# Longevity Collapse in Dolphins: A Growing Conservation Concern in the Bay of Biscay

Etienne Rouby, Floriane Plard, Vincent Ridoux, Audrey Mauchamp, Willy

Dabin, Jérôme Spitz, Matthieu Authier

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Etienne Rouby, Floriane Plard, Vincent Ridoux, Audrey Mauchamp, Willy Dabin, et al. Longevity Collapse in Dolphins: A Growing Conservation Concern in the Bay of Biscay. Conservation Letters, 2025, 18 (5), 10.1111/conl.13142. hal-05310366

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LETTER

OPEN ACCESS

# Longevity Collapse in Dolphins: A Growing Conservation Concern in the Bay of Biscay

Etienne Rouby¹,²,³ ID
Matthieu Authier³ ID

Floriane Plard³,⁴

Vincent Ridoux³,⁵

Audrey Mauchamp³

Willy Dabin³

Jérôme Spitz³,⁵

¹Institute of Arctic and Alpine Research, Colorado University Boulder, Boulder, Colorado, USA | ²Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA | ³Observatoire Pelagis, La Rochelle Université, La Rochelle, France | ⁴Independent Freelance Researcher | ⁵Centre d'Etudes Biologiques de Chizé, CNRS-La Rochelle Université UMR-7372, Villiers en Bois, France

**Correspondence:** Etienne Rouby (etienne.rouby@colorado.edu)

**Received:** 12 March 2025 |

**Revised:** 18 July 2025 |

**Accepted:** 12 August 2025

**Funding:** The study was supported by the French "Ministère de la Transition Ecologique et Solidaire (MTES)" and the "Direction de l'Eau et de la Biodiversité" (DEB).

**Keywords:** age-at-death | demography | fisheries bycatch | marine mammals | population viability | stranding data | temporal trends | vital rates

## Abstract

Marine megafauna populations face global decline from human impacts, making early detection of demographic tipping points essential for effective conservation. Traditional viability analyses rely on Capture-Mark-Recapture data, logistically impractical for highly mobile pelagic cetaceans. Stranding data provide an alternative to conventional monitoring. This study presents the first evidence of declining viability in the most abundant cetacean of the Northeast Atlantic Ocean, the common dolphin (Delphinus delphis) in the Bay of Biscay. Using a novel cross-sectional framework with stratified random sampling, we analyzed age-at-death data from 759 specimens collected between 1997 and 2019. Female longevity declined dramatically from 24 to 17 years, corresponding to a 2.4% reduction in population growth rate. This demographic decline highlights the Bay of Biscay as a demographic sink despite stable abundance estimates. Our findings demonstrate that stranding data can provide acute demographic signals for wide-ranging cetacean species, offering critical early warning indicators for proactive conservation management.

## 1 | Introduction

In the Anthropocene, formerly abundant species can rapidly decline toward rarity, as evidenced across numerous cetacean populations (i.e., Jaramillo-Legorreta et al. 2007). The urgent need for early warning indicators of negative demographic shifts has never been more critical (de Silva and Leimgruber 2019). While population abundance trends represent cornerstone metrics in conservation frameworks, they have proven limited for detecting rapid declines in cetaceans due to inherent statistical limitations (Jewell et al. 2012), which often postpones timely management actions (Jaramillo-Legorreta et al. 2007).

Conservation science must, therefore, evolve beyond traditional monitoring approaches to develop more sensitive demographic indicators. Focusing on temporal changes in vital rates offers superior predictive power for population trajectories, potentially enabling conservation measures before critical thresholds are crossed (de Silva and Leimgruber 2019).

