Introduction
Conservation of threatened species has been a defeating goal of plant management worldwide, especially in the current global change scenario (Wrobleski et al. 2023). At this moment, models predicted that 45% of all known flowering plants are potentially threatened with risk of extinction (Antonelli et al. 2023). In the light of this, literature on conservation biology is now focused on elucidating how to develop proper monitoring studies to assess precise population trends (Lavery et al. 2021; Gauthier et al., 2023). One of the most important point of monitoring is the assessment of demographical structure, that describes the population viability (i.e., the probability of a population persisting through time), depending on the population size at a given time and individual performance in terms of survival, growth and fecundity (Caswell 2001). For a proper population viability analysis, it is crucial to develop intensive field data survey based on individual monitoring collected across years (Morris and Doak 2002). Particularly, demography of plants is commonly assessed through matrix population models, where individuals are divided into discrete classes based on their age, size or morphological stage (Caswell 2001; Morris and Doak 2002) and allows good reproducibility in the long-term timeframe. Since demographic studies of plants have been exponentially increasing in the last decades, an online-open repository compiling all the existing information was developed, namely COMPADRE (Salguero-Gómez et al. 2015). This database allows to compare population models and facilitate large-scale investigation of timely and key ecological and evolutionary questions.
The use of population viability analyses is traditionally applicable for (i) identification of critical demographic stages on which population viability is most jeopardised, (ii) categorization of threatened species attending to their risk of extinction, (iii) assessment of human activities impact on a population, and (iv) evaluation of different management options (Albert and Iriondo 2009). Commonly, methods to monitor individuals across time include identification by steel nails, metal or plastic labels, or writing number directly on the rocks in the case of rupicolous species (Albert and Iriondo 2009). More recently, the use of drone imagery analysis has been incorporated in plant demographic studies (Olsoy et al. 2024) despite their limitations for time consuming efforts (Tay et al. 2018) and its restricted utility for species with distinguishing architecture that occur in habitats with low plant coverage (Rominguer et al., 2021). Also, some studies have used non-physical labels such as the geolocation of individuals (Hernández-Pedrero and Valverde, 2017), but it cannot discriminate plants with a small size and aggregated in clusters due to the low precision of GPS position. Although other global navigation satellite systems (GNSS), such as Galileo in Europe, can be much more precise than GPS (Hofmann-Wellenhoff et al., 2008), the error level is not enough in these cases, and a good alternative method could be the use of a real-time kinematic global positioning systems (RTK-GPS), which provide an error level of centimetres (1–2 cm horizontally and 2–5 cm vertically) that allows individuals to be accurately located (Leica Geosystems 2013; Aykut et al. 2015).
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The RTK-GPS technology has been used for validating the use of drones-acquired images in plant population dynamics (Tay et al. 2018) and for species distribution modelling at a micro-scale (Cursach et al. 2020), as well as for geospatial mapping of crops (Karayel et al. 2012; Pérez-Ruiz et al. 2012). However, as far as we know, no studies have used the RTK-GPS technology for plant monitoring in demography studies.
Due to the high potential utility of this technology to improve traditional methods in demography studies of threatened plants, we took the opportunity to test the feasibility of using RTK-GPS for geospatial mapping and individual monitoring using Euphorbia fontqueriana Greuter as a study case. This species raises as a good opportunity due to (i) new data can be compared to previous traditional assessments with populations matrix included in COMPADRE repository and (ii) it is a good example of risk of extinction threatened species since it is listed as Critically Endangered in the Red List of Spanish Vascular Flora (Moreno 2008).
Hence, the objectives of this study are (i) to assess the real utility of RTK-GPS technology for demographic assessment of plant threatened species, (ii) to compare its applicability with the traditional methods performed previously and (iii) to determine if the use of this technology allows to stablish more optimized decision-making. Taking this aims, we hypothesize that (i) RTK-GPS technology would provide a high-accuracy geographical position of each monitored individual and would allow to find same individuals across time with no necessity of physical labelling, (ii) the robustness of the obtained population matrix will increase since more detailed information of individual transition would be detected, and (iii) the population dynamic trend would be more precise than using traditional methods, which could even differ and offering new considerations.
