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Decline of the plastochrone interval in a Zostera marina meadow: 18 years of data

Published online by Cambridge University Press:  30 May 2025

Elena Solana-Arellano Open the ORCID record for Elena Solana-Arellano [Opens in a new window] ,
Olga Flores-Uzeta ,
Carlos Eduardo Cabrera-Ramos  and
J. Adán Avilés-Chávez Open the ORCID record for J. Adán Avilés-Chávez [Opens in a new window]
Elena Solana-Arellano*
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
Olga Flores-Uzeta
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
Carlos Eduardo Cabrera-Ramos
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
J. Adán Avilés-Chávez
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
*
Corresponding author: Elena Solana-Arellano; Email: esolana@cicese.mx
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Abstract

Climate change profoundly affects plant phenology. An important parameter in research on plant dynamics is the plastochrone interval (PI), which is define as the time interval between the formation of successive leaves. The PI has been used to evaluate seagrass demography and as a direct measure of shoot growth and age. Variations in PI determine the growth rates, maintenance, and success of seagrass beds. Global warming could affect the PI dynamics of Zostera marina and, consequently, alter the dynamics of seagrass beds. Using Bayesian linear regression with a time series composed of 316 biweekly sampling dates from 1998 to 2018, we evaluated PI dynamics in the Punta Banda Estuary in Baja California, Mexico. We found that the tendency of the series was linear with parameter values of β0 = 1.65 (SD ±0.19) and β1 = −0.012 (SD ±0. 001). The Bayesian analysis of variance showed strong evidence of differences in the PI among years, given probabilities from 3.2 to 1.88 × 106 times higher of differences than no differences. The largest differences were detected between cold and hot years. The climatology of the time series PI values showed changes in seasonality over time. Summer and autumn were found to be the most perturbed seasons. Finally, by linking the PI estimates with the sea surface temperature anomalies for the complete series, a good inverse correspondence was observed between hot years and high PI, as well as cold years and low PI values, suggesting that climate change has affected PI among years and seasons.

Keywords

Bayesian statisticsclimate changeONIplastochrone intervalsea surface temperatureseagrass

Information

Type
Research Article
Information
Journal of the Marine Biological Association of the United Kingdom , Volume 105 , 2025 , e54
Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of Marine Biological Association of the United Kingdom

Introduction

Estuaries provide many important ecological functions and services. Shallow estuaries with soft sediments provide ecological services that are predominately due to their extensive seagrass meadows. Seagrasses provide habitat, food, and shelter for diverse migratory and resident organisms, some of which have high commercial value. Seagrasses also improve sediment stability and prevent erosion (Orth et al., Reference Orth, Carruthers, Dennison, Duarte, Fourqurean, Heck, Hughes, Kendrick, Kenworthy, Olyarnik, Short, Waycott and Williams2006; Duarte et al., Reference Duarte, Kennedy, Marbà and Hendriks2013; Lavery et al., Reference Lavery, Mateo, Serrano and Rozaimi2013; Bartelli and Unsworth, Reference Bartelli and Unsworth2014; Dahl et al., Reference Dahl, Deyanova, Gütschow, Asplund, Lyimo, Karamfilov, Santos, Björk and Gullström2016; Brodie and N'Yeurt, Reference Brodie and N'Yeurt2018). Globally, seagrass meadows play important roles in nutrient production and cycling (Levin et al., Reference Levin, Boesch, Covich, Dahm, Erséus, Ewel, Kneib, Moldenke, Palmer, Snelgrove, Strayer and Weslawski2001), while their sediments also play an important role in carbon dioxide sequestration (Dahl et al., Reference Dahl, Deyanova, Gütschow, Asplund, Lyimo, Karamfilov, Santos, Björk and Gullström2016; Dahl et al., Reference Dahl, Asplund, Björk, Deyanova, Infantes, Isaeus, Nyström Sandman and Gullström2020; Shulkin et al., Reference Shulkin, Zharikov, Lebedev and Bazarov2024).

Seagrasses are sensitive to environmental conditions. Indeed, changes in environmental conditions are evident in tissue morphology, growth forms, distribution patterns, and growth rates (Fourqurean et al., Reference Fourqurean, Moore, Fry and Hollibaugh1997; Short and Duarte, Reference Short, Duarte, Shorts, Coles and Short2001). From the evidence to date, global warming is affecting the dynamics of seagrass beds. Short and Neckles (Reference Short and Neckles1999) summarized the effects of global climate change on seagrasses and concluded that the rise in global temperatures will alter seagrass growth rates, distributions, patterns of sexual reproduction, and other physiological functions. More recently, multiple studies have confirmed that rising sea surface temperatures (SSTs) affect seagrass ecosystems worldwide (Seddon et al., Reference Seddon, Connolly and Edyvane2000; Levin et al., Reference Levin, Boesch, Covich, Dahm, Erséus, Ewel, Kneib, Moldenke, Palmer, Snelgrove, Strayer and Weslawski2001; Gaeckle and Short, Reference Gaeckle and Short2002; Solana-Arellano et al., Reference Solana-Arellano, Echavarria-Heras and Leal-Ramirez2009; Marbá and Duarte, Reference Marbá and Duarte2010; Jordà et al., Reference Jordà, Marbà and Duarte2012; Brodie and N'Yaurt, Reference Brodie and N'Yeurt2018).

