Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin
Abstract
1. Introduction
2. Results
2.1. Correlation Between Climate and Demographic Data
2.2. Clustering in Operational Territorial Units (OTUs)
2.3. Principal Component Analysis of Climate–Abundance Relationships
2.4. Deterministic Population Projection Based on PCA-Derived Predictive Algorithm
2.5. Comparison of Population Projections Derived from Empirical and Climate-Driven Models
3. Discussion
- (1)
- Incorporating PCA-derived climatic variable weights into the population projection algorithm constitutes a multivariate approach to link environmental variability with demographic trends in relict fern populations. This framework allows for the simultaneous consideration of key climatic drivers and their combined influence on population trajectories. However, this methodology has inherent limitations. The model’s linear and additive structure may oversimplify complex biological processes that are often non-linear and influenced by threshold effects, feedback mechanisms, or interactions among climatic and ecological factors. Furthermore, it assumes that the principal components captured by PC1 and PC2 fully represent the climatic drivers of population change, potentially neglecting other important abiotic or biotic influences. Critical demographic processes such as reproduction, mortality, dispersal, and genetic variability are also absent from this model, limiting its ability to fully capture population viability.
- (2)
- The deterministic survival projection model, grounded in empirical annual rates derived from a decade of census data, offers a practical tool for projecting population trends. By capturing the net effects of demographic and environmental factors observed during the study period, the model reflects the integrated trajectory of population change. However, its assumption of temporal constancy in demographic conditions, given that the bootstrap simulations are based solely on past observed rates, limits its reliability under dynamic scenarios, such as those induced by climate change, land-use transformations, or unforeseen ecological disturbances. The model does not account for demographic stochasticity, density dependence, interannual variability, or the impact of rare but consequential events like extreme droughts or disease outbreaks, all of which can be particularly influential in small, isolated populations.
- (3)
- The divergence between observed and modeled population abundances has been used as indicator of conservation effectiveness. While this approach offers valuable preliminary insights, it is important to acknowledge that such differences may also arise from a range of other ecological and demographic dynamics not fully captured in the current modeling framework. These include natural population variability and external environmental factors that are inherently challenging to parameterize comprehensively. Additionally, model–data mismatch is an expected outcome when dealing with complex ecological systems. As such, while the observed–predicted discrepancies can help identify potential conservation outcomes, they should be interpreted within the broader context of model assumptions, data limitations, and ecological variability.
4. Materials and Methods
4.1. Study Area
4.2. Ecology of the Studied Species and Key Threats
- (1)
- Habitat degradation. The conversion of native laurel and mixed forests to monoculture plantations, particularly Eucalyptus spp., leads to soil desiccation and loss of shaded, humid niches. Wildfires, especially prevalent in mainland Portugal, further threaten C. macrocarpa, already classified as Critically Endangered in that region. Similarly, D. caudatum and P. incompleta are highly sensitive to the degradation of hygrophilous forest ravines and streambanks where they occur.
- (2)
- Hydrological alterations, such as water extraction, river channeling, and wetland desiccation, directly impact the moisture-dependent habitats of these species. Contamination from agricultural runoff, livestock waste, and untreated urban or industrial effluents further degrades water quality, altering edaphic and microclimatic conditions essential for fern survival.
- (3)
- Land-use changes, including deforestation, land clearing for agriculture, and infrastructure development (e.g., roads, firebreaks), fragment habitats and reduce the extent of suitable environments. Overgrazing contributes to trampling, herbivory, and soil nitrification, which in turn promotes colonization by competitive native or invasive species, displacing native ferns.
- (4)
- Recreational pressures, such as unregulated tourism, hiking, and the construction of leisure infrastructure, can result in physical disturbance to sensitive fern populations, especially when located near trails or accessible forested areas.
- (5)
- Population isolation and small population sizes exacerbate genetic erosion and vulnerability to stochastic events, including droughts, landslides, and fires. The scattered and fragmented nature of existing populations limits gene flow and reduces resilience.
- (6)
- Biotic threats, such as invasive plant species, increase competition for light, space, and moisture. Although specific pathogens or pests have not yet been documented in these species, their potential impact remains a concern, particularly under changing environmental conditions.
- (7)
- Climate change emerges as a major overarching threat. Projections under the RCP 4.5 scenario [73] indicate a sustained increase in mean annual maximum temperatures and a decline in annual precipitation (Figure 10), leading to higher evapotranspiration rates and, consequently, reduced water availability (Figure 11). These changes are particularly detrimental during the most sensitive stages of the fern life cycle, spore germination, gametophyte growth, and fertilization, where moisture is essential for success.
