range may be restricted, increasing the risk of local extinction [72]. We found that, in the Gulf of Carpentaria, the Arafura Sea, and the southern Timor Sea, the severe loss of suitable habitats may be due to most sea temperatures exceeding the upper thermal limits of N. pompilius. Under future climate change scenarios, the areas for suitable habitats of N. pompilius all show a decline, and this decline is particularly evident in nearshore sea. Given the species’ preference for coral reef-associated environments [6], climate change-driven coastal habitat degradation, such as coral reef bleaching, may accelerate habitat loss [74], indirectly impacting N. pompilius by reducing prey availability and shelter. The decline and transfer in suitable habitats for N. pompilius is expected to continue under future climate change scenarios, highlighting the urgent need for effective conservation strategies to mitigate these risks.
It is worth mentioning that this prediction is based on species distribution models (SDMs), which currently assess horizontal distribution across varying latitudes and longitudes. These models, however, are unable to predict vertical distribution (e.g., at different depths within the same geographic coordinates). Therefore, while vertical migration is a plausible behavioral response for N. pompilius, the limitations of current modeling tools prevent such predictions from being integrated into this analysis. Further refinement of SDMs to incorporate depth-related variables would provide a more comprehensive understanding of potential species distribution changes under future climatic scenarios. Furthermore, climate change may exacerbate the effects of other anthropogenic disturbances, such as fishing, on marine organisms, increasing their vulnerability to extinction [13,75,76]. Expanding our modeling approach to integrate additional human-induced stressors in future research would help address this issue more effectively.

4.3. Management and Conservation

In the four future climate scenarios, the area of suitable habitat is reduced by not less than 4.8% or even 15.4%. It should be noted that the ensemble model has some limitations, due to the few environmental variables considered and the omission of biological variables affecting N. pompilius. Nevertheless, the impact of climate change on the marine environment will undoubtedly result in future habitat reductions [16,77]. According to the conservation gap study, the coverage of marine protected areas (MPAs) within the habitat of N. pompilius meets the conservation target recommended by the Kunming-Montreal Global Biodiversity Framework [78], which aims to protect 30% of marine areas by 2030. This indicates a relatively optimistic protected status for the species. However, in the face of future climate change, the vulnerable habitats of N. pompilius, such as expansion and contraction areas [21], are not well protected. Based on the above results, we recommend an adaptive management strategy based on future distribution projections. Additional MPAs should be established in important habitats, such as the Gulf of Carpentaria, the Arafura Sea, and the coastal sea between Port Hedland and Broome, to pay attention to changes in suitable habitat and prevent the risk of the local extinction of N. pompilius. The scope and location of protected areas should be adjusted according to changes in distribution, to ensure that the protected areas effectively cover the suitable habitats. This study reinforces previous findings on the importance of expanding and adapting protected areas for biodiversity conservation in a changing environment [16]. It further highlights the need for precise, proactive management to safeguard vulnerable habitats, mitigate climate change impacts, and support the long-term survival of N. pompilius while maintaining ecological stability.
Marine protected areas (MPAs) serve as essential sites for long-term ecological monitoring and research, enabling the collection of critical data on species populations, migration patterns, and responses to climate change [79]. Additionally, MPAs play a key role in preserving vital ecosystems [80], including coral reefs and seamounts, which provide shelter and foraging grounds for N. pompilius. To enhance conservation outcomes, regular evaluations of MPA effectiveness are necessary to ensure adequate habitat coverage and prevent destructive activities. Finally, N. pompilius is distributed in the coastal sea of countries, and all countries should strengthen international cooperation and jointly implement a conservation strategy of a MPA network, to ensure that N. pompilius is effectively protected throughout its distributional range.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d17040243/s1. Figure S1. Map of the study area; Figure S2. Results of Pearson correlation analysis for seven environmental variables; Figure S3. Relative importance of environmental factors; Figure S4. Temperature distribution across different scenarios.

Author Contributions

Conceptualization, X.L. and S.L.; methodology, X.L.; software, X.L.; validation, W.H. and B.C.; formal analysis, B.C.; investigation, K.L. and B.W.; resources, S.L. and L.Z.; data curation, T.L.; writing—original draft preparation, X.L.; writing—review and editing, L.M., T.L., and S.L.; visualization, W.H.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key Research and Development Program of China (2022YFF0802204 and 2023YFC2811402), China-Indonesia Deep-Sea Habitat Survey and Research Capacity Building Cooperation Program, and China-ASEAN Blue Partnership Construction Program.