Robust demographic data (e.g., Capture-Mark-Recapture framework) are essential for constructing population matrix models with accurately estimated vital rates and assessing long-term population viability (Caswell 2001; Conde et al. 2019). Population Viability Analyses can then be performed to provide

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). Conservation Letters published by Wiley Periodicals LLC.
extinction risk predictions (Coulson et al. 2001; Pe'er et al. 2013; Hashimoto et al. 2015). At the demographic level, these analyses can reveal temporal variations in vital rates (Fujiwara and Caswell 2001; Kraus et al. 2005). Unfortunately, for many cetacean species, such longitudinal monitoring proves logistically unfeasible due to insufficient natural markings and extremely low detection probabilities (Hupman et al. 2018). Cross-sectional sampling from stranded individuals offers a valuable alternative, providing time-to-event data (age and reproductive status at death; e.g., Kesselring et al. 2017; Betty et al. 2023) from which vital rates (e.g., survivorship and fertility) can be estimated.

However, population projections using cross-sectional data typically require restrictive assumptions, particularly stationarity in vital rates (Coulson et al. 2001). Moreover, selectivity bias induced by carcass collection could bias survival estimates (Caughley 1966). Samples collected from several years (years of death of individuals, hereafter called “period”) must often be aggregated to achieve statistical robustness (Betty et al. 2023). This aggregation masks potential period-specific variations in vital rates and may obscure subtle yet biologically significant demographic shifts. These shifts could serve as early indicators of decreasing population viability (Horswill et al. 2021).

We developed a novel cross-sectional framework employing stratified random sampling (to avoid selection bias) and recent statistical developments (to relax stationarity assumptions, Rouby et al. 2024), and identified an alarming decline in common dolphin (*Delphinus delphis*) viability in the Bay of Biscay. This protected marine mammal faces substantial bycatch mortality throughout the Northeast Atlantic, with particularly severe impacts in the Bay of Biscay (Taylor et al. 2022). The magnitude of this threat is exemplified by winter 2021 data, when an estimated 6900 common dolphins were incidentally captured in fishing gear (Peltier et al. 2024), out of an estimated winter abundance of 182,600 individuals (95% confidence interval: 128,600 - 258,052; Hammond et al. 2021). Annual bycatch mortality in this region consistently reaches several thousand individuals (Peltier et al. 2016; Taylor et al. 2022). Despite this substantial mortality pressure, abundance estimates have not provided evidence of population decline (Hammond et al. 2021). Under the European Union's Marine Strategy Framework Directive (MSFD, 2008/56/EC), member states are required to maintain marine ecosystems in good environmental status (Shephard et al. 2015). Yet, there are currently no operational MSFD indicators of population viability on cetaceans, preventing a full assessment of their good environmental status. Currently, emergency measures have been taken for the years 2024–2026 with a 1-month closure of fishing activities in the Bay of Biscay to protect common dolphins. Mannocci et al. (2012) built a population matrix model for common dolphins in the Bay of Biscay, highlighting threats to long-term viability, but did not investigate period effects, the focus of this study. Here, we analyzed 759 stranded specimens (1997–2019) revealing declining female longevity and a 2.4% reduction in population growth rate, demonstrating stranding data's potential for early demographic warning signals.

## 2 | Methods

### 2.1 | Stranding Record

All marine mammal strandings in France are registered by the French stranding network. Data on strandings are centralized and curated at "Observatoire Pelagis" (UAR 3462, CNRS-LRUniv). Any stranded carcass that is being reported is collected by the local authorities and examined by trained members (Authier et al. 2014). Training sessions are organized annually to ensure accurate species and sex identification, clean collection of biological samples, and consistent biometrical measurements.

