The relationship between genetic diversity, function, and stability in marine foundation species Free

Abstract

Seagrasses, corals, marsh plants, kelps, and mangroves are all major contributors to primary production and form the foundation of shallow water ecosystems. They are critical habitat for a large diversity of invertebrates and fish (Zimmerman et al. 2002, Nagelkerken et al. 2008, Christie et al. 2009, Coker et al. 2014, Cullen-Unsworth et al. 2014) and provide structure that buffers wave energy and prevents shoreline erosion (Walters et al. 2008, Nelson and Zavaleta 2012, Christianen et al. 2013, Ferrario et al. 2014, Teagle et al. 2017). Despite their value, these foundational taxa are vulnerable to disturbances, and the coastal ecosystems they support have been heavily degraded and threatened (Gardner et al. 2003, Waycott et al. 2009, 2015, Krumhansl et al. 2016, Goldberg et al. 2022). The proximity of coastal ecosystems to terrestrial ecosystems makes them vulnerable to disturbances such as oil spills, eutrophication, coastal development, and hurricanes (Crain et al. 2009). Furthermore, they face increasing pressures brought about by climate change, such as sea level rise, and altered rainfall and freshwater delivery patterns (Wong et al. 2014). The conservation and restoration of foundational species is needed to maintain ecosystem services and function, but this has become an increasingly difficult task, given these multiple threats. Management efforts of coastal ecosystems have proven unsuccessful in many instances because of the vulnerability of organisms to environmental stressors (Boström and Mattila 2005, Cunha et al. 2012, Lovelock et al. 2022). To provide long-term protection and restoration of ecosystem services, managers need to focus on mechanisms that promote ecosystem function and stability (Unsworth et al. 2015).

One potential way to ensure ecosystem function and the stability of coastal ecosystems is for managers to consider the genetic variation of foundational species. Genetic diversity plays a role in shaping ecological responses, defined as any measurable effect at the species, population, community, or ecosystem level, such as population dynamics and species interactions, as well as the stability of these responses (Hoban et al. 2022). These concepts are referred to in ecological theory under two different but related hypotheses, referred to as the diversity–function and diversity–stability hypotheses, respectively. The diversity–function hypothesis states that ecosystems that harbor communities with higher species diversity maintain greater ecosystem productivity and function (Steudel et al. 2012). Under diversity–stability, Elton hypothesized that simpler ecosystems or those with fewer species interactions are more easily perturbed than complex ones with greater diversity (Elton 1958). Communities with higher diversity are believed to have species that occupy overlapping niches (Rosenfeld 2002). This redundancy within niches then prevents the community from entering an alternative stable state after a disturbance, because the species that do survive will maintain the functions of those that did not. Under the diversity–stability hypothesis is the concept of resilience. Resilience refers to both resistance and recovery (Hodgson et al. 2015). Resistance is the ability of an ecosystem to buffer the initial adverse effects of a disturbance. Recovery is the ability to return to its original state after a disturbance has passed.

Although researchers often focus on the role of species diversity in contributing to function response variables and stability, these two hypotheses apply to diversity on the genetic level as well (Vellend and Geber 2005). The ecological effects of genetic variation have been well studied in agricultural settings where increasing genetic varieties of a crop has increased overall crop yield or resistance to disease outbreaks (Smithson and Lenné 1996, Jump et al. 2009). It is also widely accepted that genetic rescue, adding genetic variation into a population through deliberate introductions, prevents populations from experiencing inbreeding depression (Hedrick and Garcia-Dorado 2016). However, increasing genetic diversity does not necessarily result in greater function and stability, because the relationship between the ecological response and diversity can be nonlinear. The ecological response can initially increase as diversity increases and can then plateau at a certain level because of complex interactions in time and space (Qiu and Cardinale 2020). The relationship between genetic diversity and function or stability can also depend on interspecific interactions and the ecological mechanisms involved in shaping these relationships (Des Roches et al. 2017).

The mechanisms that contribute to the relationship between diversity and ecological responses can be categorized as additive or nonadditive. In additive mechanisms, the relative initial abundance of each genotype and the ecological response of individual genotypes in a monoculture predict the overall outcome when grown in a polyculture. In nonadditive mechanisms, which are the most common, the response for a genetically diverse population is not predictable from the measurements of genotypes in isolation (Hughes et al. 2008, Whitlock 2014). Within the two categories of additive and nonadditive, there are four main processes that contribute to the relationship between diversity and function or stability: niche complementarity, facilitation, and sampling and selection effects. In some cases, the general term complementarity is used to refer to both niche complementarity and facilitation, which are both nonadditive mechanisms (Hughes et al. 2008, Vanelslander et al. 2009). Niche complementarity occurs when individuals or species use resources differently, limiting competition (Tilman 1999). Facilitation occurs when a species modifies the environment to benefit other species (Vandermeer 1989). Sampling effects, which is a common additive mechanism, arise because the probability of sampling a higher functioning genotype is greater in more diverse populations (Hector et al. 2002, Kettenring et al. 2014). Selection effects can occur in populations in which a high-functioning genotype dominates the population over time (Huston 1997, Kettenring et al. 2014). A more detailed discussion of the mechanisms can be found in Hughes and colleagues’ (2008) review.