Some of these seagrass studies have examined the effects of rising sea levels due to high SST (Short and Neckles, Reference Short and Neckles1999; Duarte et al., Reference Duarte, Martins, Rosa, Matos, Roleda, Reush, Engleen, Serrao, Pearson, Marques, Cacador, Duarte and Jueterbock2018), while others have documented the loss of seagrass meadows, reporting that some have become functionally extinct (Höffle et al., Reference Höffle, Thomsen and Holmer2011; Jordà et al., Reference Jordà, Marbà and Duarte2012; Brodie and N'Yeurt, Reference Brodie and N'Yeurt2018). The loss of seagrass meadows inherently implies a loss of ecological services and habitats, which greatly affects the ability of seagrasses to sequester carbon. The consequences of these losses are global in scope. Orth et al. (Reference Orth, Carruthers, Dennison, Duarte, Fourqurean, Heck, Hughes, Kendrick, Kenworthy, Olyarnik, Short, Waycott and Williams2006) emphasized the urgency of prioritizing global conservation efforts for seagrass ecosystems, highlighting the crucial need for further research in seagrass meadows. Although the effects of climate change on seagrass meadows and habitats have been widely documented, the interactions between global warming and seagrass ecosystem dynamics require further study to develop sustainable management strategies for these crucial ecosystems.

Seagrasses exhibit clonal growth; meristematic activity results in the growth of rhizome nodules that form rhizome segments with bundles of attached shoots, leaves, and related roots (Tomlinson, Reference Tomlinson1974; Marbá and Duarte, Reference Marbá and Duarte1998; Vieira et al., Reference Vieira, López and Creed2018). In addition, seagrass biomass, density, and growth patterns are species-specific characteristics (Duarte, Reference Duarte1991; Duarte and Chiscano, Reference Duarte and Chiscano1999; Brun et al., Reference Brun, Vergara, Peralta, García-Sánchez, Hernández and Pérez-Lloréns2006; Vieira et al., Reference Vieira, López and Creed2018) that show strong seasonal and site dependence (Brun et al., Reference Brun, Vergara, Peralta, García-Sánchez, Hernández and Pérez-Lloréns2006; Turner, Reference Turner2007; Solana-Arellano et al., Reference Solana-Arellano, Echavarria-Heras and Leal-Ramirez2009; Solana-Arellano et al., Reference Solana-Arellano, Echavarria-Heras and Franco-Vizcaíno2010; Wong et al., Reference Wong, Bravo and Dowd2013; Vieira et al., Reference Vieira, López and Creed2018). Another of these characteristics is the plastochrone interval (PI), which is the time interval (days) between the formation of successive leaves (Jacobs, Reference Jacobs1979).

The PI concept was introduced by Erickson and Michelini (Reference Erickson and Michelini1957) in their study of Xanthium plants. Some years later, Jacobs (Reference Jacobs1979) used PI to evaluate the seagrass Zostera marina (Linnaeus, 1753). Since then, PI has been widely used in seagrass assessments and metabolic or physiological studies of leaf production (Dennison, Reference Dennison, Phillips and McRoy1990); growth, production, and biomass (Marbá et al., Reference Marbá, Cebrian, Enriquez and Duarte1996; Short and Duarte, Reference Short, Duarte, Shorts, Coles and Short2001; Gaeckle and Short, Reference Gaeckle and Short2002; Echavarria-Heras et al., Reference Echavarria-Heras, Solana-Arellano, Leal-Ramírez and Castillo2013); primary production (Erftemeijer and Herman, Reference Erftemeijer and Herman1994); and shoot age (Duarte and Sand-Jensen, Reference Duarte and Sand-Jensen1990; Duarte et al., Reference Duarte, Marbá, Agawin, Cebrián, Enriquez, Fortes, Gallegos, Merino, Olesen, Sand-Jensen, Uri and Vermaat1994; Tupan et al., Reference Tupan, Pentury and Uneputty2016; Fang et al., Reference Fang, Hang, Wang, Jiansheng and Li2018). Therefore, PI is an important and useful index in research on seagrass demography (e.g. Kraemer and Alberte, Reference Kraemer and Alberte1993; Van Tussenbroek, Reference Van Tussenbroek2002).

Every Z. marina leaf that is produced corresponds to a rhizome node that was formed leaving a scar in the rhizome; therefore, clonal growth and leaf formation can be considered equivalent processes in this seagrass (Jacobs, Reference Jacobs1979; Duarte et al., Reference Duarte, Hemminga and Marbá1996). Thus, it is reasonable to assume that variation in PI can be used to understand the architectural traits of Z. marina and the effects of environmental change (Brun et al., Reference Brun, Vergara, Peralta, García-Sánchez, Hernández and Pérez-Lloréns2006; Turner, Reference Turner2007). In particular, it is likely that the effects of climate change will be reflected in the seasonal patterns of PI variability.

This study aimed to (1) evaluate the tendency, seasonality, and interannual variability of the PI time series in a Z. marina meadow in Baja California using Bayesian statistics and (2) associate SST with changes in the PI time series.

Materials and methods

Study site

Data were collected in a Zostera marina meadow in the Punta Banda Estuary, located near Ensenada, Baja California, Mexico (31°44′N, 116°38′W). A Mediterranean climate characterizes the estuary, with warm summers (maximum temperature of 28°C) and cool humid winters (10°C) (Martinez-Inostros, Reference Martínez-Inostros1994). The salinity in the estuary ranges from 33.5 to 37.3‰ during winter (Acosta-Ruiz and Alvarez-Borrego, Reference Acosta-Ruiz and Alvarez-Borrego1974; Martinez-Inostros, Reference Martínez-Inostros1994). Normally, evaporation is greater than precipitation, but extreme winter storms can drive freshwater inputs that result in precipitation exceeding evaporation.

The Punta Banda Estuary has an approximate area of 18.71 km2. During the maximum high tide, ~16.40 km2 of the estuary is covered by water. The main channel (4–8 m depth) of the estuary covers ~1.78 km2. The estuary also contains mudflats that cover 2.85 km2, in which Z. marina is the only seagrass present. Zostera marina grows from the mudflats at a depth of −7 m (mean lower, low water) to the main channel (Flores-Uzeta, personal communication). Muddy banks are also present in the middle and at the head of the estuary. Seagrasses and green macroalgae grow on these banks, forming large mats that are clearly visible at low tide. Seagrasses and green algae have adapted to being periodically and completely covered by water (Pritchard et al., Reference Pritchard, de la Paz-Vela, Cabrera-Muro, Farreraz and Morales1978). The tides in the area are semidiurnal and play a notable role in the dynamics of the estuary. For example, up to 60% of the water in the estuary can ebb in one tidal cycle (Ibarra-Obando and Poumian-Tapia, Reference Ibarra-Obando and Poumian-Tapia1991).