### 2.2 | Sample Selection

A stratified random sampling framework was implemented to ensure representative analysis of age-at-death data, as comprehensive aging cannot feasibly be conducted across all stranded specimens. To our knowledge, this represents the first application of stratified random sampling to cetacean age-at-death demographic studies, though this approach is widely used in fisheries and wildlife surveys to reduce sampling bias (Siniff and Skoog 1964). Stratification criteria encompassed four key variables: sex, mortality cause, stranding year, and body length. To ensure adequate sample sizes within strata while maintaining representativeness, we combined variables to create balanced strata and avoided creating overly small groups that could compromise statistical robustness. From within each stratum (defined by variable combinations), we randomly selected individuals, totaling 759 specimens (397 males and 362 females) for age determination through tooth sectioning and growth layer group counting, of which 15% had previously been aged in prior investigations (e.g., Mannocci et al. 2012; Figure 1). Aging methodology remained consistent across all studies as the same trainer supervised age determinations following standardized age reading protocols for delphinid teeth (detailed protocol is given in Supporting Information). This methodological approach ensured that our resultant sample accurately represented the full spectrum of stranding events through stratified random sampling across key demographic and temporal variables, with random selection within strata preventing systematic bias toward any particular group. Throughout subsequent analyses, we operated under the assumption that demographic parameters derived from these stranded individuals provided a reliable proxy for the living, at-sea population. Age-at-death distributions are available in Figures S1 (proportion and frequency per age) and S2 (proportion by age and period).

### 2.3 | Demographic Modeling

Our demographic modeling framework assumes: (1) a closed population without immigration or emigration; (2) temporally constant stranding and detection patterns; and (3) that demographic characteristics of stranded individuals reflect those of the living population. We evaluate these key assumptions and discuss potential violations.
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Sampling methodology.</figcaption></figure>

*Note:* Each registered stranded dolphin in the database was assigned to a stratum (i.e., group) given its sex, cause of death, year of stranding, and body length at death.

**2.3.1 | Proportion of Mature Females and Age-Specific Fertilities**

The proportion of sexually mature females at each age was estimated using a logistic regression model fit to the sexual maturity data. Female common dolphins (*n* = 166) were analyzed using a binomial generalized linear model with sexual maturity status (mature = 1, immature = 0) as the response variable and age as the predictor. These 166 females represented a subset of the 362 females in our sample that had adequate preserved gonadal tissue for reproductive status determination. Maturity was defined by the presence of *corpus albicans* in female ovaries. From this model, we calculated the age at sexual maturity (ASM), defined as the age at which 50% of females are mature. The equation for proportion of mature females over ages is available in Supporting Information.

Age-specific fertility rates were calculated using age-specific pregnancy rates across five age classes (ages 0–3, 4–8, 9–12, 13–17, and 18+) derived from our sample of necropsied mature females and common age class segregation (Murphy et al. 2009). Fertilities represent the expected number of female offspring per female at each age, computed by multiplying the proportion of mature females by the age-specific pregnancy rate, the birth sex-ratio (assumed 1:1), and by the mother's survival rate of age *x* (pre-breeding census representation; Kendall et al. 2019).

**2.3.2 | Survival Rates**

Using a flexible linear model able to fit a bathtub-shaped hazard rate curve (which accommodates megafauna mortality pattern, Rouby et al. 2021), we investigated period effects (i.e., temporal factors specific to each calendar year that influence mortality rates across all ages) in survivorship (i.e., the probability of surviving from birth to a given age) and age-specific survival rates (probability of surviving from age *x* to *x* + 1). Eight models

were fitted to age-at-death data using the approach described in Rouby et al. (2024) to estimate sex, period, and temporal trend effects in survivorship. Model selection was done using the leave-one-out information criterion (Vehtari et al. 2017). Equations of survivorship and age-specific survival rate estimates from model outputs are available in Supporting Information.

**2.3.3 | Population Growth Rate**

From age-specific survival rates and fertilities, female matrix population models were built, for each year, and the dominant eigenvalue was computed (Caswell 2001). Survivorship was estimated from the complete sample of 759 aged specimens (397 males and 362 females), while sexual maturity and fertility parameters were derived from the subset of 166 females with adequate gonadal tissue preservation. Population growth rates combined survivorship estimates from the full dataset with fertility parameters from the female subset. Assuming the age-at-death dataset provides unbiased estimates of population demographic parameters, this metric represents the deterministic population growth rate ($\lambda$). However, while absolute values may be biased due to limitations in age-dependent survival rate estimation (Rouby et al. 2024), relative temporal trends in growth rate remain robust indicators of population trajectory. The main focus was on relative changes in the growth rate over the study period.