Biotic and abiotic factors play a role in shaping genetic diversity and its relationship to function and stability, potentially affecting the relative strength of the previously mentioned processes (Procaccini et al. 2001, Sinclair et al. 2014, Foust et al. 2016). Regional environmental conditions and large-scale disturbances can affect genetic diversity by leading to the dominance of a single resistant genotype that can withstand some changes in water quality (Diaz-Almela et al. 2007). The frequency and duration of abiotic stressors can also, in turn, affect diversity and stability. For example, intense pulsed or chronic disturbances can lead to a loss of genetic diversity and local extinction (Banks et al. 2013). Furthermore, the ability of a community or population to withstand a disturbance will depend on the baseline standing stock of genetic diversity.

It has been over a decade since some of the early investigations of the relationship between genetic diversity and ecological responses in marine foundational species. Some research has shown that high genetic variation in marine primary producers increases productivity and stability (Hughes and Stachowicz 2004, Zogg and Travis 2022), which also translates into benefits for higher-order consumers (Reusch 2005, Reynolds et al. 2012). But there have been some examples from the literature where greater genotypic variation did not enhance stability (Robertson et al. 2017, Connolly et al. 2018). Recently, a meta-analysis of 16 publications for marsh plants and seagrasses indicated an overall positive correlation between genetic diversity and function and stability for these habitat forming species (Salo and Gustafsson 2016). Our objective was to expand on this former review by focusing more broadly on coastal foundational taxa including corals, kelps, and mangroves along with marsh plants and seagrasses. These five taxa exhibit different life histories and dispersal patterns and have different requirements for survival that may influence the impact of genetic diversity on function and stability.

Our objectives were to quantitatively summarize metadata in the existing literature to assess what is known about the genetic diversity–function and genetic diversity–stability relationships, to identify limitations in the current scientific understanding, and to suggest future avenues of research to inform conservation management action. Rather than conducting a meta-analysis on the data from past studies, we performed an analysis of the metadata because we felt this approach was best suited for the objectives of the study. We wanted to include the plethora of observational and correlative studies on the topic, which would not be advisable in a meta-analysis, where the combination of approaches can bias the resulting effect sizes such as the log response ratio or Hedge's g (Gates 2002). We focused our efforts on publications that could test a prediction of the diversity–function hypothesis (prediction 1) and two predictions of the diversity–stability hypothesis (predictions 2 and 3). Following the idealized diversity–function relationship, high genetic diversity is positively correlated with the measured response variables (prediction 1). Prediction 2, following the diversity–stability hypothesis, is that high genetic diversity helps maintain ecological responses under a disturbance, and in prediction 3, high genetic diversity confers greater resilience to a disturbance.

Survey of the literature

To survey for literature applicable to the diversity–function and diversity–stability hypotheses for five coastal foundation taxa, we performed a Web of Science search in December 2021 using the following search string:

The initial search returned 1533 publications from peer-reviewed journals, which were each screened for relevance and inclusion by a team of researchers (figure 1). To be included in the extraction process, the publications could not be a review or a meta-analysis and had to focus on one of the focal foundation species, to contain a metric of genetic diversity (e.g., genotypic richness, allelic diversity), and to include a measured response variable (e.g., survival, metabolism, production, or whole-ecosystem function) or the environment (e.g., temperature, salinity, irradiance). When a publication met these three requirements, we noted whether the study design included replication at all levels (within and among diversity and disturbance treatments or observations). Furthermore, if these studies included any kind of disturbance, we noted whether the authors reported baseline information on a response variable before the disturbance began and whether they included proper controls (i.e., plots or treatments that did not experience a disturbance). A publication was categorized as having a disturbance if the authors described it as such; it could be either a one-time event or chronic stressor.

Figure 1.

A literature search was conducted using search terms in the Web of Science on 1 December 2021. The publications returned were screened for inclusion. The publications in which one of the five focal taxa was not studied or in which a metric of genetic diversity was not measured were excluded. All of the reviews and meta-analyses were excluded, and the literature cited was screened for relevant publications. Metadata and outcomes were extracted from the remaining publications if they were able to test prediction 1, 2, or 3.

