# Remote sensing in seagrass ecology: coupled dynamics between migratory herbivorous birds and intertidal meadows observed by satellite during four decades

Maria Laura Zoffoli, Pierre Gernez, Simon Oiry, Laurent Godet, Sébastien Dalloyau, Bede Ffinian Rowe Davies, Laurent Barillé

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## To cite this version:

Maria Laura Zoffoli, Pierre Gernez, Simon Oiry, Laurent Godet, Sébastien Dalloyau, et al.. Remote sensing in seagrass ecology: coupled dynamics between migratory herbivorous birds and intertidal meadows observed by satellite during four decades. Remote Sensing in Ecology and Conservation, 2023, 9 (3), pp.420-433. 10.1002/rse2.319. hal-03905797

HAL Id: hal-03905797

https://hal.science/hal-03905797

Submitted on 19 Dec 2022

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1 Remote sensing in seagrass ecology: coupled dynamics between migratory herbivorous birds and intertidal meadows observed by satellite during four decades

2 birds and intertidal meadows observed by satellite during four decades

3 Zoffoli, Maria Laura¹*; Gernez, Pierre²; Oiry, Simon²; Godet, Laurent³; Dalloyau, Sébastien⁴;

4 Davies, Bede Ffinian Rowe²; Barillé, Laurent²

5 ¹Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133, Rome, Italy

7 ²Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France

9 ³CNRS, Nantes Université, UMR LETG, B.P. 81223, 44312, Nantes, France

10 ⁴LPO France, Fonderies Royales, 8 rue du Dr Pujos, 17305, Rochefort, France

11

12 *Corresponding author: laura.zoffoli@artov.ismar.cnr.it

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15 Running Title: Brent geese - seagrass beds coupled dynamics

16 Word count

17 Summary: 271 words

18 Main text: 4780 words

19 Acknowledgements: 159 words

20 References: 2024 words

21 Tables and figure legends: 291 words

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23 Number of tables and figures: 0 tables; 7 figures

24
25 Abstract

26 Taking into account trophic relationships in seagrass meadows is crucial to explain and predict
27 seagrass temporal trajectories, as well for implementing and evaluating seagrass conservation
28 policies. However, this type of interaction has been rarely investigated over the long-term and at
29 the scale of a whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass
30 species, *Zostera noltei*, and an herbivorous bird feeding on this seagrass species, the migratory
31 goose *Branta bernicla bernicla*, were investigated using an original combination of long-term
32 Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs)
33 such as seagrass abundance and phenology were measured from 1985 – 2020 using high-
34 resolution satellite remote sensing over Bourgneuf Bay (France), and cross-analysed with *in situ*
35 measurements of bird population size during the goose wintering season. Our results showed a
36 mutual relationship between seagrass and Brent geese over the four last decades, suggesting that
37 the relationship between the two species extends beyond a simple grass – herbivore consumptive
38 effect. We provided evidence of two types of interactions: (i) a bottom-up control where the late-
39 summer seagrass abundance drives the wintering population of herbivorous geese, and (ii) an
40 indirect top-down effect of Brent goose on seagrass habitat, where seagrass development is
41 positively influenced by the bird population during the previous wintering season. Such
42 mutualistic relationship has strong implications for biodiversity conservation because protecting
43 one species is beneficial to the other one, as demonstrated here by the positive trajectories
44 observed from 1985 – 2020 in both seagrass and bird populations. Importantly, we also
45 demonstrated here that exploring the synergy between EO and *in situ* bird data can benefit
46 seagrass ecology and ecosystem management.
47 Key words: Time-series; Earth Observation; Essential Biodiversity Variables; *Zostera noltei*;

48 *Branta bernicla bernicla*, trophic relationships

49

50 **Résumé**

51 Les interactions trophiques sont rarement considérées dans les études à long-terme et macro-
52 échelle des habitats formés par les angiospermes marines. Pourtant, leur prise en compte
53 permettrait de mieux comprendre les trajectoires temporelles des herbiers marins, d'anticiper leur
54 évolution future, et d'améliorer les mesures de gestion et de protection des écosystèmes côtiers.

55 Dans cette étude, nous avons analysé, en combinant de manière originale des séries temporelles à
56 long-terme issues de la télédétection spatiale et du suivi de terrain de l'avifaune marine, les
57 relations mutuelles entre un herbier intertidal de zostère, *Zostera noltei*, et la bernache cravant,
58 *Branta bernicla bernicla*, une oie migratrice herbivore. Les variables essentielles de biodiversité
59 telles que l'abondance et la phénologie des zostères ont été mesurées par des satellites
60 d'observation de la Terre à haute résolution spatiale de 1985 à 2020. Ces observations ont été
61 analysées conjointement avec les comptages pluriannuels de la population de bernaches
62 hivernant en Baie de Bourgneuf (France). Nous avons mis en évidence une relation entre les
63 zostères et les bernaches lors des quatre dernières décennies, suggérant que les interactions entre
64 les deux espèces ne sont pas limitées à une simple relation d'herbivorie. Notre interprétation met
65 en lumière deux types d'interactions : (i) une relation de type "bottom-up" par laquelle la
66 population hivernante de bernaches est régulée par l'abondance estivale de zostère, et (ii) une
67 relation indirecte de type "top-down" par laquelle le développement annuel de l'herbier est
68 influencé par la population de bernaches séjournant dans l'herbier lors de l'hiver précédent. Dans
69 un contexte général de conservation de la biodiversité, une telle relation mutualiste est
particulièrement importante car la protection d'une des deux espèces est bénéfique à l'autre,
comme le démontre l'augmentation à long terme observée de 1985 à 2020 de manière parallèle
de l'abondance de zostères et de bernaches. En outre, notre étude met en lumière l'intérêt
d'analyser en synergie les suivis d'avifaune et les observations issues de la télédétection spatiale
pour l'étude et la gestion des écosystèmes côtiers.