Sampling, data processing, and statistical methods

During low tides, eelgrass was sampled using the Kentula and McIntire (Reference Kentula and McIntire1986) marking technique. The technique consists of marking the eelgrass shoots at the ligule (scar at the top of the sheath) by inserting a 22-gauge hypodermic needle through all the leaves just above the level of the youngest sheath. Marking and recovery depends on the tide. We sampled approximately every 2 weeks (13–17 days), the seagrass samples were collected. The complete data set is composed of the following periods: April 1998 to September 2003, October 2005 to March 2006, July 2008 to December 2013, September 2015 to April 2016, and November 2017 to August 2018. The data represent 316 biweekly sampling dates spanning approximately 158 months.

After marking the shoots, we recorded the number of leaves and placed a flag on the marked shoots (Meling-Lopez and Ibarra-Obando et al., Reference Meling-Lopez and Ibarra-Obando1997; Jimenez-Ramos et al., Reference Jiménez-Ramos, Henares, Egea, Vergara and Brun2023). During each sampling event, previously marked shoots were removed, and a new set of shoots were marked (40 during each sampling event). In laboratory, we recorded the number of new leaves, the leaf distance from the ligule to the needle mark (leaf growth), and number of leaves lost (number of leaves marked – number of leaves with needle marks). We also recorded the number of recovered shoots and the number of leaves. Following the methodology of Jacobs (Reference Jacobs1979), the average PI values associated with a sampling date t were calculated as follows:

(1)$$( {PI} ) _t = \displaystyle{{( {MS_t} ) \nabla t} \over {NLS_t}}, \;$$

where MS t is the number of recovered marked shoots at time t, $\nabla t$ is the time elapsed between sampling dates, and NLS t is the number of new leaves on marked shoots at time t.

A time series is a set of data recorded over time; it has its own components that act at the same time. The components of a time series are expressed by

(2)$$Y_t = C_t + S_t + I_t + e_t, \;$$

where C t is the long-term trend; it is the pattern of the data over the time of analysis and constitutes the basis of an increase or reduction of the time series. S t characterizes data variation due to seasonal influences. I t is a set of cycles that last more than 1 year that can be due to a variety of factors. Finally, e t is the random error and explains the irregular and unpredictable behaviour of the time series (Brockwell and Davis, Reference Brockwell and Davis2002; Cowpertwait and Metcalfe, Reference Cowpertwait and Metcalfe2009; Shumway and Stoffer, Reference Shumway and Stoffer2010). In this study, we did not estimate I t due to a lack of data for some years in the PI t time series. Instead, we focus on C t and S t.

The tendency of the time series in terms of the response variable PI t was calculated fitting an equation of the form: C t = a + bt by the mean square method after removing seasonality by subtracting the PI mean of the log run series from each PI estimation (Cowpertwait and Metcalfe, Reference Cowpertwait and Metcalfe2009). A third degree polynomial was fitted to the PI data to show the decline on PI. Seasonality was assessed using the methodology of Grolemund and Wickham (Reference Grolemund and Wickham2011).

Water-column temperature and salinity were measured in situ with a digital thermometer and a portable refractometer (Leica, Wetzlar, Germany) respectively. To evaluate the degree of correlation between SST anomalies with monthly PI t means, we downloaded data from the Oceanic el Niño Index (ONI) (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php). A one-way Bayesian analysis of variance (ANOVA) was used to identify differences in mean PI among years and seasons. All analyses were conducted in R (R Core Team, 2020).

Results

After fitting a Bayesian linear regression, the parameters of C t = a + bt of the time series were a = 1.65 ± 0.19 (95% credible intervals: [−1.27, 2.63]) and b = −0.01 ± 0.001 (95% credible intervals: [−0.012, −0.008]), and the Bayesian correlation coefficient was −0.495. The annual mean PI (days) values and standard deviation are shown in Table 1; the complete PI time series is shown in Figure 1.

Table 1.

Mean PI (days) and standard deviation per month

Figure 1.

Complete biweekly PI means series measured in Punta Banda Estuary, Ensenada, México, without seasonality along with the Bayesian fitting tendency C t = a + bt with a negative b = −0 01 ± 0 001 with 95% credible intervals [−0. 02, −0 008].

A Bayesian ANOVA showed strong evidence of differences among years with the Bayes factor (BF) ranging from 3.2 < BF < 1.88 × 10−6 (Table 2), indicating that the differences ranged from substantial to decisive (Jeffreys, Reference Jeffreys1935; Kass and Raftery, Reference Kass and Raftery1995). The biggest differences were found between hot and cold years. For example, 1999 exhibited cold SST anomalies, while 2010–2011 exhibited positive SST anomalies.

Table 2.

Bayesian ANOVA results

Second and fourth columns indicate the yearly differences with respect to year in first and third columns, whereas the third and sixth columns are the respective Bayes factors that indicate the probability of differences between years over the probability of equality between years. Individual comparisons are based on the default t-test with a Cauchy priori.