Methods
Study species
Euphorbia fontqueriana is a rhizomatous perennial plant characterized by its slender and often procumbent branches (5–10 cm in length) with its blooming period during mid-spring and seed dispersal occurs in early summer (Far and Cursach 2023). The species inhabits mountain scrub clearings and dry stony slopes where geological materials accumulate (Llorens et al. 2021). There is a single natural population located from 900 to 1200 m asl (Mallorca Islands, western Mediterranean Basin), which consists of less than 2000 ramets and just a hundred of reproductive ramets (Cursach et al. 2020). Euphorbia fontqueriana is faced with two major threats: (i) biotic threats due its small distribution area, demographic weakness, and predation by goats, and (ii) anthropogenic threats due to the intensity of hiking (Sáez et al. 2017). Due to the conservation status of the species, the environmental authority performed a pilot conservation translocation in 2014 in the Puig Major area, a few kilometres away from the natural location (Sáez et al. 2017).
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Data collection
According to the accurate distribution map of E. fontqueriana generated by Cursach et al. (2020), four permanent plots of 10 × 10 m were located in the highest density areas of the natural population. A metal bar was located in each corner to delimitate the plots, and a differential GPS (Leica RX1200 RTK GPS) was used to geolocate all the ramets inside the plots as a method of plant monitoring. The rhizomatous character of the target species did not allow us to discriminate at the individual level, and we thus work at the ramet level (sensu Cook 1985). Annually, from 2018 to 2022, we surveyed the plots in June collecting the following data: plant stage (seedling, vegetative, reproductive, dormant and dead) and, for reproductive ramets, the number of fructified branches to estimate the number of seeds produced (see below). Seedlings were defined as individuals that emerged from seeds and were known to be less than 1 year old. Vegetative stage included ramets that did not produce flowers during the year in which the sampling was conducted, regardless of whether they had flowered in the past. Reproductive stage included those ramets with flowering or fruiting branches. Finally, the dormant stage included ramets that were not present above ground for 1–3 years but were observed at a later census. To differentiate between dormant and dead stages, data was manually revised along time and those ramets that were absent for more than 3 years were considered dead ramets. However, as for years 2020, 2021 and 2022 no information could be obtained to difference between dead and dormant, the proportion of dormant and dead ramets were estimated from values obtained for 2018 and 2019.
Model structure
The structured demographic model (Fig. 1) was parameterized using data of survival (presence or absence) and reproduction (reproductive ramets that set fruits) for plants found in each plot. From field data of 5 consecutive years, we calculated annual transitions between stages for all ramets found in each plot to construct the demographic transition matrix model of the species. A total of four transitions were analysed from year t to year t + 1, and stage-based transition matrix models were performed between years according to Caswell (2001) and Morris and Doak (2002).
Fig. 1
Life cycle diagram for Euphorbia fontqueriana. Nodes represent stages: (S) Seedling, (V) Vegetative, (R) Reproductive, (D) Dormant. Each possible transition between stages over an annual projection interval is identified as an element conformed by a letter and two subscripts that correspond to the transition matrix shown in Fig. 2
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Due to fruits of E. fontqueriana are dehiscent, we could not record the number of seeds per fruit. Thus, to estimate seed production, seed survival and seed dormancy, the mean percentage of seed germination and seed set data from Far and Cursach (2023) was used and incorporated into the transition matrix model of Stubben and Milligan (2007) structure. From the three estimated parameters, fertility was obtained (f). For the case of 2020 in which the number of fruits could not be recorded due to COVID19 pandemic, we estimated the number of fruits per fruiting branch using the mean number of fruits per fruiting branch obtained in Far and Cursach (2023).
Model analysis
We used field data to estimate the elements of the transition matrix (Fig. 2) for each annual transition. We selected a stochastic model according with Caswell (2001) and Morris and Doak (2002) to modelized our data (Model 1). The model included information on annual variations in vital rates. We determined the stochastic population growth rates (λ) (Morris and Doak 2002), for the comprehensive models from simulations where one of the possible eight annual transition matrices was randomly selected for each projection interval. The λ refers to the expected long-term growth rate of a population under environmental variability where values greater than 1 indicates population increase and values lower than 1 indicates population decrease (Morris and Doak 2002). We calculated λ for each transition from the average growth rate estimated after 50,000 projection intervals for 50 years, and we calculated 95% confidence intervals for these estimates according to Caswell (2001). We also determined the stochastic growth rates, which refears to population size at a specific time, and their 95% confidence intervals for each transition between years and quasi-extinction probability for a 50 years projection.