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76

77

78

1. Introduction

Seagrasses are key components of the coastal ocean. As ecosystem engineers (sensu Jones et al., 1994), seagrasses have a structuring role in sedimentation (Bos et al., 2007), tend to stabilize benthic communities and food webs (Jankowska et al., 2019), and are attractive feeding grounds for fishes (Spalding et al., 2003), birds (Robin et al., 2015; Balsby et al., 2017) as well as marine mammals (de Iongh et al., 1995; Sheppard, Lawler and Marsh, 2007) and sea turtles (Taquet et al., 2006). Seagrass beds provide important ecosystem services to humankind including carbon sequestration, oxygen production, and coastal protection (Nordlund et al., 2016).

The ecological relevance and threats to which seagrass meadows are subjected to boosted
ecosystem conservation managers to include seagrass into priority lists for preservation and
observing programs. Many studies have proposed maps of coverage and spatial dynamics of
seagrass beds (e.g., Agostini et al., 2003; Duarte and Sand-Jensen, 1990; Godet et al., 2008;
Robbins, 1997). In particular, seagrass cover and composition are essential oceanic variables
(EOVs) for the monitoring of habitat extent and ecosystem health (Miloslavich et al., 2018), and
seagrass taxonomic diversity, species distribution, population abundance and phenology have
been recognized as essential biodiversity variables (EBVs, Pereira *et al.*, 2013). A better
understanding of seagrass ecosystem functioning including environmental drivers and trophic
interactions is therefore required to mitigate anthropic impacts and implement biodiversity
conservation (Valentine and Duffy, 2006).

Previous works have identified several drivers of seagrass trajectories, such as solar radiation and
tidal exposure (Unsworth *et al.*, 2012), diseases (Rasmussen, 1977; Den Hartog, 1987), as well
as anthropic pressures related to eutrophication (Jones *et al.*, 2018), coastal development (Dunic
*et al.*, 2021), a variety of human activities (Desmonts *et al.*, 2009), water quality (Dunic *et al.*,
2021, Turschwell *et al.*, 2021), and fisheries (Turschwell *et al.*, 2021). While many
environmental drivers have been documented in the literature, less attention has been paid to
investigate the influence of trophic interactions on seagrass dynamics (e.g., Balsby *et al.*, 2017;
Horn *et al.*, 2020; Kollars *et al.*, 2017). Herbivory is a crucial interaction to consider because
grazing influence seagrass biomass via top-down control and, in turn, changes in seagrass
abundance affect herbivore population size via bottom-up control (Unsworth and Butterworth,
2021; Valentine and Duffy, 2006).

Seagrass – herbivores interactions can be investigated at several scales to provide different types
of ecological information (Jacobs *et al.*, 1981). One type of information relates to the direct
effect of grazing on the individual plant, i.e., the immediate reduction of vegetal biomass by
herbivore consumption (Ganter, 2000; Sato *et al.*, 2020). Another type of information concerns
the seagrass – herbivores relationship at habitat/population level over larger time scales. This
interaction is much more complex to investigate because it depends on grazing magnitude and
frequency, on seagrass physiological responses with long-term effects (e.g., compensatory
growth mechanisms, investment in sexual reproduction), and on indirect mechanisms associated
to herbivory (e.g., soil fertilization with faeces, seed propagation, relief of competition by other
vegetal species) (Shaughnessy et al., 2021; Valentine and Duffy, 2006).

Interestingly, most herbivorous birds feeding on seagrass are migratory birds. Herbivory by migratory animals is a particular case of plant - herbivore interaction. On the one hand, consumers exert pressures over the food resource only during a certain period of time (e.g., a few months). On the other hand, the population of migratory herbivores is affected by factors related to a large geographical range that includes wintering, stop-over, and breeding sites (Newton, 2007). Thus, both the timing of seasonal cycles of grazers' population and food resource, as well as regional components that drive herbivore population are fundamental factors to understand their complex relationship in a specific site.