Reproducible R scripts and data are available on the following github repository: https://github.com/erouby/dolphin_viability_cons_letter. The repository includes step-by-step scripts to reproduce all figures, tables, and Supporting Information. Statistical analyses were carried out on R version 4.3.1 (R Core Team 2022) using RStan version 2.21.8 (Stan Development Team 2023). Details for model specifications and priors are provided in Supporting Information. Model selections, through WAIC and LOOIC, are given in Figures S3 and S4. Convergence results, for the best model, are given in Figures S5 and S6.
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Age-specific proportion of sexually mature female common dolphins.</figcaption></figure>

*Note:* The black curve shows the predicted probability of sexual maturity as a function of age based on a logistic regression model, with the gray band representing the 95% confidence interval. Blue points represent observed proportions of mature females at each age, with point size indicating sample size (see legend). The vertical red dotted line indicates the age at sexual maturity (ASM = 7.3 years), defined as the age at which 50% of females are expected to be sexually mature (horizontal red dotted line).

## 3 | Results

Our stratified random sampling of stranded common dolphins in the Bay of Biscay yielded 759 aged specimens (397 males, 362 females) collected between 1997 and 2019, with 166 females providing adequate gonadal tissue for reproductive analysis. From these data, we documented a 7-year decline in estimated female longevity (24 to 17 years), corresponding to a 2.4% reduction in population growth rate over the study period.

### 3.1 | Age at Sexual Maturity

The logistic regression analysis yielded an estimated ASM of 7.3 years (Figure 2, 95% CI: 6.47–8.11) for females, which aligns closely with previous estimates reported by Mannocci et al. (2012). From necropsies, we estimated age-specific pregnancy rates that varied across five age classes: 0% for ages 0–3, 12.5% for ages 4–8, 40% for ages 9–12, 35% for ages 13–17, and 30% for ages 18 and older.

### 3.2 | Declining Female Survivorship

The best model included a negative trend in survivorship over time and the effect of sex (Table 1, see Figures S3 and S4 for WAIC and LOOIC plots. And Figures S5 and S6 for convergence diagnostics of the best model). Estimated female longevity decreased from 24 ($l_{24} =_{0.00} 0.01_{0.04}$) to 17 ($l_{17} =_{0.00} 0.01_{0.04}$) years old between 1997 and 2019 (Figure 3).

### 3.3 | Early Warnings of Change in Population Viability

Estimated population growth rate exhibited a consistent decline of approximately 0.3% annually over the 22-year study period (Figure 4). Between 1997 and 2019, the cumulative reduction in growth rate reached 2.4%, attributable exclusively to diminishing survivorship, as all other demographic parameters were held constant in our analysis. This result was derived under the conservative assumption of temporal stability in both age at sexual maturity and fertility rates throughout the study timeframe.

## 4 | Discussion

This study provides the first framework for estimating temporal vital rate variations in wide-ranging cetaceans that lack Capture-Mark-Recapture monitoring yet face severe anthropogenic impacts (Peltier et al. 2016; Taylor et al. 2022). Nevertheless, our framework assumes a closed population, an assumption unlikely to hold for most cetaceans and other wide-ranging species. Violating this assumption could bias mortality and population growth rate estimates through unaccounted migration effects. While no population substructure has been documented for common dolphins (Murphy et al. 2021), we identified the Bay of Biscay population as a demographic sink based on declining growth rate trends despite stable abundance estimates. This inconsistency between observed demographic decline and stationary abundances (Geelhoed et al. 2022), combined with high bycatch mortality (Peltier et al. 2016; Bouchard et al. 2019),
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Decline in common dolphin female survivorship from 1997 to 2019.</figcaption></figure>

*Note:* Estimated female longevity decreased from 24 to 17 years. Cooler colors (blue and green) correspond to older years. Warm colors (light green and yellow) correspond to recent years.