Fig. 2
Transition matrix obtained from the transition matrix model of life cycle for Euphorbia fontqueriana showed in Fig. 1. Demographic transitions represented by matrix elements (\(\:a\)ij) describe the demographic contributions of a stage (j) to another (i) during an annual transition
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In order to compare how using precise geospatial tools affects the final result of the model, 3 additional models were performed. We performed a model using transition matrix obtained in our study (hereafter, OTM) and we incorporated both our own initial population values (Model 1) and incorporating initial population values of Albert and Iriondo (2009) data (Model 2), and other 2 models using existing transition matrix in COMPADRE database (hereafter, AITM) with our initial population data and with initial population data from Albert and Iriondo (2009) (Model 3 and Model 4, respectively).
Elasticities analysis
In transition matrix models, measures of the proportional sensitivity of λ to small proportional changes in the elements of the transition matrix are known as elasticities. Thus, elasticities provide insight into which life stages or demographic processes have the most significant impact on λ. We undertook analyses to evaluate the proportional sensitivity (elasticity) of the variable λs to ramet transitions denoted as aij. This involved estimating elasticities for stochastic growth rates via numerical simulation, adhering to the methodology described in Caswell (2001). During the simulations, we systematically adjusted the transition probabilities (aij) while iteratively running the model. Before conducting the analysis, we rigorously validated both our data model and through cross-validation with empirical data and sensitivity testing.
Statistical analysis
According to the Shapiro-Wilk test for normality and the Bartlett test for homogeneity of variance, non-parametric Kruskal-Wallis tests with post-hoc Dunn tests − when significant differences were found − were performed to compare differences between years in seedling survival rate, reproductive ramets survival rate, proportion of vegetative ramets that became reproductive (a32), proportion of dormant ramets that became reproductive (a34), proportion of reproductive ramets that remained reproductive (a33), and proportion of dormant ramets that became vegetative (a24). ANOVAs with post-hoc Tukey tests (when significant differences were found) were performed to compare differences between years in the fecundity rate (f) and vegetative ramet survival rate. ANOVAs for GLMs with Poisson distribution and post-hoc Tukey tests were performed to compare differences between years in the number of ramets for each stage. All statistical analyses were performed in R version 4.2.3 (R Core Team 2023).
Results
Plant stage distribution and vital rates
A mean of 96.75 ± 13.38 ramets per plot (n = 4), with a total of 385 ramets representing the 23.69% of the population, was recorded the first year of the study. Vegetative stage was the most representative in all the surveyed years, with a minimum mean value of 52.75 ramets ± 8.09 (n = 4) in 2018 and a maximum mean value of 78 ramets ± 8.03 (n = 4) in 2020, with significant differences among years (p < 0.001) (Fig. 3; Table 1). Number of reproductive ramets ranged from a minimum mean value of 16.50 ramets ± 3.86 (n = 4) in 2021 to 19.75 ramets ± 5.29 (n = 4) in 2019, but no significant differences were found among years (p = 0.813) (Fig. 3). Number of seedlings was very low, with a maximum mean value of 3.50 seedlings ± 1.19 (n = 4) in 2019, and no differences were found among years (p = 0.1158) except for 2022 when no seedlings were observed.
Fig. 3
Number of ramets per stage (seedling, vegetative, reproductive, dormant and dead) represented by the mean and its standard error for each sampling year (2018, 2019, 2020, 2021, 2022). Means and standard error were obtained from number of ramets of each stage obtained from 4 monitoring 10 × 10 m plots
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Number of dead ramets was constant throughout the five years of study, with an estimated maximum of 5 ramets in 2021 and 2022 and a minimum of 0 for 2018, and no differences were found among years (p = 0.082) (Fig. 3). Finally, the number of dormant ramets was constant among time, from a maximum mean of 30.50 ± 0.58 (n = 4) for estimated values of 2022 and a minimum of 12.50 ± 8.50 for 2019 (n = 4). Also, differences in dormant ramets were found among years (p < 0.001) (Fig. 3; Table 1).