The dark-bellied Brent goose (Branta bernicla bernicla, Fig. 1a, b) is an herbivore waterfowl that migrates from arctic Russia to the French Atlantic coast (Fig. 1c). It breeds during summer in the Taymyr Peninsula (~76.3° N) and migrates south- and westward in order to spend the autumn and winter in Western Europe. Sixty percent of the global Brent goose population winter in France across 18 sites, down to Arcachon Bay (~ 44.6° N) in the Atlantic Coast (Dalloyau and Robin, 2013). Brent goose has several stop-overs in the White, Baltic, and Wadden Seas. Its main food resource is constituted by seagrass of the *Zostera* genus (either *Z. marina* or *Z. noltei*). When seagrass is not available, Brent goose can switch diet to feed on green macroalgae (*Ulva* spp.), saltmarsh vegetation (*Puccinellia maritima*), or agricultural crops (Inger *et al.*, 2006; Mathers and Montgomery, 1997; Tinkler *et al.*, 2009).

Several works already documented the crucial role of megaherbivores (waterfowls, turtles, and dugongs) on seagrass ecosystem structure, biomass, and primary production (Scott *et al.*, 2018).
Also, there are numerous examples of the potential of Earth Observation (EO) to study seagrass EBVs worldwide (Barillé *et al.*, 2010; Calleja *et al.*, 2017; El-Hacen *et al.*, 2020; Zoffoli *et al.*, 2020). However, to our knowledge, combined analyses of long-term dynamics in herbivorous population and remotely-sensed seagrass distribution have never been attempted. This study focused on Bourgneuf Bay (France, Fig. 1c), an important Brent goose wintering site (Valéry and Schricke, 2013) where satellite observations revealed an increasing trend in both seagrass density and areal extent since the 1980s (Zoffoli *et al.*, 2021). Our objective was to investigate, for the first time, the relationship between remotely-sensed decadal changes in seagrass habitat and bird population. More generally, our work seeks at demonstrating the potential of high-resolution satellite remote sensing for the conservation and ecology of two intertwined and protected species.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** *Branta b. bernicla* individuals (a – b) and its migration flyway (c). The main breeding, stopover, and wintering sites are highlighted in yellow (adapted from (Ebbinge *et al.*, 2013). The red dot shows the location of Bourgneuf Bay (France), a wintering site where seagrass spatial distribution has been documented using satellite remote sensing since 1985.</figcaption></figure>

**2. Material and methods**

**2.1 Remote sensing of seagrass essential biodiversity variables**

Along the French Atlantic coast, Bourgneuf Bay is a macrotidal bay hosting a large intertidal seagrass meadow dominated by *Zostera noltei*, which is part of a Natura 2000 protected area. At this latitude (~47° N), seagrass displays a seasonal cycle with a late summer peak (Zoffoli *et al.*, 2020). In a previous work, several seagrass EBVs were computed from long-term high-
resolution remote sensing over this site (Zoffoli et al., 2021). Briefly, a multi-mission satellite time-series was compiled from Landsat, SPOT, and Sentinel-2 acquisitions. Images were selected for every summer from 1985 – 2020. Maps of seagrass percent cover (Fig. 2) were computed from the normalized difference vegetation index (NDVI) with an uncertainty of about 14% (Zoffoli et al., 2020), thus making it possible to measure several seagrass indicators at ecosystem scale. Because seagrass data was missing during 7 years in the initial dataset of Zoffoli et al. (2021), we completed the time-series using additional Landsat and SPOT images in 1989, 1992, 1994, 1999, 2000, 2007, and 2014 in order to get a continuous time-series. The new images were slightly suboptimal in terms of acquisition time (i.e., they were marginally out-of-time compared to the date of seagrass summer peak) or cloud cover (and in that case seagrass quantification has been made by a combination of two images acquired during the same summer). Through these data might be subjected to higher uncertainties, they still remained among the range of variability observed during the 29 other years, and can be consistently used for time-series analysis.

In the present study, we used the following seagrass data: the meadow-averaged seagrass density as an indicator of seagrass above-ground biomass, and two proxies of habitat extent (A20 and A50) corresponding to the surface of the whole and dense seagrass meadow, defined as the area where % cover is higher than 20% or 50% respectively. Seagrass density was computed as the mean % cover over the whole meadow. A 20% threshold in seagrass cover was applied to reduce mapping uncertainties (Dolch et al., 2017; Zoffoli et al., 2021). Interestingly, this value is also biologically meaningful for Brent goose as it is close to the percent cover of 15% indicated as a “giving-up” density below which seagrass leaf cover is not profitable for feeding (Jacobs et al., 1981; Percival and Evans, 1997).
The Landsat time-series was also used to detect possible long-term shift in seagrass phenology over Bourgneuf Bay. A total number of 238 cloud-free images acquired during low tide was selected from 1984 – 2020. Temporal changes in NDVI were used to characterize seagrass seasonal cycle, as in Zoffoli *et al.* (2020). Due to limitations in temporal resolution, it was not possible to measure seagrass seasonal variability every year. Monthly-averaged composite annual cycles were then reconstructed over a 10-year period, and compared between the start (1984 – 1993) and end (2011 – 2020) of the Landsat time-series.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Examples of satellite-derived seagrass map in Bourgneuf Bay, showing seagrass % cover (SPC) at high-resolution over the whole meadow: a) SPOT-2 image on 28 August 1991; b) Sentinel-2 image on 16 September 2019. The complete series of SPC maps from 1985 – 2020 is shown in Zoffoli *et al.* (2021).</figcaption></figure>