Monthly PI (days) means were calculated and correlated with the SST anomalies based on the monthly ONI. The estimated PI average for the whole series was 6.3 ± 1.9. Figure 2 shows the monthly PI series and SST anomalies. By 2018, the compound decedent tendency of PI resulted in PI declining three units below the overall mean. This could be the reason that most years exhibited a strong probability of being different from 2018 (Figure 2). Also, we found good correspondence between PI and ONI for most years (Figure 2). In particular, hot years that contained intermediate or strong El Niño events, such as 1997–1998, 2002–2003, 2010–2011, and 2015–2016, were associated with a decrease in PI values. Conversely, cold years, such as 1998–2002, 2008–2009, and 2012–2013, were associated with increasing PI values. Of note, a very long La Niña event occurred from 1998 to 2002, which decreased in strength over time.

Figure 2.

Monthly PI average series (continuous line) and the corresponding ONI temperature anomalies (dashed line). Overall, there is a strong correspondence between PI and ONI in which positive anomalies are mostly associated with low PI values and negative anomalies correspond to high PI values.

The PI series contained gaps because some months did not include sampling, the seasonal factor (equation (2)) could not be robustly estimated (Brockwell and Davis, Reference Brockwell and Davis2002). Nevertheless, we evaluated the climatology with the data, estimating the mean PI for the winter (December–February), spring (March–May), summer (June–August), and autumn (September–November) of each year (Figure 3). From the resulting climatology, we evaluated how PI varied during each season among different years (Figure 3), exhibiting a clear effect of seasonality on PI over time, since for all seasons, PI decline over time. Moreover, a Bayesian ANOVA was performed for each season's finding probability of differences (FB10 > 1.5) on PI among years; results are presented in the Supplementary material. Therefore, it is possible that climate change may be responsible for the variability observed in PI among years and seasons.

Figure 3.

Seasonal representation of PI through years: (A) spring, (B) summer, (C) autumn, and (D) winter.

Discussion

Zostera marina is a dominant seagrass in temperate areas. It has been widely documented that temperature influences seagrass dynamics, especially those of Z. marina (Lee et al., Reference Lee, Park and Kim2007; Hosokawa et al., Reference Hosokawa, Nakamura and Kuwae2009; Solana-Arellano et al., Reference Solana-Arellano, Echavarria-Heras and Leal-Ramirez2009; Kaldy, Reference Kaldy2014; Kim et al., Reference Kim, Kim and Lee2015). Seasonality has also been found to affect seagrass dynamics, although this depends on the species and region (Ibarra-Obando et al., Reference Ibarra-Obando, Boudouresque and Roux1997; Azcárate-García et al., Reference Azcárate-García, Beca-Carretero, Cara, Villamayor, Cosnett, Bermejo, Hernández, Brun and Stengel2022; Salazar-Barrios, Reference Salazar-Barrios2015). Thus, we hypothesized that the rate of leaf and shoot formation (PI) varies seasonally. In normal consecutive years, we expected that seasonal PI fluctuations should be similar or that they should fluctuate around a stable average.

Overland et al. (Reference Overland, Rodionov, Minobe and Bond2008) suggested that these stable regimes can change gradually or abruptly due to perturbations or disturbances, such as increasing temperatures as in our study site. Azcárate-García et al. (Reference Azcárate-García, Beca-Carretero, Cara, Villamayor, Cosnett, Bermejo, Hernández, Brun and Stengel2022) evaluated seasonal vegetative development (morphology, population structure, and productivity) of Z. marina and Zostera noltii in western Ireland (cold temperatures) and southern Spain (warm temperatures) during 2017–2018 (Z. marina and Z. noltii) and 2019–2020 (only Z. noltii). The authors found that Z. marina in Ireland exhibited maximal growth during warmer months, while the Spanish population exhibited signs of growth reduction during periods of maximum annual temperatures, concluding that the optimal temperature for Z. marina in Spain was exceeded during summer, this could the case of our declining in PI. Moreover, Azcárate-García et al. (Reference Azcárate-García, Beca-Carretero, Cara, Villamayor, Cosnett, Bermejo, Hernández, Brun and Stengel2022) also demonstrated that leaf formation increased in Z. marina in Spain during autumn–winter 2017 and decreased during summer 2018 similarly as our results. Similarly, Johnson et al. (Reference Johnson, Shields, Kendrick and Orth2021) compared the survival of Z. marina in Chesapeake Bay (USA) after a warming event in 2005 and extraordinary precipitation and warming in 2018, reporting that these conditions led to large Z. marina declines. The authors concluded that (a) adult clone survival was lower in 2019 in regions with greater variation in temperature and salinity. (b) Mean bottom cover in recovering populations was higher in 2019 than 2006 despite lower adult survival due to high seedling abundance. (c) ‘If climate change increases the frequency of extreme events in the Chesapeake Bay and elsewhere, perennial Z. marina populations may be increasingly dependent on the previous years’ flowering and recruitment from seed’.

The mean annual PI showed an apparent decline over time and a clear agreement with SST anomalies (Table 1, Figure 2). It is therefore likely that temperature changes could have affected PI among years and seasons. Further, the mean annual PI ranged between 2.1 and 8.4 days, which is far below the ranges that have been reported in the literature for this species worldwide (13–28.7 days, Jacobs, Reference Jacobs1979; 9–14 days, Wium-Andersen and Borum, Reference Wium-Andersen and Borum1984; 8–18.86 days, Kentula and McIntire, Reference Kentula and McIntire1986; 10–30 days, Gaeckle and Short, Reference Gaeckle and Short2002). Moreover, our PI assessments generated a range of values that were lower than the ranges that have been reported for this species in the study region (6.4–12 days, Ibarra-Obando and Huerta-Tamayo, Reference Ibarra-Obando and Huerta-Tamayo1987; 5.7–11.2 days, Ibarra-Obando et al., Reference Ibarra-Obando, Boudouresque and Roux1997).