Table 1
Statistical analysis for vegetative and dormant stage between years, results from a Tukey test performed over a GLM with Poisson distribution
Year | Vegetative | Dormant | ||
|---|---|---|---|---|
Z | p-value | Z | p-value | |
2019 − 2018 | 4.388 | < 0.001 | -3.471 | 0.004 |
2020 − 2018 | 4.848 | < 0.001 | -0.298 | 0.998 |
2021 − 2018 | 4.388 | < 0.001 | 2.044 | 0.241 |
2022 − 2018 | 3.020 | 0.021 | 2.044 | 0.241 |
2020 − 2019 | 0.476 | 0.989 | 3.192 | 0.012 |
2021 − 2019 | 0 | 1 | 5.312 | < 0.001 |
2022 − 2019 | -1.399 | 0.627 | 5.312 | < 0.001 |
2021 − 2020 | -0.476 | 0.989 | 2.336 | 0.131 |
2022 − 2020 | -1.873 | 0.331 | 2.336 | 0.132 |
2022 − 2021 | -1.399 | 0.627 | 0 | 1 |
Regarding variations in the proportion of stages among years, a considerable portion of reproductive ramets maintained their reproductive (a33) status in the following year, with values ranging from 68.2% ± 10.8 (n = 4) in 2018–2019 to 83.3% ± 5.6 (n = 4) in 2021–2022 (Table 2). Moreover, a small proportion of vegetative ramets in year t became a reproductive ramet (a23) in the year t + 1 (valued ranged from 7.9% ± 2.1 (n = 4) in 2020–2021 to 17.9% ± 1.9 (n = 4) in 2021–2022; Table 2). Regarding dormant ramets that became vegetative (a24) from year t to year t + 1, values ranged from 39.3% ± 8.9 (n = 4) in 2020–2021 to 49.4% ± 8.6 (n = 4) in 2021–2022 (Table 1). Furthermore, a limited number of dormant ramets shifted to the reproductive stage (a34), and values ranged from 13.9% ± 5 (n = 4) in 2018–2019 to 26% ± 24.7 (n = 4) in 2019–2020 (Table 2). Generally, no significant differences were observed in the year-to-year transitions for any of the previously mentioned stage proportions.
Table 2
Proportions of changes in stage of ramets among the fourth annual transitions studied. Proportion of vegetative ramets that become reproductive (a23), dormant ramets that become reproductive (a34), dormant ramets that become vegetative (a24), and reproductive ramets that maintain reproductive (a33)
2018–2019 | 2019–2020 | 2020–2021 | 2021–2022 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
mean | SE | mean | SE | mean | SE | mean | SE | ||||
Proportion of vegetative ramets that become reproductive | 0.164 | 0.034 | 0.142 | 0.011 | 0.079 | 0.021 | 0.179 | 0.019 | |||
Proportion of dormant ramets that become reproductive | 0.139 | 0.05 | 0.26 | 0.247 | 0.168 | 0.082 | 0.208 | 0.097 | |||
Proportion of dormant ramets that become vegetative | 0.488 | 0.088 | 0.456 | 0.057 | 0.393 | 0.089 | 0.494 | 0.086 | |||
Proportion of reproductive ramets that become reproductive | 0.682 | 0.108 | 0.7 | 0.018 | 0.744 | 0.075 | 0.833 | 0.056 | |||
In terms of survival rates, a substantial proportion of both vegetative and reproductive ramets showed a high survival rate (Table 3). The vegetative survival ranged from a mean value of 83.9% ± 3.2 (n = 4) in 2019–2020 to a maximum mean value of 99.7% ± 0.3 (n = 4) in 2018–2019 (Table 3), and the reproductive survival ranged from a minimum mean value of 89% ± 3.5 (n = 4) in 2021 − 2020 to a maximum mean value of 100% ± 0 (n = 4) in 2018–2019. Regarding fertility, the highest fertility mean value was 16.4% ± 3.2 (n = 4) observed in 2021–2022 and the lowest was in 2019–2020, with a mean value of 6% ± 3.5% (Table 3). No differences were found between year-to-year transitions for seedling survival rate, reproductive rate, and fertility. However, significant differences were observed in vegetative survival rate among annual transitions (F = 12.781, p < 0.001).
Table 3
Fertility (f) and survival rate for Seedling, Vegetative and Reproductive stages among the fourth annual transitions studied
2018–2019 | 2019–2020 | 2020–2021 | 2021–2022 | |||||
|---|---|---|---|---|---|---|---|---|
mean | SE | mean | SE | mean | SE | mean | SE | |
Fertility | 0.097 | 0.020 a | 0.06 | 0.035 a | 0.104 | 0.052 a | 0.164 | 0.032 a |
Seedling survival | 0.929 | 0.071 a | 1 | 0.00 a | 0.417 | 0.250 a | 0.275 | 0.160 a |
Vegetative survival | 0.997 | 0.003 a | 0.893 | 0.018 b | 0.858 | 0.013 b | 0.839 | 0.032 b |
Reproductive | 1 | 0.00 a | 0.955 | 0.025 a | 0.918 | 0.030 a | 0.89 | 0.035 a |
Demographic model analyses
As mentioned in Material and Methods section, we generated four models as follows: using the transition matrix model generated with our population data using our initial population data [Model 1] and from Albert and Iriondo (2009) [Model 2]: and then using transition matrix model generated with COMPADRE data with our initial population data [Model 3] and from Albert and Iriondo (2009) [Model 4].