## 2.2 Brent goose data

In this work we used two Brent goose datasets in France: one for the goose size population at Bourgneuf Bay, and the other one at national level. For Bourgneuf Bay, we used monthly records of Brent goose size population from September to April (period later referred as “goose season”),
from 1976 – 2021. Bird counts were performed during high tide in 6 different sighting sites within Bourgneuf Bay from the Noirmoutier Island to the Port du Collet. For each goose season, a log-normal model was fitted to the monthly bird numbers (*p*-value < 0.05 for all years). The date of maximal goose number was then computed from the fitted curve. The date of arrival and departure was defined as the days when the goose number corresponded to 50% of the seasonal maximum (Valéry and Schricke, 2013).

The national Brent goose wintering population data was available for the period 1990 – 2020, as part of the French Brent network (Goose Specialist Group / Wetlands International) framework. Monthly counts were based on the Wetlands protocol and were carried out at mid-month from September to April. The data retained correspond to the sum of each site performed in all French wintering sites at the time of the national abundance peak, which may occur mainly in November, less frequently in December.

The world population of Brent goose from 1976 – 2011 was compiled from previously published results. It corresponded to the sum of all mid-January counts performed in France, United Kingdom, The Netherlands, Germany and Denmark (Ebbinge *et al.*, 2002, 2013).

## 2.3. Environmental datasets

The potential influence of environmental parameters on seagrass extent and density was investigated from 1984 – 2020 using monthly time-series of abiotic factors such as air temperature, sea surface temperature, cumulative rainfall, flow of the nearby Loire River, surface solar radiation, wave height, and sea level. The details regarding the environmental data are provided in Supplementary Information.
225 **2.4. Statistical analyses**

Two methods were used to identify the most important factors (environmental and goose abundance) influencing seagrass variables (Density, A20 and A50): the BIOENV method (Clarke and Ainsworth, 1993) and the Conditional Interference Tree (CIT; Hothorn et al., 2006).

The BIOENV method identifies the best subset of environmental variables with the Euclidian distance of scaled environmental variables having the maximum Pearson correlation with the similarity matrix calculated from seagrass parameters. The CIT is a random forest approach used to assess the relative importance of multiple explanatory variables with respect to a single response variable. With the BIOENV method, explanatory variables that are correlated must be removed while the CIT is not affected by multicollinearity. The CIT also allows for non-linear relationships unlike BIOENV which only performs linear relations. BIOENV was run on PRIMER 7 (Clarke and Gorley, 2006) and the CIT analysis with R (Party Kit, Hothorn and Zeileis, 2015). The winter-, spring- and summer-averages of all abiotic parameters were tested using both methods. The tested goose data corresponded to the maximum number recorded during the goose season preceding the seagrass peak (i.e., September – April).

The association between Brent Goose monthly counts and Seagrass Density was assessed using Generalized Linear Mixed Effect Models (GLMM). Brent Goose counts were modelled as a function of Month (October to March), normalised Seagrass Density ($N_i$, Eq. 1), Year as a random factor, and using a temporal autocorrelation structure based on Year. Modelling was performed using the “glmmTMB” package in the R programming language (R Core Team, 2022; Brooks et al., 2022; Chadsuthi et al., 2022) assuming that Brent Goose counts followed a Poisson distribution.

$$N_i = \frac{S_i - \mu_S}{\sigma_S} \qquad (\text{Eq. 1})$$
where Sᵢ corresponds to seagrass Density in each individual year, μS to the mean, and σS to its
standard deviation. Sample vs. fitted residuals, quartile-quartile, and autocorrelation of
temporally sequential samples were visually assessed to evaluate model assumptions. Seagrass
A20 and A50 were likewise modelled.

The BIOENV analysis indicated that the Pearson correlation between seagrass EBVs and the similarity matrices of the investigated factors was maximized at ρ = 0.33 (p-value < 0.05) for Maximum Goose Abundance, suggesting that among all the seagrass drivers tested, it was the main factor contributing to seagrass variability. This was confirmed by the result of the CIT model, which showed that seagrass variability was significantly associated in a first order to Maximum Goose Abundance (p-value < 0.01), and in a second order, to average Wave Height in spring (p-value < 0.01), suggesting that a higher number of wintering geese promoted a larger and denser seagrass at the end of the following summer.

Seagrass and Brent goose counts presented increasing trajectories from 1985 – 2020 (Fig. 3).
Monthly goose counts were significantly related to all seagrass parameters (GLMM, p-value <
0.001). Interestingly, the relationship between Monthly Goose Counts and seagrass Density was
positive from October to January with the strongest relationships in October and November as
evidenced by the steepest slopes in the modelling response (Fig. 4): higher seagrass biomass in
summer attracted a higher number of birds during the subsequent goose wintering season. On the
contrary, the relationship was negative in March when most of the geese already started their
way back towards the Northern breeding sites. GLMM results for seagrass Density were similar
to the ones obtained for the other seagrass parameters analysed such as A20 and A50 (figures not
shown).