<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Relative change in estimated population growth rate of common dolphins in the Bay of Biscay.</figcaption></figure>

*Note:* The dotted line represents no change from the 1997 baseline. The population growth rate declined consistently by approximately 2.4% from 1997 to 2019. 95% Confidence intervals are displayed as vertical bars.
<table><caption>TABLE 1 | Model selection for survivorship using the leave-one-out information criterion (looic).</caption><thead><tr><th>Rank</th><th>Period (random)</th><th>Period (trend)</th><th>Sex</th><th>looic<sub>se</sub></th><th>Δ<sub>se</sub></th></tr></thead><tbody><tr><td>1</td><td></td><td>×</td><td>×</td><td>553.0<sub>16.3</sub></td><td>0.0<sub>0.0</sub></td></tr><tr><td>2</td><td></td><td>×</td><td></td><td>600.0<sub>16.8</sub></td><td>47.0<sub>8.4</sub></td></tr><tr><td>3</td><td></td><td></td><td>×</td><td>728.0<sub>17.9</sub></td><td>175.0<sub>13.2</sub></td></tr><tr><td>4</td><td>×</td><td></td><td>×</td><td>799.0<sub>18.7</sub></td><td>246.0<sub>14.9</sub></td></tr><tr><td>5</td><td>×</td><td>×</td><td></td><td>801.0<sub>19.0</sub></td><td>248.0<sub>15.1</sub></td></tr><tr><td>6</td><td></td><td></td><td></td><td>820.0<sub>18.9</sub></td><td>267.0<sub>15.3</sub></td></tr><tr><td>7</td><td>×</td><td></td><td></td><td>829.0<sub>19.3</sub></td><td>276.0<sub>15.7</sub></td></tr><tr><td>8</td><td>×</td><td>×</td><td>×</td><td>1063.0<sub>21.1</sub></td><td>510.0<sub>17.3</sub></td></tr></tbody></table>

Note: Models are ranked from best (lowest looic) to worst. Standard errors (se) are reported. Differences in looic (Δ) and their standard errors were computed using the function *loo_compare()* in package *loo* (Vehtari et al. 2017).

The × in table 1 is just to highlight what is included in the model. So for example, the number 1 ranked model includes Period (trend) and Sex effects.

indicates probable immigration from adjacent waters. Our demographic estimations, therefore, likely reflect local Bay of Biscay dynamics rather than the broader Northeast Atlantic common dolphin population.

The documented female survivorship decline may link to high human-induced mortalities in the Bay of Biscay (Taylor et al. 2022; Peltier et al. 2024). While our approach cannot establish causality between bycatch and reduced viability, the demographic signals remain concerning. Our observed 2.4% decline is substantial compared to the maximum 4% growth rate typically assumed for small cetaceans not under carrying capacity (Wade 1998). Even in an optimistic scenario with maximum initial growth, our estimated decline would reduce growth rates from 4% to 1.6% by 2019. Under more realistic scenarios with lower initial growth, populations could shift to negative growth. This conservation concern persists regardless of baseline growth rate, which cannot be precisely determined from cross-sectional approaches. Although our survivorship estimates are unbiased, age-specific survival rates used in matrix population models remain less reliable (Rouby et al. 2024).

Stranding samples inherently involve selectivity bias. Our approach relies on two key assumptions: (1) demographic characteristics of stranded individuals reflect the living population; and (2) selectivity bias remained temporally consistent. Regarding the first assumption, while absolute demographic parameters may differ from the living population (Caughley 1966), temporal trends remain robust population dynamics indicators (Holmes 2001). For the second, selectivity bias in common dolphins combines fishery bycatch probability, carcass drift dynamics (Peltier et al. 2016), and reporting efficiency (Authier et al. 2014). Consistent fishing practices throughout our study period (Peltier et al. 2021) and temporally stable reporting rates along the French coastline (Authier et al. 2014) support the accuracy of relative changes in population viability.