The mean population growth rate (λs) obtained from stochastic projection for the four annual transition evaluated was 1.018 ± 0.003 (n = 4) (Model 1). Specifically, λs was 1.018, 1.009, 1.022 and 1.021 for 2018–2019, 2019–2020, 2020–2021 and 2021–2022, respectively. Thus, taking our transition matrix we observed that the population of E. fontqueriana is growing (Fig. 4, Fig. S1), and forward simulation of the Model 1 showed that the initial 385 ramets would increase to more than 1050 in 50 years (Fig. 4A). In addition, forward simulation using Albert and Iriondo (2009) data (Model 2) showed that the initial 83 ramets would increase to more than 250 ramets (Fig. 4B). On the contrary, Model 3 showed the decrease in population form our initial 385 ramets to less than 100 (Fig. 4C), and the initial value of 83 ramets to less than 20 (Fig. 4D) in the next 50 years for the Model 4.
Fig. 4
Average population size from 5000 forward projections of representative models and its 95% confidence intervals for our transition matrices (OTM) and COMPADRE transition matrices from Albert and Iriondo (2009) (AITM) over 50 years for the population of Euphorbia fontqueriana with initial population of 385 ramets for our data and 83 ramets for Albert and Iriondo (2009) data. A, OTM with initial population of 385 ramets; B, OTM with initial population of 83 ramets; C, AITM with initial population of 385 ramets; D, AITM with initial population of 83 ramets
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The four maximum elasticity values were observed in vegetative ramets that remained vegetative (a22), dormant ramets transitioning to vegetative (a24), vegetative ramets transitioning to dormant (a42) and latent ramets remaining latent (a44), with values 0.405, 0.103, 0.092, 0.084, respectively. Moreover, elasticity for reproduction by seed, with a value of 0.018 was obtained (Fig. 5). Additionally, reproductive ramets that remain reproductive (a33) showed a value of 0.075 and seedling ramets that transition to vegetative 0.014.
Fig. 5
The four largest elements (a22 = vegetative ramets that remain vegetative, a22 = dormant ramets transitioning to vegetative, a22 = vegetative ramets transitioning to dormant, a22 = latent ramets remaining latent) and elasticity for the fertility (f = elasticity for the incorporating reproduction by seed) from the elasticity matrix for model of population of Euphorbia fontqueriana. Model analysis was for stochastic simulations incorporating four 1-year transition matrices. Labels on the x-axis correspond to the elements of the transition matrix for the model (see Fig. 2)
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Discussion
Low seedling recruitment but no declining trend for Euphorbia fontqueriana population
Information about population dynamics is crucial to rapidly detect potential decline trends and motivate conservation actions. Hence, good-quality data becomes essential in conservation biology (Taylor et al., 2021). In our study case, following the permanent plots of E. fontqueriana, we observed that ramets inside all the permanent plots at the beginning of the study represented c. 24% of the whole population (385 out of 1625, see Cursach et al. 2020), which is a considerably higher proportion than the acceptable 10% used in many demographic studies for threatened species (Iriondo 2011). Our data revealed that vegetative stage is the most common plant stage, followed by reproductive and dormant individuals. Survival rate was considerably high in both vegetative and reproductive individuals (more than 85% and 89% in all the annual transitions studied for vegetative and reproductive individuals, respectively). Also, elasticity analyses revealed that plant survival has the largest impact on λ in the population, which has also been reported in other long-lived perennial Mediterranean plant species such as Ranunculus weyleri Marès ex Willk. (Cursach et al. 2013), Helianthemum caput-felis Boiss. (Sulis et al. 2018) and Dianthus morisianus Vals. (Cogoni et al. 2019). A contrasting result regarding previous studies is the occurrence of seedling recruitment in the only natural population of E. fontqueriana, which is extremely low but enough to guarantee the population persistence through time. The mean population growth rate obtained from data along 2018–2022 (data from this study) indicates a positive trend for the threatened species (λ = 1.018) in contrast to the declining trend from data along 2001–2006 (λ = 0.969) reported in Albert and Iriondo (2009). All in all, our results bring hope to the conservation of this threatened species in the long term as the individuals inside the plots are predicted to increase to more than 1050 in 50 years. Conversely, the model based on transition matrixes from Albert and Iriondo (2009), in which seedling recruitment was not detected, the individuals inside the plots would have predicted to decrease to less than 100 in the next 50 years. The difference between both studies is probably related to the initial number of ramets included in the monitoring (385 in this study and 83 in the previous one) which supports the importance of including a representative number of individual from the global population when assessing the dynamics. Here, we proved that including more individuals at the beginning of the experiment is essential, as it can even switch the population trend from negative to positive.