275

<figure><img src="image_5.png" /><figcaption>276
277 Figure 3. Time-series of variables from 1985 – 2020: meadow-averaged Seagrass Density (in %) (orange dots) represented in the left y-axis, and maximum Brent Goose counts (black squares) in the right y-axis.
278
279
280</figcaption></figure>

280
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Modelled change in Brent Goose counts with Normalised Seagrass Density across Months. Lines and shaded areas show GLMM model estimates and standard errors.</figcaption></figure>

**3.3. Seasonal variability in seagrass and Brent geese**

The seagrass phenology did not change over the past 40 years. The averaged seasonal NDVI cycle was the same during the first (1984 – 1993) and last decade (2011 – 2020) of the studied time-series (Fig. 4). At this latitude, seagrass biomass increases during spring, peaks at the end of summer (late August / early September), and decreases during autumn and winter. While there was no change in the timing of the seagrass seasonal cycle, there was a difference in its amplitude. NDVI reached a higher peak during the 2011 – 2020 decade compared to the 1984 – 1993 decade, consistently with the afore-mentioned long-term increase in seagrass density and extent. Interestingly, a phenological shift in the Brent goose season occurred from 1984 – 2020.
294 Whereas most of the wintering population generally used to arrive in October and depart before 295 the end of March, during the last decade an increasing number of geese have been spotted as 296 early as September and as late as April (Fig. 4). In average, the date of the maximum goose 297 counts shifted from January to November between the first and last considered decades.

298 The analysis of the whole Brent goose time-series from 1976 – 2020 further confirmed the 299 phenological shift of the goose wintering season. According to the log-normal model, the date of 300 maximum goose counts moved about 19 days earlier, from 12 January in 1976 to 24 December 301 in 2020 (Fig. 5). This trend was accompanied by an extension of the whole wintering season of 302 at least 2 months, with an advance of about 56 days in the date of arrival (from 12 November to 303 17 September), and a delay of about 17 days in the date of departure (from 16 March to 2 April).

304
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Monthly mean NDVI representing seagrass seasonal cycle (orange circles, left axis), and monthly mean Brent goose counts (black bars, right axis in log-scale). Data corresponds to 10-year average during two time periods: (a) 1984 – 1993, and (b) 2011 – 2020. Error bars correspond to standard deviation.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 6.** Timing of modelled goose season in Bourgneuf Bay from 1976 – 2020, showing the dates of departure (white diamonds), maximum (black circles) and arrival (grey squares).</figcaption></figure>
314 **3.4. Long-term variability in Brent goose regional, national and global population**
315 The global Brent goose population showed an increasing trend since 1976, peaking to
316 approximately 329,000 individuals in 1992, followed by a reduction and stabilization around
317 220,000 individuals since the 2000s (Fig. 6; Ebbinge et al., 2013). The number of Brent geese
318 wintering in France and in Bourgneuf Bay also increased up to the 1990s, but contrary to the
319 world population, continued to increase afterwards. The French population thus regularly
320 increased up to the early 2010s, stabilizing around 142,000 individuals over the past decade. The
321 French population thus now represents about 60% of the global goose population. The
322 population in Bourgneuf Bay also displayed an overall increasing trend, reaching a total number
323 of geese fluctuating around 11,500 individuals over the last years. Compared to the national
324 population, the population in Bourgneuf Bay displayed a high degree or interannual variability,
325 with drastic decreases in 1999, 2001, 2008, 2009 and 2014. Interestingly, the years of strong
326 declines in Brent goose abundance coincided with significant reductions in seagrass surface area
327 and meadow-averaged density, suggesting that a reduction of food supply prompted the
328 migratory birds to overwinter in other sites in France.

329
330
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Brent goose population in Bourgneuf Bay from 1976 – 2020 (black circles), in France from 1990 – 2020 (grey diamonds), and in the world from 1976 – 2011 (white squares). For comparative purpose, the French population was divided by 10, and the world population was divided by 30. The vertical axis is graduated in log-scale.</figcaption></figure>