Relaxing the stationarity assumption customary in classical survivorship estimation from age-at-death data (e.g., Betty et al. 2023), we documented a clear downward trend in common dolphin survivorship from 1997 to 2019 in the Bay of Biscay. Up to

7 years of expected adult life were lost between 1997 and 2019. This represents ≈ 2 to 3 fewer breeding events per female on average, assuming calving intervals 3.5 years (Murphy et al. 2009) or 2.1 years (Taylor et al. 2007), respectively. Independent of the growth rate decline we documented, this result is alarming as it indicates reduced lifetime reproductive success essential for maintaining population viability.

We developed a population viability indicator for timely management and proactive decision-making: the relative change in population growth rate (Figure 4). This early warning predicts abundance decline before detection via sea survey data (de Silva and Leimgruber 2019), and can immediately inform cetacean management in the Northeast Atlantic. The European Union's MSFD currently lacks operational population viability indicators for cetacean species. Our study fills this gap by (1) demonstrating novel methodology to make the most of small toothed cetacean stranding data and (2) providing timely and policy-relevant assessment of long-term population viability.

Assessing population viability using only abundance trend indicators risks being a “witness to extinction” (Survey 2008): management decisions occur too late. The slow pace of life of long-lived vertebrates (de Silva and Leimgruber 2019) leads to prolonged recovery from population depletion. Some whale species remain unrecovered from 19th-century commercial exploitation (International Whaling Commission 2024). Documenting reduced viability for common dolphins in the Bay of Biscay answers calls for ecological forecasts (Clark et al. 2001; de Silva and Leimgruber 2019) and proactive conservation measures. To meet ambitious biodiversity conservation targets (e.g., the European Union Biodiversity Strategy for 2030), management actions minimizing anthropogenic impacts are essential in the Northeast Atlantic, particularly to remedy the demographic sink the Bay of Biscay has become. The current 1-month winter closure for high-risk fishing vessels exceeding 8 meters, implemented for 2024–2026, is an emergency measure to reduce human-induced mortalities in the short-term. However, our demographic findings suggest that long-term measures are needed to redress the Bay of Biscay’s status from a demographic sink to a healthy ecosystem.
## Author Contributions

E.R., F.P., V.R., and M.A. designed the research; E.R. performed the research; E.R. analyzed the data; E.R., A.M., and W.D. acquired the data; E.R., F.P., V.R., A.M., W.D., J.S., and M.A. wrote the paper.

## Acknowledgments

This study is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS. We thank the French "Ministère de la Transition Ecologique et Solidaire (MTES)" and the "Direction de l'Éau et de la Biodiversité" (DEB) for funding this research. We thank all the field-workers involved in the French National Stranding Network, who help maintain a constant observation effort on the seashore, and the collection of stranded animals. We thank all the interns involved in the sampling and data collection. We thank colleagues at Woods Hole Oceanographic Institution and CU Boulder for their inputs and feedbacks.

## Conflicts of Interest

The authors declare no potential conflicts of interest.

## Data Availability Statement

All data used to conduct this study are available with the PelaMSFD R package: https://gitlab.univ-lr.fr/pelaverse/pelaMSFD. Reproducible scripts are available at the following Github repository: https://github.com/erouby/dolphin_viability_cons_letter

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.

Data S1 Figure S1: Distribution of age at death along with frequencies out of 759 individuals. Figure S2: Distribution of age at death per year. Figure S3: WAIC values for model selection. The lowest is the best model. Figure S4: LOOIC values for model selection. Figure S5: Convergence for parameters of the selected model. Figure S6: Rhat for the parameters of the selected model.