Seagrass plasticity, which is the capacity of seagrasses to acclimate to environmental stressors like increasing temperature (Hemminga and Duarte, Reference Hemminga and Duarte2000; McDonald et al., Reference McDonald, Prado, Heck, Forqurean, Frankovich, Dunton and Cebrian2016), may allow some seagrass species to withstand changing environmental conditions through morphological changes (Ruesink et al., Reference Ruesink, Fitzpatrick, Dumbauld, Hacker, Trimble, Wagner and Wisehart2012). In the Punta Banda Estuary, along with the declining PI, we observed an increase in the expansion of the meadow and number of leaves but a substantial decrease in the number of shoots and leaf length (Solana-Arellano and Flores-Uzeta, unpublished data). Moreover, we also observed an increase in opportunistic species, such as Gracilaria vermiculophylla (Salazar-Barrios, Reference Salazar-Barrios2015) and Ruppia maritima (Johnson et al., Reference Johnson, Williams, Lieberman and Solbak2003).

Shifts in PI dynamics may have negative effects on the ecology of seagrass ecosystems because elevated SSTs could induce multifarious responses in eelgrass metabolism (Hosokawa et al., Reference Hosokawa, Nakamura and Kuwae2009). Moreover, the relationship between the time series of PI and SST anomalies (ONI) (Figure 2) indicates that hot years were generally associated with low PI values. Johnson et al. (Reference Johnson, Williams, Lieberman and Solbak2003) directly linked the 1997–1998 El Niño with seagrass colonization in San Diego, California. The authors concluded that high SST, with anomalies ranging from 1.5 to 2.5°C, could be responsible for the observed shift in dominance from Z. marina to R. maritima, which likely caused changes in ecosystem functioning. Moreover, Thom et al. (Reference Thom, Southard and Borde2014) showed that SST and mean sea level were directly related to the ONI and closely related to eelgrass leaf growth and density in three estuaries in Washington state (USA). The authors concluded that seagrasses should be considered valuable indicators of the effects of climate change, specifically temperature variation in marine ecosystems, due to the close relationship between climate and seagrass dynamics. Furthermore, Jordá et al. (Reference Jordà, Marbà and Duarte2012) concluded that seagrass mortality was due to an increase in maximum annual SST and predicted the extinction of Posidonia oceanica in the Mediterranean Sea by 2049 if current warming conditions continue.

Given our findings of declining (or shifting) PI, if SST continues to rise due to global warming, Z. marina in the Punta Banda Estuary is likely to exhibit morphological changes with unknown ecological consequences for this important marine ecosystem. Further research is needed to understand how PI may change due to this stressor and the possible consequences of these changes for the ecosystem.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0025315425000190

Data availability

Data are available on request from the authors.

Acknowledgements

We thank Andrea Lievana-MacTavish for the English revision of the manuscript.

Author contributions

E. S.-A. conducted the sampling over the entire time period, analysed and interpreted data, and wrote the manuscript. O. F.-U. conducted technical work, sampling, and sample processing during the entire period of time. C. E. C.-R., helped with the last 2 years of samplings and help with the data files. J. A. A.-C. contributed with data curation, analyses and the review/edition of the manuscript for the second peer-review.

Financial support

The is study was supported by Mexican Government.

Competing interests

None.