# 4. Discussion

## 4.1. Data requirements and challenges to interpret seagrass trajectory

While catastrophic seagrass loss has been globally documented (Waycott *et al.*, 2009; Green *et al.*, 2021) a recent increase in the status of intertidal meadows dominated by *Z. soltei* was reported in several European sites (de los Santos *et al.*, 2019) including Bourgneuf Bay (Zoffoli *et al.*, 2021). The knowledge of seagrass status and its ecological drivers is required to develop predictive models and optimize conservation strategies of these protected habitats, but the determination of such drivers is extremely challenging due to the lack of both seagrass and environmental measurements (Unsworth *et al.*, 2019). In the present study, a long-term seagrass time-series has been analysed using satellite observations acquired during its annual maximum to avoid biases caused by seasonal variations. The status of the seagrass meadow at this time corresponds to an ecological response to environmental drivers that acted over the previous
350 months. Thus, the effect of environmental factors should be analysed taking into account the
351 conditions experienced by seagrass during the whole growing season. For that purpose,
352 environmental observations have to be performed at high temporal resolution. Here, the
353 investigated environmental datasets were either acquired using EO, autonomous systems such as
354 meteorological stations and tide gauges, or resulting from modelling. This highlights the
355 importance of maintaining coordinated continuous monitoring programs (e.g., satellite missions,
356 *in situ* monitoring networks, autonomous platforms; Papathanasopoulou et al., 2019) collecting
357 data on both EBV and abiotic factors at large spatial and temporal scales to develop an improved
358 understanding of biodiversity and ecosystem functioning (Navarro et al., 2017; Paganini et al.,
359 2016). Here, monthly data of solar irradiance, air temperature and SST, rainfalls, river flow,
360 wave height and sea level were available. These parameters, however, were not the main
361 explanatory variables for the seagrass dynamics observed in Bourgneuf Bay from 1985 – 2020.
362 Similarly, Lebrasse et al. (2022) did not find any significant relationship between seagrass long-
363 term trends and climatic factors in a subtidal meadow in Florida. Other environmental factors
364 such as sedimentation rate, nutrient concentration, and sediment composition have been pointed
365 out as important seagrass drivers (Guerrero-Meseguer et al., 2021; Qin et al., 2021; Soissons et
366 al., 2018); they were not considered here because of the absence of available long datasets at the
367 required temporal resolution.

368 While the investigated abiotic factors were not correlated with seagrass variability in Bourgneuf
369 Bay, some synchronous events are noteworthy. This is the case of a drastic seagrass decline in
370 2003, which coincided with an intense summer heatwave in Europe (Garcia-Herrera et al.,
371 2010). The low seagrass status observed in 2010 occurred several months after the so-called
372 Xynthia storm, which resulted in dramatic coastal erosion and flooding in Bourgneuf Bay and
373 nearby towns (Chadenas *et al.*, 2014). The limited seagrass extent during the 1980s and early 374 1990s also coincided with a higher occurrence of frost days during the preceding winter whose 375 influence on intertidal seagrass has been previously documented in northern Brittany (Fournier *et 376 al.*, 2006). Due to the absence of correlation between abiotic factors and seagrass trajectory, 377 examples of synchronous decline in seagrass and environmental parameters are however to be 378 interpreted with caution, all the more as it is generally easier to highlight coincident abrupt losses 379 rather than to identify other types of changes. Nevertheless, the projected frequency increase of 380 extreme climatic events such as heatwaves, cold waves and droughts in the forthcoming decades 381 (IPCC, 2021) calls for a continuous monitoring of EBVs and environmental drivers. Unlike the 382 investigated abiotic data, Brent goose abundance was significantly related to seagrass EBVs. 383 Long-term datasets of waterbirds represent valuable indicators of conservation status of coastal 384 habitats (Ogden *et al.*, 2014; Brandis *et al.*, 2018). In previous studies, the lack of seagrass time- 385 series was highlighted as a limitation to interpret the long-term dynamics in Brent goose 386 population (Valéry and Schricke, 2013), as well as to document plant – herbivore relationships 387 over large temporal scales (Balsby *et al.*, 2017). In this regard, our work demonstrates that EO 388 makes it possible to further investigate reciprocal interactions between *Z. noltei* and *B. b.* 389 *bernicla* at the scale of a whole seagrass meadow over several decades. 390

## 4.2. Ecological interactions between Brent goose and seagrass

391 One of the most striking results of the present study was that long-term changes in the size of the 392 Brent goose population was related with seagrass EBVs. On the first hand, the positive 393 association between both the seagrass meadow extent and density and goose counts during the 394 subsequent wintering season has a straightforward bottom-up interpretation: a higher seagrass 395
396 biomass (i.e., higher food supply) attracts a higher number of consumers (Ganter, 2000).

397 Furthermore, the results from the GLMM demonstrated that the relationship between goose
398 population and seagrass was the strongest shortly after the time of the seagrass maximum, also
399 corresponding to the month of maximal goose counts (October and/or November). On the other
400 hand, grazing pressure could be expected to negatively impact seagrass population (Sato et al.,
401 2020). As a matter of fact, the influence of grazing by birds on seagrass can be divided into
402 short- and long-term effects (Jacobs et al., 1981). In Bourgneuf Bay, as well as in other
403 temperate wintering sites, the absence of a long-lasting grazing impact could be related to
404 differences in timing between seagrass growth and consumption by migratory birds. Brent geese
405 arrive at the end of the seagrass growing season, and depart before the start of the seagrass
406 reproduction, thus causing little impact on seagrass development (Ganter, 2000). Indeed, Brent
407 geese consume *Zostera* during seagrass senescent phase, and at a time of the year with a higher
408 frequency of storms. It means that seagrass biomass, if not consumed by geese, would be
409 eventually reduced by the decomposition of tissues by microorganisms as well as by
410 hydrodynamical processes (Jacobs et al., 1981).

411 Our results of BIOENV and CIT evidenced a positive, long-lasting effect of Brent geese on
412 seagrass ecology, suggesting that the two species extends beyond a simple plant – herbivore
413 consumptive effect (Heck and Valentine, 2006; Kollars et al., 2017; Valentine and Heck, 1999).