Institutional Review Board Statement

Ethical review and approval were waived for this study because all data sources for Nautilus pompilius were downloaded online.

Data Availability Statement

The data presented in this study are openly available in the Ocean Biodiversity Information System (OBIS; https://obis.org; accessed on 22 May 2024), Global Biodiversity Information Facility (GBIF; https://www.gbif.org; accessed on 22 May 2024), Bio-ORACLE v2.2 (https://www.bio-oracle.org; accessed on 26 April 2024), Global Marine Environmental Database (GMED; https://gmed.auckland.ac.nz; accessed on 18 April 2024), and Protected Planet (https://www.protectedplanet.net; accessed on 17 December 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
N. pompiliusNautilus pompilius
MPAsMarine protected areas
SDMsSpecies distribution models
Gap analysisA geographic approach to protect biological diversity

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Figure 1. (a) Predictive performance of the models in projecting the distribution. The dashed and solid lines represent cut-off levels based on the true skill statistic (TSS = 0.7) and area under the receiver operating characteristic curve (AUC = 0.8), respectively; (b) Importance of environmental variables to the distribution of Nautilus pompilius, with higher average values indicating greater importance.
Figure 1. (a) Predictive performance of the models in projecting the distribution. The dashed and solid lines represent cut-off levels based on the true skill statistic (TSS = 0.7) and area under the receiver operating characteristic curve (AUC = 0.8), respectively; (b) Importance of environmental variables to the distribution of Nautilus pompilius, with higher average values indicating greater importance.
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Figure 2. Response curve of the occurrence probability of Nautilus pompilius to (a) mean water temperature (°C) and (b) distance to land (km). GLM—generalized linear models, GBM—generalized boosted models, GAM—generalized additive models, ANN—artificial neural networks, FDA—flexible discriminant analysis, RF—random forests, and MARS—multivariate adaptive regression splines.
Figure 2. Response curve of the occurrence probability of Nautilus pompilius to (a) mean water temperature (°C) and (b) distance to land (km). GLM—generalized linear models, GBM—generalized boosted models, GAM—generalized additive models, ANN—artificial neural networks, FDA—flexible discriminant analysis, RF—random forests, and MARS—multivariate adaptive regression splines.
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Figure 3. Habitat suitability map of Nautilus pompilius projected by ensemble models under current climate scenario. (a) The continuous habitat suitability prediction. (b) Binary output of habitat suitability. The color gradient indicates variations in habitat suitability on the left (green = highest (1) and yellow = lowest (0)); green colors indicate suitable areas, and white colors represent unsuitable ranges on the right. A value of zero indicates predicted absence and one indicates predicted presence.
Figure 3. Habitat suitability map of Nautilus pompilius projected by ensemble models under current climate scenario. (a) The continuous habitat suitability prediction. (b) Binary output of habitat suitability. The color gradient indicates variations in habitat suitability on the left (green = highest (1) and yellow = lowest (0)); green colors indicate suitable areas, and white colors represent unsuitable ranges on the right. A value of zero indicates predicted absence and one indicates predicted presence.
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Figure 4. Range shifts in habitat suitability of Nautilus pompilius under future climate conditions: (a) under the RCP 4.5 scenarios in the 2050s, (b) under the RCP 8.5 scenarios in the 2050s, (c) under the RCP 4.5 scenarios in the 2100s, and (d) under the RCP 8.5 scenarios in the 2100s. Blue represents suitable areas that will become unsuitable in the future, green areas are projected to be suitable under both current and future climates, red indicates areas that will become suitable in the future, and white represents unsuitable areas.