4.3. Methods
4.3.1. Structured Abundance Data of Fern Populations
4.3.2. Climate Data Description
4.3.3. Correlation Analysis and Grouping in Operational Territorial Units (OTUs)
4.3.4. Deterministic Population Projection Based on PCA-Derived Predictive Algorithm
4.3.5. Deterministic Population Projection Based on Empirical Annual Change Rates
4.3.6. Comparison and Validation of Projection Methods
- Retrospective model validation: By comparing modeled versus observed data during the last decade (2016–2022), we assessed the degree to which each model could reproduce historical population trajectories. Positive deviations (observed > expected) may suggest additional buffering factors (e.g., microhabitat protection, successful management), while negative deviations (observed < expected) might indicate unmodeled stressors or limited conservation effectiveness.
- Model performance by species: This analysis allowed identification of which species were better captured by the modeling framework.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Population | Tmax | P | DmaxHW | DmaxCHD | ET |
---|---|---|---|---|---|
Culcita macrocarpa | |||||
OGU01 | 0.44 | 0.13 | −0.05 | 0.13 | −0.39 |
OGU02 | −0.08 | 0.30 | −0.69 | 0.40 | −0.62 |
OGU05 | −0.16 | −0.10 | 0.26 | −0.34 | −0.51 |
OGU06 | −0.08 | 0.34 | −0.22 | 0.39 | −0.66 |
OGU08 | −0.32 | 0.26 | 0.45 | −0.03 | −0.67 |
OGU09 | −0.08 | 0.25 | 0.16 | −0.04 | −0.32 |
OGU10 | 0.20 | −0.03 | −0.45 | 0.09 | −0.66 |
OGU11 | 0.21 | 0.09 | −0.31 | 0.33 | −0.05 |
OGU13 | 0.39 | 0.23 | −0.11 | 0.28 | −0.08 |
OGU15 | 0.39 | 0.05 | 0.19 | −0.17 | −0.70 |
Pteris incompleta | |||||
OGU21 | 0.33 | 0.51 | 0.46 | 0.61 | 0.42 |
OGU22 | 0.84 | 0.38 | 0.79 | 0.56 | 0.41 |
OGU23 | 0.14 | 0.86 | 0.98 | 0.68 | 0.22 |
OGU24 | 0.14 | 0.69 | 0.92 | 0.61 | 0.05 |
OGU25 | 0.06 | 0.58 | 0.88 | 0.52 | 0.03 |
OGU27 | 0.94 | 0.85 | 0.20 | 0.35 | 0.39 |
OGU28 | 0.19 | 0.69 | 0.37 | 0.61 | 0.55 |
OGU29 | 0.29 | 0.80 | 0.90 | 0.71 | 0.02 |
OGU30 | 0.87 | 0.86 | 0.19 | 0.35 | 0.46 |
Diplazium caudatum | |||||
OGU31 | −0.27 | 0.09 | −0.35 | 0.34 | −0.42 |
OGU33 | 0.53 | −0.39 | −0.16 | −0.22 | 0.71 |
OGU34 | −0.69 | 0.47 | 0.19 | 0.28 | −0.51 |
OGU35 | 0.42 | −0.02 | −0.46 | 0.24 | 0.33 |
OGU36 | −0.24 | 0.26 | 0.25 | −0.03 | −0.88 |
OGU37 | 0.21 | 0.37 | 0.02 | 0.08 | −0.84 |
OGU40 | −0.22 | 0.05 | 0.09 | 0.03 | −0.72 |
OGU41 | −0.19 | 0.37 | −0.06 | 0.34 | −0.72 |
OGU44 | −0.33 | 0.38 | −0.05 | 0.34 | −0.81 |
OGU45 | −0.05 | 0.49 | −0.14 | 0.39 | −0.07 |
spp. | Operational Territorial Unit | Operational Geographical Unit | Toponym | Variables Explaining Similarities in Climate–Abundance Relationships |
---|---|---|---|---|
Culcita macrocarpa | OTU01 | OGU05 | Garganta de la Vegueta | DmaxHW |
OGU08 | Canuto de la Leña | |||
OGU09 | Canuto 6.7 Carril arriba | |||
OGU15 | Laja del Pinalejo | |||
OTU02 | OGU01 | Linde Comares-Las Corzas | P, Tmax, DmaxCHD | |
OGU11 | Arroyo y Albinas del Viguetón | |||
OGU13 | Garganta del Niño | |||
OTU03 | OGU02 | Canuto 6.4 Arriba Carril | ET, DmaxHW | |