414 Shaughnessy et al. (2021) experimentally demonstrated that Brent goose grazing can stimulate
415 seagrass flowering and compensatory growth mechanisms via leaf clipping and fertilization by
416 faecal pellets addition. Previous field experiments in the Wadden Sea showed that Brent geese
417 could promote *Z. noltei* growth by seed propagation and sediment reworking, thus avoiding plant
418 burial by sedimentation (Nacken and Reise, 2000). However, this observational evidence
419 remains to be experimentally evaluated to address the question of whether or not seagrass
420 indicators would continue to show increasing trends in the absence of Brent geese.

421 Interestingly, the increase in Brent goose population along the 36-years studied was also
422 associated with an extension of their wintering season by about 2 months since the 1980s. While
423 the reasons behind a longer wintering season in Bourgneuf Bay remain unknown, the increase in
424 food supply likely played a role. The analysis of decadal-averaged seagrass cycles using the
425 Landsat archive did not reveal long-term changes in seagrass phenology (comparing the 1984 –
426 1993 vs. 2011 – 2020 cycles), but Landsat limited temporal resolution as well as the 10-year
427 compositing process might have masked variations shorter than one month. The higher temporal
428 resolution of the S2 time-series is expected to improve further analyses of seagrass dynamics,
429 thus bringing new insights on Brent goose ecology to assess whether their later departure (in case
430 it continues to happen) impacts seagrass distribution in Bourgneuf Bay. At larger spatial scales, a
431 cross-analysis of seagrass dynamics and Brent goose migration patterns could also be performed
432 over several stopover sites all along the north-western European coastline to get a broader picture
433 of the Brent goose migration flyway. While the “green wave hypothesis” (i.e., the spring
434 migration towards breeding sites is triggered by plant growth) has been recently investigated for
435 *Branta leucopsis* using MODIS time-series of terrestrial vegetation dynamics at continental scale
436 (Shariatinajafabadi *et al.*, 2014), to our knowledge it remains an open question in the case of
437 Brent goose *B. b. bernicla*.

438

439 **4.3. Conservation and management perspectives**

440 Beyond its regional implications in ecology and biodiversity, the present study aims at
demonstrating the benefits of a synergetic use of two atypical datasets: time-series of satellite
441
derived seagrass EBVs and bird census data. By providing continuous, long-term, and spatially resolved measurements of seagrass dynamics (Paganini et al., 2016), EO made it possible to get a consistent picture of biodiversity changes in the (seasonal) habitat of a migratory waterfowl over four decades. As both B. b. bernicla and Zostera noltei are protected species, such a mutualistic plant - herbivore relation has implications for conservation because protection of the seagrass habitat is beneficial to Brent goose. Then, documenting the ecological status of an ecosystem/meadow that can guide conservation actions expected to protect seagrass meadows, herbivorous waterfowl, as well as a variety of marine avifauna (Stillman et al., 2015; Unsworth and Butterworth, 2021). On the other hand, knowledge about the effect of the seagrass food webs is fundamental for an efficient management of these habitats (Valentine and Duffy, 2006).

As migratory bird, Brent geese are subjected to different biological controls along the whole flyway, thus interconnecting very distant coastal ecosystems (Newton, 2007). Habitat degradation or destruction in any point of the flyway can have far reaching consequences in the Brent goose global population (Newton, 2007). As those long routes usually overpass national frontiers, the protection of biodiversity calls for coordinated conservation partnerships at continental and global scales (Navarro et al., 2017).

As mentioned earlier, the mutually positive effect of the relationship between seagrass beds and geese is probably due to the fact that geese start their wintering during the senescence of the seagrass bed. However, since the wintering period of the geese is getting longer, we can assume a future potential phenological mismatch of this mutually positive effect. Such a temporal mismatch between a predator and its trophic resource, often attributed to climate change, has already been documented in different bird species, such as blue tits, great tits and pied flycatchers (Burgess et al., 2018).
Finally, the conservation of this “seagrass-goose tandem” seems necessary in terms of
conservation management in the coastal area. Like other herbivorous birds, geese can indeed
move to other habitats when their trophic resource becomes scarce. Leaving their original
habitats (*sensu* Martínez-Abraín and Jiménez, 2016), the substitute habitats (*sensu* Martínez-
Abraín and Jiménez, 2016) will be probably the peripheral agricultural fields, which may lead to
conflicts with farmers. The conservation of seagrass beds is therefore necessary so that geese are
not considered in the future as simple pest species creating agricultural damages.

## 5. Conclusions

Using a 36-year time-series of seagrass EBVs (namely seagrass extent and meadow-averaged
density) derived from high-resolution satellite remote sensing, we analysed the drivers of
biodiversity changes in an intertidal Natura 2000 and RAMSAR protected area along the French
Atlantic coast. While none of the investigated abiotic factors (solar irradiance, air temperature
and SST, rainfall, river flow, wave height, and sea level) was explanatory of the seagrass
dynamics, we evidenced a relationship between *Zostera noltei* and *B. b. bernicla*, a migratory
herbivorous bird. Our results suggest that a complex and mutualistic plant-herbivore interaction,
extending beyond consumption grazing, is key in the increasing trend observed in both species
over the last decades. In complement to the increase in bird abundance observed since the 1980s,
we observed a phenological change in the goose wintering season. Investigating the reasons of
such phenological change would require to perform a global analysis of Brent goose ecology
along its entire flyway, from the Taymyr Peninsula to Arcachon Bay.