Figure 4. Range shifts in habitat suitability of Nautilus pompilius under future climate conditions: (a) under the RCP 4.5 scenarios in the 2050s, (b) under the RCP 8.5 scenarios in the 2050s, (c) under the RCP 4.5 scenarios in the 2100s, and (d) under the RCP 8.5 scenarios in the 2100s. Blue represents suitable areas that will become unsuitable in the future, green areas are projected to be suitable under both current and future climates, red indicates areas that will become suitable in the future, and white represents unsuitable areas.
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Figure 5. Analysis of the conservation gap for Nautilus pompilius under current and future climate scenarios: (a) current climate scenario, (b) the RCP 4.5 scenarios in the 2050s, (c) the RCP 8.5 scenarios in the 2050s, (d) the RCP 4.5 scenarios in the 2100s, and (e) the RCP 8.5 scenarios in the 2100s. Light green indicates marine protected areas (MPAs); light blue indicates low suitability habitat (Low); dark blue indicates moderately suitable habitat (Medium); and orange indicates highly suitable habitat (High).
Figure 5. Analysis of the conservation gap for Nautilus pompilius under current and future climate scenarios: (a) current climate scenario, (b) the RCP 4.5 scenarios in the 2050s, (c) the RCP 8.5 scenarios in the 2050s, (d) the RCP 4.5 scenarios in the 2100s, and (e) the RCP 8.5 scenarios in the 2100s. Light green indicates marine protected areas (MPAs); light blue indicates low suitability habitat (Low); dark blue indicates moderately suitable habitat (Medium); and orange indicates highly suitable habitat (High).
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Table 1. Seven environment variables were initially selected for the model analysis. “√” indicates the variable was retained after covariate estimation, and “×” indicates the variable was removed because it was highly correlated with other variables. The environment variable is the average environment variable of the marine benthic layer.
Table 1. Seven environment variables were initially selected for the model analysis. “√” indicates the variable was retained after covariate estimation, and “×” indicates the variable was removed because it was highly correlated with other variables. The environment variable is the average environment variable of the marine benthic layer.
Environment VariableUnitSourceUsed (√) or Not (×)
Water temperature°Chttps://www.bio-oracle.org√
Dissolved molecular oxygenmol·m−3https://www.bio-oracle.org×
Primary productivityg·m−3·day−1https://www.bio-oracle.org√
Light at bottom-https://www.bio-oracle.org√
Water depthmhttp://gmed.auckland.ac.nz×
Slope-http://gmed.auckland.ac.nz√
Distance to landkmhttp://gmed.auckland.ac.nz√
Table 2. Range size change for Nautilus pompilius under future climate scenarios (%).
Table 2. Range size change for Nautilus pompilius under future climate scenarios (%).
Future Climate ScenariosLossGainNet Change
2050s RCP 4.59.54.7−4.8
2050s RCP 8.59.74.4−5.3
2100s RCP 4.510.65.3−5.3
2100s RCP 8.520.55.1−15.4
Table 3. Change in protected area of Nautilus pompilius under future climate scenarios (%).
Table 3. Change in protected area of Nautilus pompilius under future climate scenarios (%).
ValueCurrent2050s RCP 4.52050s RCP 8.52100s RCP 4.52100s RCP 8.5
Low17.017.220.820.623.2
Medium18.126.127.327.229.0
High46.650.447.748.547.2
Total28.329.8530.0730.1629.34
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Lai, X.; Zhao, L.; Huang, W.; Meilana, L.; Li, T.; Liu, K.; Wang, B.; Cong, B.; Liu, S. Distribution and Conservation Gaps of Nautilus pompilius: A Study Based on Species Distribution Models. Diversity 2025, 17, 243. https://doi.org/10.3390/d17040243

AMA Style

Lai X, Zhao L, Huang W, Meilana L, Li T, Liu K, Wang B, Cong B, Liu S. Distribution and Conservation Gaps of Nautilus pompilius: A Study Based on Species Distribution Models. Diversity. 2025; 17(4):243. https://doi.org/10.3390/d17040243

Chicago/Turabian Style

Lai, Xianshui, Linlin Zhao, Wenhao Huang, Lusita Meilana, Tingting Li, Kaiyu Liu, Bei Wang, Bailin Cong, and Shenghao Liu. 2025. "Distribution and Conservation Gaps of Nautilus pompilius: A Study Based on Species Distribution Models" Diversity 17, no. 4: 243. https://doi.org/10.3390/d17040243

APA Style

Lai, X., Zhao, L., Huang, W., Meilana, L., Li, T., Liu, K., Wang, B., Cong, B., & Liu, S. (2025). Distribution and Conservation Gaps of Nautilus pompilius: A Study Based on Species Distribution Models. Diversity, 17(4), 243. https://doi.org/10.3390/d17040243

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