OGU06 | Canuto 6.7 Carril abajo | |||
OGU10 | Juan de Sevilla | |||
Pteris incompleta | OTU04 | OGU23 | Albinas del Pino | ET, Tmax |
OGU24 | Arroyo de Pepe Ayala 3 | |||
OGU25 | Arroyo y Albinas del Pinillo | |||
OGU29 | Chorreras del Alto Mariscal | |||
OTU05 | OGU26 | Arroyo del Pino | P, Tmax, DmaxCHD, DmaxHW | |
OTU06 | OGU27 | Arroyo de Huerto Campano | DmaxHW | |
OGU30 | Garganta del Rayo | |||
OTU07 | OGU21 | Junta afluentes 6.1-6.4 | DmaxCHD | |
OGU22 | Arroyo Pepe Ayala 1 | |||
OGU28 | Albina y Alto Mariscal | |||
Diplazium caudatum | OTU08 | OGU33 | Comares | ET, Tmax |
OGU35 | Pedregoso | |||
OTU09 | OGU31 | Pedregoso | DmaxCHD | |
OGU45 | Ojén | |||
OTU10 | OGU34 | Pedregoso | ET | |
OGU36 | Pedregoso | |||
OGU37 | Ojén | |||
OGU40 | Pedregoso | |||
OGU41 | Pedregoso | |||
OGU44 | Pedregoso |
Species | Principal Component | % Variance Explained | Variable | Loading PC1 | Loading PC2 |
---|---|---|---|---|---|
Culcita macrocarpa | 1 | 52.32% | Tmax | 0.25 | 0.73 |
P | 0.10 | −0.20 | |||
DmaxHW | −0.79 | 0.24 | |||
DmaxCHD | 0.52 | −0.14 | |||
ET | 0.18 | 0.58 | |||
2 | 25.11% | Tmax | 0.25 | 0.73 | |
P | 0.10 | −0.20 | |||
DmaxHW | −0.79 | 0.24 | |||
DmaxCHD | 0.52 | −0.14 | |||
ET | 0.18 | 0.58 | |||
Pteris incompleta | 1 | 54.19% | Tmax | 0.76 | 0.15 |
P | 0.13 | 0.18 | |||
DmaxHW | −0.39 | 0.75 | |||
DmaxCHD | −0.04 | 0.50 | |||
ET | 0.51 | 0.36 | |||
2 | 28.68% | Tmax | 0.76 | 0.15 | |
P | 0.13 | 0.18 | |||
DmaxHW | −0.39 | 0.75 | |||
DmaxCHD | −0.04 | 0.50 | |||
ET | 0.51 | 0.36 | |||
Diplazium caudatum | 1 | 57.41% | Tmax | −0.46 | 0.06 |
P | 0.53 | 0.19 | |||
DmaxHW | 0.37 | −0.66 | |||
DmaxCHD | 0.35 | 0.71 | |||
ET | −0.50 | 0.15 | |||
2 | 25.17% | Tmax | −0.46 | 0.06 | |
P | 0.53 | 0.19 | |||
DmaxHW | 0.37 | −0.66 | |||
DmaxCHD | 0.35 | 0.71 | |||
ET | −0.50 | 0.15 |
2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
---|---|---|---|---|---|---|---|
Culcita macrocarpa | |||||||
Observed | 380 | 359 | 375 | 373 | 377 | 363 | 360 |
Modeled | 370 | 349 | 345 | 370 | 373 | 370 | 352 |
Difference (O–M) | 10 | 10 | 30 | 3 | 4 | −7 | 8 |
Pteris incompleta | |||||||
Observed | 312 | 456 | 535 | 613 | 582 | 547 | 494 |
Modeled | 300 | 302 | 471 | 549 | 612 | 583 | 549 |
Difference (O–M) | 12 | 154 | 64 | 64 | −30 | −36 | −55 |
Diplazium caudatum | |||||||
Observed | 233 | 397 | 405 | 438 | 421 | 379 | 366 |
Modeled | 224 | 221 | 391 | 398 | 440 | 417 | 357 |
Difference (O–M) | 9 | 176 | 14 | 40 | −19 | −38 | 9 |
Species | MAE | RMSE | MBE | MAPE |
---|---|---|---|---|
Culcita macrocarpa | 10.29 | 13.54 | 8.29 | 2.78 |
Pteris incompleta | 59.29 | 73.50 | 24.71 | 11.8 |
Diplazium caudatum | 43.57 | 70.4 | 27.32 | 11.1 |
Species | MAE | RMSE | MBE | MAPE (%) | |
---|---|---|---|---|---|
Culcita macrocarpa | 46.63 ± 26.67 [11.24–116.34] | 56.7 ± 32.69 [15.79–130.46] | 40.14 ± 35.92 [19.25–116.28] | 12.95 ± 8.18 [3.26–32.22] | |
Pteris incompleta | 188.68 ± 12.60 [162.64–211.64] | 212.82 ± 13.64 [184.42–237.58] | 188.63 ± 12.65 [162.45– 211.64] | 47.92 ± 3.31 [41.11–53.97] | |