As a proof-of-concept, our work demonstrates that the original combination of EO with bird
census data is of interest to develop an improved understanding of seagrass ecology and
biodiversity, as well as to assess the efficiency of any protective measures. As migratory species
use resources overpassing administrative and political boundaries, it is noteworthy to remind
here that EO makes it possible to provide seamless measures of seagrass status worldwide.

We thank the *Ligue de Protection des Oiseaux* for providing the Brent goose data used in this work, and more particularly the French Brent network volunteers who are mobilized each month to carry out the counts on the entire Channel/Atlantic coastline. SST data were provided by GHRSSST, Met Office and CMEMS. Meteorological data were acquired from INFOCLIMAT. SSRD and height wave data were provided by the Copernicus Climate Change Service within the Climate Data Store (CDS). River flow data were provided by the Eulerian Network, *Institut Français de Recherche pour l'Exploitation de la Mer* (IFREMER) and sea level data by the *Service hydrographique et océanographique de la Marine* (SHOM). This research was supported by the BiCOME project (Biodiversity of the Coastal Ocean: Monitoring with Earth Observation) funded by the European Space Agency under "Earth Observation Science for Society" element of FutureEO-1 BIODIVERSITY+ PRECURSORS call, contract No. 4000135756/21/I-EF. Thanks to anonymous reviewers for their critic comments to improve the manuscript.

MLZ, PG, LG and LB conceived the ideas and designed methodology; MLZ, PG, SO, SD and LB collected the data; MLZ, PG, SO, BFRD and LB analysed the data; MLZ and PG led the writing of the manuscript; LG, SO, SD, BFRD and LB made edits to the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
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717
718 Supplementary material

719 Environmental datasets

720 Monthly average of air temperature (AT, in °C) and Cumulative precipitations (PP, in mm)
721 corresponded to *in situ* measurements acquired at the meteorological station in Saint Sauveur, Ile d'Yeu (France) (46.69°N – 2.33°W). They corresponded to monthly measurements spanning the period 1/Jan/1984 – 31/Mar/2021. Meteorological data were downloaded from www.infoclimat.fr.

725 Sea Surface Temperature (SST, in °C): The “European North West Shelf/Iberia Biscay Irish Seas” product produced by the Copernicus Marine Service (CMEMS, https://marine.copernicus.eu/) was used. The product is based on satellite data from infrared and microwave radiometers. Its spatial resolution was 0.05° for 1/Jan/1984 – 31/Dec/2019 and 0.02° for 1/Jan/2020 – 31/Mars/2021. The acquired SST product consisted on daily values and was aggregated as a monthly average for the area comprising the coordinates: 46.8 °N – 47.1°N and 2°W – 2.5°W.

732 Loire river flow (in m³ sec⁻¹): It corresponded to the daily average of flux of the Loire River measured *in situ* at the station located in Monjean-sur-Loire (France) (47.39°N – 0.86°W). This station is located at approximately 120 km from the Loire River mouth and is the latest fluvial station, i.e., it is not affected by tides and, then, it provides information about freshwater inputs to the bay. Data were downloaded from the *Institut Français de Recherche pour l'Exploitation de la Mer* (FREMER) portal (http://www.ifremer.fr/co-en/eulerianNetwork?contextId=386&lang=en) from January/1984 – March/2021. From daily river flow measurements, monthly averages were computed.
740 **Surface solar radiation downwards (SSRD, in J m⁻²) and Significant height of combined**
741 **wind waves and swell (Height waves, in m):** They were produced as a part of the reanalysis
742 dataset of the fifth generation of the European Centre for Medium-Range Weather Forecasts
743 (ERA5), provided by the Copernicus Climate Change Service within the Climate Data Store
744 (CDS) (https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset). SSRD corresponds to
745 the amount of total solar radiation (including direct and diffuse components) that reaches the
746 horizontal Earth surface. High waves represented the surface ocean and sea waves produced by
747 wind and swell. Hourly data from Jan/1984 – Mar/2021 were downloaded in a spatial resolution
748 of 0.25° x 0.25° for SSRD and 0.5° x 0.5° for height waves, and aggregated as the monthly
749 average of the area comprising the coordinates: 46.5°N – 47.5°N and 2°W – 3°W.

750 **Sea level (in m):** Hourly data were acquired from the *Service hydrographique et*
751 *océanographique de la Marine* (SHOM). They corresponded to measurements at the *in situ*
752 station closest to the seagrass meadow “L’Herbaudière” (47.025°N – 2.299°W). Missed data
753 were filled out either with measurements at the station “Pointe Saint Gildas” (47.140°N –
754 2.246°W) or modelled data. Hourly sea level data were monthly averaged.