References

Acosta-Ruiz, M and Alvarez-Borrego, S (1974) Distribución Superficial de Algunos Parametros Hidrológicos Físicos y Químicos, en el Estero de Punta Banda, B. C., en Otoño e Invierno. Ciencias Marinas 1, 16–45.10.7773/cm.v1i1.249CrossRefGoogle Scholar
Azcárate-García, T, Beca-Carretero, P, Cara, CL, Villamayor, B, Cosnett, E, Bermejo, R, Hernández, , Brun, FG and Stengel, DB (2022) Seasonal plant development and meadow structure of Irish and southern Spanish seagrass populations. Aquatic Botany 183, 103569.10.1016/j.aquabot.2022.103569CrossRefGoogle Scholar
Bartelli, CM and Unsworth, RKF (2014) Protecting the hand that feed us: Seagrass (Zostera marina) serves as commercial juvenile fish habitat. Marine Pollution Bulletin 83, 425–429.10.1016/j.marpolbul.2013.08.011CrossRefGoogle Scholar
Brockwell, PJ and Davis, RA (2002) Introduction to Time Series and Forecasting, 2nd Edn. London: Brockwell.10.1007/b97391CrossRefGoogle Scholar
Brodie, G and N'Yeurt, AR (2018) Effects of climate change on seagrasses and seagrass habitats relevant to the Pacific Islands.Commonwealth Marine Economies Programme. Pacific marine climate report card: Science Review, 112–131 pp.Google Scholar
Brun, FG, Vergara, JJ, Peralta, G, García-Sánchez, MP, Hernández, I and Pérez-Lloréns, LL (2006) Clonal building, simple growth rules and phylloclimate as key steps to develop functional–structural seagrass models. Marine Ecology Progress Series 323, 133–148.10.3354/meps323133CrossRefGoogle Scholar
Cowpertwait, PS and Metcalfe, AV (2009) Introductory Time Series with R. NY, USA: Springer Science & Business Media.Google Scholar
Dahl, M, Asplund, ME, Björk, M, Deyanova, D, Infantes, E, Isaeus, M, Nyström Sandman, A and Gullström, M (2020). The influence of hydrodynamic exposure on carbon storage and nutrient retention in eelgrass (Zostera marina L.) meadows on the Swedish Skagerrak coast. Scientific Reports 10, 13666.10.1038/s41598-020-70403-5CrossRefGoogle Scholar
Dahl, M, Deyanova, D, Gütschow, S, Asplund, ME, Lyimo, LD, Karamfilov, V, Santos, R, Björk, M and Gullström, M (2016) Sediment characteristics as an important factor for revealing carbon storage in Zostera marina meadows: a comparison of four European areas, Biogeosciences Discuss. Available at https://bg.copernicus.org/preprints/bg-2016-137/ (accessed 10 May 2016).10.5194/bg-2016-137CrossRefGoogle Scholar
Dennison, WC (1990) Leaf production. In Phillips, RC and McRoy, CP (eds), Seagrass Research Methods. Paris, France: UNESCO, pp. 77–79.Google Scholar
Duarte, CM (1991) Allometric scaling of seagrass form and productivity. Marine Ecology Progress Series 77, 289–300.10.3354/meps077289CrossRefGoogle Scholar
Duarte, CM and Chiscano, CL (1999) Seagrass biomass and production: a reassessment. Aquatic Botany 65, 159–174.10.1016/S0304-3770(99)00038-8CrossRefGoogle Scholar
Duarte, CM, Hemminga, MA and Marbá, N (1996) Growth and population dynamics of Thalassodendron ciliatum in a Kenyan back-reef lagoon. Aquatic Botany 55, 1–11.CrossRefGoogle Scholar
Duarte, CM, Kennedy, H, Marbà, N and Hendriks, I (2013) Assessing the capacity of seagrass meadows for carbon burial: current limitations and future strategies. Ocean and Coast Management 83, 32–38.10.1016/j.ocecoaman.2011.09.001CrossRefGoogle Scholar
Duarte, CM, Marbá, N, Agawin, N, Cebrián, J, Enriquez, S, Fortes, MD, Gallegos, ME, Merino, M, Olesen, B, Sand-Jensen, K, Uri, J and Vermaat, J (1994) Reconstruction of seagrass dynamics: age determination and associated tools for the seagrass ecologist. Marine Ecology Progress Series 107, 195–207.CrossRefGoogle Scholar
Duarte, B, Martins, I, Rosa, R, Matos, AR, Roleda, MY, Reush, TBH, Engleen, AH, Serrao, E, Pearson, G, Marques, JC, Cacador, I, Duarte, CM and Jueterbock, A (2018) Climate change impacts on seagrass meadows and macroalgal forests: an integrative perspective on acclimation and adaptation potential. Frontiers in Marine Science 5, 190.10.3389/fmars.2018.00190CrossRefGoogle Scholar
Duarte, CM and Sand-Jensen, K (1990) Seagrass colonization: biomass development and shoot demography in Cymodocea nodosa patches. Marine Ecology Progress Series 67, 97–103.10.3354/meps067097CrossRefGoogle Scholar
Echavarria-Heras, H, Solana-Arellano, E, Leal-Ramírez, C and Castillo, O (2013) Using allometric procedures to substantiate the plastochrone method for eelgrass leaf growth assessments. Theoretical Biology and Medical Modelling 10, 34 (accessed 14 April 2013).10.1186/1742-4682-10-34CrossRefGoogle ScholarPubMed
Erftemeijer, PLA and Herman, PMJ (1994) Seasonal changes in environmental variables, biomass, production and nutrients in two contrasting tropical intertidal seagrass beds in South Sulawesi, Indonesia. Oecologia 99, 45–49.10.1007/BF00317082CrossRefGoogle ScholarPubMed
Erickson, RO and Michelini, FJ (1957) The plastochron index. American Journal of Botany 44, 297–305.10.1002/j.1537-2197.1957.tb10544.xCrossRefGoogle Scholar
Fang, C, Hang, P, Wang, X, Jiansheng, Z and Li, W (2018) Seasonal variation in leaf structure of the eelgrass Zostera marina on the eastern coast of the Shandong Peninsula, China. Aquatic Ecosystem Health and Management 21, 70–81.10.1080/14634988.2018.1429789CrossRefGoogle Scholar
Fourqurean, JW, Moore, TO, Fry, B and Hollibaugh, JT (1997) Spatial and temporal variation in C:N:P ratios, δ15N, and δ13C of eelgrass Zostera marina as indicators of ecosystem processes, Tomales Bay, California, USA. Marine Ecology Progress Series 157, 147–157.10.3354/meps157147CrossRefGoogle Scholar
Gaeckle, JL and Short, FT (2002) A plastochrone method to measuring leaf growth in eelgrass, Zostera marina L. Bulleting of Marine Sciences 71, 1237–1246.Google Scholar