Diplazium caudatum | 282.25 ± 30.18 [210.52–323.44] | 326.79 ± 32.48 [248.71–371.38] | 281.38 ± 31.08 [207.54–323.44] | 52.84 ± 5.88 [38.95–60.82] |
Family | General Distribution | Ratio nº spp. | Nº crom. | Spores (m, t) | Category of Threat | |
---|---|---|---|---|---|---|
Diplazium caudatum (Cav.) Jermy | Athyriaceae (Polypodiales) | Aljibic Sector and Macaronesian Region | 2/350 | 82 | m | D23: EN. LRFA: CR. LRFE: CR. |
Culcita macrocarpa C.Presl | Culcitaceae (Cyatheales) | Aljiblical Sector, Cantabro-Atlantic Subprovince and Macaronesian Region. | 1/002 | 136 | t | D23: EN. LRFA: CR. LRFE: EN. |
Pteris incompleta Cav. | Pteridaceae (Polypodiales) | Aljibic sector, Tingitana Peninsula and Macaronesian Region. | 3/250 | 58 | t | D23: EN. LRFA: CR LRFE: VU. |
Variable | Definition | Units | Ecological Roles in Ferns |
---|---|---|---|
Tmax | Yearly maximum monthly mean air temperature measured at 2 m above ground | °C | Paleomediterranean ferns require high and stable temperatures that resemble the subtropical climatic conditions that prevailed in their habitats in the past [15,69]. |
P | Annual cumulative precipitation | mm | Essential for completing the life cycle (sporophyte and gametophyte phases); all three species depend on high atmospheric and soil moisture [41]. |
DmaxHW | Maximum duration of heat waves per year (≥5 consecutive days above the 90th percentile) | days | Potentially critical for fern populations; ferns are especially vulnerable to prolonged heat and dryness [69], but microclimatic conditions in canutos may buffer this effect [15]. |
DmaxCHD | Maximum number of consecutive humid days per year (days with daily precipitation >1 mm) | days | Essential for reproductive processes. Consecutive humid periods facilitate spore germination and gametophyte development [15,69,70] |
ET | Potential evapotranspiration, estimated by the Thornthwaite method (k = 0.69) | mm/month | Key indicator of drought stress; high values reflect greater atmospheric demand for water, leading to negative effects on ferns due to their strong dependence on moisture [41]. |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Salvo-Tierra, Á.E.; Pereña-Ortiz, J.F.; Ruiz-Valero, Á. Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin. Plants 2025, 14, 2380. https://doi.org/10.3390/plants14152380
Salvo-Tierra ÁE, Pereña-Ortiz JF, Ruiz-Valero Á. Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin. Plants. 2025; 14(15):2380. https://doi.org/10.3390/plants14152380
Chicago/Turabian StyleSalvo-Tierra, Ángel Enrique, Jaime Francisco Pereña-Ortiz, and Ángel Ruiz-Valero. 2025. "Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin" Plants 14, no. 15: 2380. https://doi.org/10.3390/plants14152380
APA StyleSalvo-Tierra, Á. E., Pereña-Ortiz, J. F., & Ruiz-Valero, Á. (2025). Projecting Extinction Risk and Assessing Conservation Effectiveness for Three Threatened Relict Ferns in the Western Mediterranean Basin. Plants, 14(15), 2380. https://doi.org/10.3390/plants14152380