Geolocating plants with high accuracy optimizes population viability studies
Methods regarding plant monitoring have been a challenge for conservation biologists. Commonly, methods to label plants include metal or plastic labels and writing numbers on the rocks in case of rupicolous species (Albert and Iriondo 2009) or, more recently, using drone imagery analysis (Olsoy et al. 2024). However, all these methods are poorly effective for small-sized plants (few centimetres) inhabiting in slopes with loose substrates. This is the case of E. fontqueriana, that occurs in mountain scrub clearings areas where loose materials are easily moved by rainwater or frequentation of both ungulates and hikers (Cursach et al. 2020). Here, we propose the use of RTK-GPS to accurately geolocate the individuals, i.e., with an error lower than 5 cm (Leica Geosystems 2013). Based on our experience, this method is very effective to locate individuals along time, even if they are buried above accumulated materials or they become dormant along a period of time. Also, seedlings with typical small size (fewer than 1 cm) that can barely be noticed in the field can be easily located along annual census following this technique. Another advantage of the use of this method is that label loses along time do not interfere with the population monitoring. Nevertheless, it should be noted that the system depends on satellite coverage, and thus some areas would not be suitable for implementing this methodology. For example, in areas near cliffs or understories of dense forests the device’s resolution may be affected, and thus the use of this methodology would be compromised. Therefore, it is important to have a good understanding of the location of the population being studied when implementing these methodologies.
Overall, the use of this tool as a labelling method optimizes plant monitoring and the population viability studies, and it is particularly interesting for perennial threatened species hard to detect in the field due to its reduced dimensions, temporary dormancy or being buried.
Guide for good practices in demographic studies of threatened plants
This work constitutes a pioneering experience in demographic studies of threatened plant species with a high potential to be implemented in other threatened species. In order to be reproducible in the future, we summarize a procedure for an optimized plant monitoring in population viability studies in the following four steps: (1) developing an integral census of the target population gathering geolocation data to generate a distribution map; (2) defining areas that includes more than 10% of the total population of the target species; and (3) monitoring individuals in the previously selected areas by geolocating systems with high accuracy such as RTK-GPS. This methodology can be also useful for the management of threatened species, since the precise data about population size and population dynamics is directly applicable to the assessment of the conservation status of threatened species (IUCN 2001). Also, data obtained using this methodology is directly applicable to developing mapping and species distribution models to find other suitable zones for further management (i.e., prospections or translocations) as reported for E. fontqueriana in previous studies (Cursach et al. 2020). Finally, this protocol is specially suitable for perennial plants with reduced plant size, that can be buried or with temporary dormancy, and living in open areas (i.e., with optimal satellite coverage).
Final remarks
Accurate and comprehensive plant monitoring is essential in demography studies, as it enables detection of reliable population trends. Our study highlights the importance of using high-precision tools, such as RTK-GPS technology, to improve the accuracy of plant population monitoring. Also, by including a larger and more representative sample size, we observed a positive population trend for E. fontqueriana, contrasting with previous assessments that reported a population decline. This underscores the importance of methodological issues in conservation studies, as improvements in data collection accuracy can significantly alter the predicted outcomes for species viability. However, it is important to consider the specific context and limitations of each study case, as the effectiveness of high-precision tools can be compromised in areas with poor satellite coverage such as areas close to cliffs or understories of dense forests. Overall, our findings suggest that adopting more precise methodologies can strongly improve the assessment of population dynamics, being particularly useful in plant species that are difficult to detect due to their small size, dormancy, or challenging habitats, and thereby contributing to more effective conservation strategies.
Acknowledgements
We thank the Geographical Information Systems and Remote Sensing Service of the University of the Balearic Islands for the technical support in the use of the differential GPS. We also thank J. Rita, F. D. Cortés and P. M. Mir-Rosselló for their help in the fieldwork. This research was partly supported by Fundación Biodiversidad of the Ministerio para la Transición Ecológica (call 2018).
Declarations
Competing interests
The authors declare no competing interests.
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