Grolemund, G and Wickham, H (2011) Dates and times made easy with lubridate. Journal of Statistical Software 40, 1–25. Available at https://www.jstatsoft.org/v40/i03/ 10.18637/jss.v040.i03CrossRefGoogle Scholar
Hemminga, MA and Duarte, CM (2000) Seagrass Ecology. UK: Cambridge University Press.10.1017/CBO9780511525551CrossRefGoogle Scholar
Höffle, H, Thomsen, MS and Holmer, M (2011) High mortality of Zostera marina under high temperature regimes but minor effects of invasive macroalgae Gracilaria vermiculophylla. Estuarine Coastal and Shelf Science 92, 35–46.10.1016/j.ecss.2010.12.017CrossRefGoogle Scholar
Hosokawa, S, Nakamura, Y and Kuwae, T (2009) Increasing temperature induces shorter leaf life span in an aquatic plant. Oiko 118, 1158–1163.10.1111/j.1600-0706.2009.17288.xCrossRefGoogle Scholar
Ibarra-Obando, SE, Boudouresque, CF and Roux, M (1997) Leaf dynamics and production of a Zostera marina bed near its southern distributional limit. Aquatic Botany 58, 99–112.10.1016/S0304-3770(97)00020-XCrossRefGoogle Scholar
Ibarra-Obando, SE and Huerta-Tamayo, R (1987) Blade production of Zostera marina L during the summer autumn period on the Pacific Coast of México. Aquatic Botany 28, 301–315.10.1016/0304-3770(87)90007-6CrossRefGoogle Scholar
Ibarra-Obando, SE and Poumian-Tapia, M (1991) The effect of tidal exclusion on salt marsh vegetation in Baja California Mexico. Wetland Ecology and Management 1, 131–148.10.1007/BF00177288CrossRefGoogle Scholar
Jacobs, RPWM (1979) Distribution and aspects of the production and biomass of eelgrass, Zostera marina L at Roscoff, France. Aquatic Botany 7, 151–172.10.1016/0304-3770(79)90019-6CrossRefGoogle Scholar
Jeffreys, H (1935) Some test of significance, treated by the theory of probability. Proceedings of the Cambridge Philosophy Society 31, 203–222.10.1017/S030500410001330XCrossRefGoogle Scholar
Jiménez-Ramos, R, Henares, C, Egea, LG, Vergara, JJ and Brun, FG (2023) Leaf senescence of the seagrass Cymodocea nodosa in Cádiz Bay, Southern Spain. Diversity 15, 187.10.3390/d15020187CrossRefGoogle Scholar
Johnson, AJ, Shields, EC, Kendrick, GA and Orth, RJ (2021) Recovery dynamics of the seagrass Zostera marina following mass mortalities from two extreme climatic events. Estuaries and Coast 44, 535–544.10.1007/s12237-020-00816-yCrossRefGoogle Scholar
Johnson, M, Williams, S, Lieberman, C and Solbak, A (2003) Changes in the abundance of the seagrasses Z. marina and R. maritima in San Diego, California, following an El Nino event. Estuaries 26, 106–115.10.1007/BF02691698CrossRefGoogle Scholar
Jordà, G, Marbà, N and Duarte, CM (2012) Mediterranean seagrass vulnerable to regional climate warming. Nature Climate Change 2, 821–824.10.1038/nclimate1533CrossRefGoogle Scholar
Kaldy, JE (2014) Effect of temperature and nutrient manipulations on eelgrass Zostera marina L. from the Pacific Northwest, USA. Journal of Experimental Marine Biology and Ecology 453, 108–115.10.1016/j.jembe.2013.12.020CrossRefGoogle Scholar
Kass, RE and Raftery, AE (1995) Bayes factors. Journal of the American Statistical Association 90, 773–795.10.1080/01621459.1995.10476572CrossRefGoogle Scholar
Kentula, ME and McIntire, CD (1986) The autecology and production eelgrass (Zostera marina L.) in Netarts Bay, Oregon. Estuaries 9, 188–199.10.2307/1352130CrossRefGoogle Scholar
Kim, YK, Kim, SH and Lee, KS (2015) Seasonal growth responses of the seagrass Zostera marina under severely diminished light conditions. Estuaries and Coasts 38, 558–568.10.1007/s12237-014-9833-2CrossRefGoogle Scholar
Kraemer, GP and Alberte, RS (1993) Age related pattern metabolism and biomass in subterranean tissues of Zostera marina (eelgrass). Marine Ecology Progress Series 95, 193–203.10.3354/meps095193CrossRefGoogle Scholar
Lavery, PS, Mateo, MA, Serrano, O and Rozaimi, M (2013) Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon system service. PLoS ONE 8, e73748 (accessed 5 September 2013).10.1371/journal.pone.0073748CrossRefGoogle Scholar
Lee, KS, Park, SR and Kim, JB (2007) Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: a review. Journal of Experimental Marine Biology and Ecology 350, 144–175.10.1016/j.jembe.2007.06.016CrossRefGoogle Scholar
Levin, LA, Boesch, DF, Covich, A, Dahm, C, Erséus, C, Ewel, KC, Kneib, RT, Moldenke, A, Palmer, MA, Snelgrove, P, Strayer, D and Weslawski, JM (2001) The function of marine critical transition tones and the importance of sediment biodiversity. Ecosystems 4, 430–451.10.1007/s10021-001-0021-4CrossRefGoogle Scholar
Marbá, N, Cebrian, J, Enriquez, S and Duarte, CM (1996) Growth patterns of Western Mediterranean seagrasses: species-specific responses to seasonal forcing. Marine Ecology Progress series 133, 203–215.10.3354/meps133203CrossRefGoogle Scholar
Marbá, N and Duarte, CM (1998) Rhizome elongation and seagrass clonal growth. Marine Ecology Progress series 174, 269–280.10.3354/meps174269CrossRefGoogle Scholar
Marbá, N and Duarte, CM (2010) Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Global Change Biology 16, 2366–2375.10.1111/j.1365-2486.2009.02130.xCrossRefGoogle Scholar
Martínez-Inostros, M (1994) Caracterización de oxígeno disuelto, salinidad temperatura y nutrientes antes y después de una temporada de lluvias (Septiembre de 1992 y Marzo de 1993) en el Estero de Punta Banda (Professional Thesis). Facultad de Ciencias Marinas, Universidad Autónoma de Baja California, Ensenada, México.Google Scholar
McDonald, AM, Prado, P, Heck, KL Jr, Forqurean, JW, Frankovich, TA, Dunton, KH and Cebrian, J (2016) Seagrass growth, reproductive and morphological plasticity across environmental gradients over a large spatial scale. Aquatic Botany 134, 87–96.10.1016/j.aquabot.2016.07.007CrossRefGoogle Scholar
Meling-Lopez, AE and Ibarra-Obando, SE (1997) The use of previous growth as a morphological index to assess blade production in Zostera marina. Aquatic Botany 59, 117–125.10.1016/S0304-3770(97)00025-9CrossRefGoogle Scholar
Orth, RJ, Carruthers, TJB, Dennison, WC, Duarte, CM, Fourqurean, JW, Heck, KL Jr, Hughes, AR, Kendrick, GA, Kenworthy, WJ, Olyarnik, S, Short, FT, Waycott, M and Williams, SL (2006) A global crisis for seagrass ecosystems. BioScience 56, 987–996.CrossRefGoogle Scholar
Overland, J, Rodionov, S, Minobe, S and Bond, N (2008) North Pacific regime shifts: definitions, issues and recent transitions. Progress in Oceanography 77, 92–102.10.1016/j.pocean.2008.03.016CrossRefGoogle Scholar
Pritchard, DW, de la Paz-Vela, R, Cabrera-Muro, HR, Farreraz, S and Morales, E (1978) Hidrología fisca de estero de punta banda, parte I: análisis de datos. Ciencias Marinas 5, 1–23.10.7773/cm.v5i2.325CrossRefGoogle Scholar
R Core Team (2020) R: A language and environment for statistical computing. Vienna, Austria: Foundation for Statistical Computing. Available at https://www.r-project.org/ (accessed 12 October 2024).Google Scholar
Ruesink, JL, Fitzpatrick, JP, Dumbauld, BR, Hacker, SD, Trimble, AC, Wagner, EL and Wisehart, LM (2012) Life history and morphological shifts in an intertidal seagrass following multiple disturbances. Journal of Experimental Marine Biology and Ecology 424–425, 25–31.10.1016/j.jembe.2012.05.002CrossRefGoogle Scholar
Salazar-Barrios, A (2015) Simulación de la tasa de crecimiento foliar de una pradera de Zostera marina L. en el estero de Punta Banda, Ensenada Baja California, México (Master's Thesis). Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, México.Google Scholar
Seddon, S, Connolly, RM and Edyvane, KS (2000) Large scale seagrass dieback in Northern Spencer Gulf, South Australia. Aquatic Botany 66, 297–310.10.1016/S0304-3770(99)00080-7CrossRefGoogle Scholar
Short, FT and Duarte, CM (2001) Methods for the measurements of seagrass abundance and depth distribution. In Shorts, FT, Coles, R and Short, CA (eds), Global Seagrass Research Methods. Amsterdam: Elsevier Science BV, pp. 155–182.CrossRefGoogle Scholar
Short, FT and Neckles, HA (1999) The effect of global climate change on seagrasses. Aquatic Botany 63, 169–196.CrossRefGoogle Scholar
Shulkin, V, Zharikov, V, Lebedev, A and Bazarov, K (2024) Assessment of carbon stock in the Zostera marina Linnaeus, 1753 ecosystem on sandy sediments of the Srednyaya Bight (Peter the Great Bay, the Sea of Japan) based on field observations. Marine Biological Journal 9, 98–114.Google Scholar
Shumway, RH and Stoffer, DS (2010) Time Series Analysis and its Applications with R Examples, 4th Edn. NY, USA: Springer.Google Scholar
Solana-Arellano, E, Echavarria-Heras, H and Franco-Vizcaíno, E (2010) A dynamical model for characterizing seasonality effects on plastochrone intervals. Annals of Applied Biology 157, 99–110.10.1111/j.1744-7348.2010.00414.xCrossRefGoogle Scholar
Solana-Arellano, E, Echavarria-Heras, H and Leal-Ramirez, C (2009) Characterization of environmental forcing on Zostera marina L plastochrone interval dynamics in the Punta Banda Estuary, BC Mexico: an empirical modelling approach. Scientia Marina 73, 95–103.10.3989/scimar.2009.73n1095CrossRefGoogle Scholar
Thom, R, Southard, S and Borde, A (2014) Climate-linked mechanisms driving spatial and temporal variation in eelgrass (Zostera marina L) growth and assemblage structure in Pacific Northwest estuaries, USA. Journal of Coastal Research 68, 1–11.10.2112/SI68-001.1CrossRefGoogle Scholar
Tomlinson, PB (1974) Vegetative morphology and meristem dependence – the foundation of productivity in seagrasses. Aquaculture 4, 107–130.10.1016/0044-8486(74)90027-1CrossRefGoogle Scholar
Tupan, CI, Pentury, R and Uneputty, PA (2016) Population dynamics of seagrass Thalassia hemprichii in Tanjug Tiram Waters, Poka, Ambon Island, Indonesia. Aquaculture, Aquarium Conservation & Legislation Bioflux 9, 1286–1293.Google Scholar
Turner, SJ (2007) Growth and productivity of intertidal Zostera capricorni in New Zealand estuaries New Zealand. Journal of Marine and Freshwater Research 41, 77–90.10.1080/00288330709509897CrossRefGoogle Scholar
Van Tussenbroek, BI (2002) Static life-table analysis and demography of the foliar shoots of the tropical seagrass Thalassia testudinum. Bulleting of Marine Sciences 71, 1247–1256.Google Scholar
Vieira, VMNC, López, IE and Creed, J (2018) The biomass–density relationship and its use as an ecological indicator BMC. Ecology 18, 44.Google Scholar
Wium-Andersen, S and Borum, J (1984) Biomass variation and autotrophic production of an epiphyte–macrophyte community in a coastal Danish area: I Eelgrass (Zostera marina L.) biomass and net production. Ophelia 23, 33–46.10.1080/00785236.1984.10426603CrossRefGoogle Scholar
Wong, MC, Bravo, MA and Dowd, M (2013) Ecological dynamics of Zostera marina (eelgrass) in three adjacent bays in Atlantic Canada. Botanica Marina 56, 413–424.10.1515/bot-2013-0068CrossRefGoogle Scholar
Figure 0

Table 1. Mean PI (days) and standard deviation per month

Figure 1

Figure 1. Complete biweekly PI means series measured in Punta Banda Estuary, Ensenada, México, without seasonality along with the Bayesian fitting tendency Ct = a + bt with a negative b = −0 01 ± 0 001 with 95% credible intervals [−0. 02, −0 008].

Figure 2

Table 2. Bayesian ANOVA results

Figure 3

Figure 2. Monthly PI average series (continuous line) and the corresponding ONI temperature anomalies (dashed line). Overall, there is a strong correspondence between PI and ONI in which positive anomalies are mostly associated with low PI values and negative anomalies correspond to high PI values.

Figure 4

Figure 3. Seasonal representation of PI through years: (A) spring, (B) summer, (C) autumn, and (D) winter.

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