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We noted which hypothesis (diversity–function or diversity–stability) each publication would be able to test and determined which specific predictions of those hypotheses each publication assessed. In order to test the prediction that high genetic diversity is positively correlated with a measured response variable (prediction 1), a publication was required to report measurements of both genetic variation and at least one response variable. To evaluate whether high genetic diversity helps maintain a response variable under a disturbance (prediction 2), a publication must have measurements of genetic diversity and response variables, as well as a recorded stressor. A before–after–control–impact (BACI) design is required to test whether genetic diversity confers greater resilience to a disturbance (prediction 3). In this instance, a publication must measure a response variable and genetic diversity metric both before and after a disturbance regime, in comparison to a control that does not experience the disturbance.

To minimize personal bias between researchers in the selection process, the team held a workshop where we evaluated the relevancy, inclusion, and categorization of five publications on the basis of the criteria described above. Once we all agreed on these aspects, the 1533 publications returned by the search were split into three sets of 511 publications. A pair of researchers were assigned to each set. Both researchers in each pair independently evaluated at least 140 publications in their assigned set for the inclusion criteria and hypothesis prediction the publication could test. The two researchers then compared their evaluations and resolved any discrepancies. On reaching full agreement, one or both researchers would move on to finish the remainder in the set. When an agreement could not be reached between the two researchers, the publication was reviewed by a larger committee and was included or not on the basis of majority consensus. Reviews and meta-analyses (107 of the 1533) were not included in our data set, but the literature cited was cross-referenced to find 20 additional relevant publications (figure 1).

We extracted metadata from 129 publications that met our inclusion criteria. Metadata on the study design and results of each publication were extracted by one of four reviewers. Information on the focal organism (taxon and species), the study type (observational or manipulative), location (field or laboratory), region (including GPS coordinates when provided), and study duration were extracted from each publication. When the researchers collected data from or conducted experiments at multiple sites, the GPS coordinates for all sites were recorded in order to assess where the studies had been conducted and to identify gaps in geographical location. Publications that included metrics of coral Symbiodinium diversity were included into the coral category. A single coral species can be functionally different because of the type of symbionts, so the nine Symbiodinium types were, for this purpose, considered genetically different entities of coral.

We recorded the type of diversity metric used and the observed range of these values reported by each study. If the study manipulated levels of genetic diversity, we also noted the total number of levels in the study and the number of replicates used at each level. A list of all metrics used in each publication was compiled for each taxon (supplemental table S1). When a paper reported multiple metrics, each metric was tallied separately. The values are reported as the percentage of the total number of metrics tallied rather than the total number of publications.

Measured diversity metrics were divided into eight different categories (table S1). Genotypic information consisted of measurements such as a calculated genotypic richness, the number of genotypes, and evenness. The allelic information category included measurements such as number of alleles and calculations of allelic richness and allelic diversity. The haplotype or clade metrics category included the number of clades, the number of ITS-2 (ribosomal internal transcribed spacer 2) types, and the haplotype diversity. Other categories included observed heterozygosity, expected heterozygosity, inbreeding coefficients (F_IS and F_ST), nucleotide specific metrics, and diversity indices (Simpson's index, the Shannon Wiener index, etc.).

We recorded any environmental variables reported in the study and observed ranges because abiotic environmental variables can alter genetic variation itself and affect the strength of the relationship between genetic variation and response variables (supplemental table S2). Based on the results, we tallied publications that showed a correlation between genetic diversity and one or more environmental metric. The type and duration (in days) of any manipulated or natural disturbances that occurred during the study were recorded. In studies in which a disturbance was manipulated, the number of levels of each stress treatment was recorded. In studies involving a natural or accidental disturbance, it was noted whether the stressor was ongoing after the study ended.

We recorded the response variables used in each study and noted how many times throughout the experiment it was measured. The extracted response variables were categorized into six categories: abundance, physiology, morphometric, reproduction, community, and ecosystem (tables 1a–1c). Abundance metrics consisted of measurements such as density and biomass. Physiology included metrics related to metabolism, pigment content, and growth rates. Morphometric measurements included height, length, width, and surface area. Reproduction consisted of measurements such as flower and fruit production and seed count. Community metrics were those that related to any other organism beyond the focal species. For example, macrofauna abundance, levels of herbivory, and grazer and epiphyte biomass or diversity were all counted as community metrics. Ecosystem-level metrics were those that described an ecological process and would affect all organisms such as nutrient cycling or decomposition. The number of metrics in each category was tallied for each taxon in both hypotheses (diversity–function and diversity–stability). The results are reported as a percentage of instances in each category out of the total number reported for each taxon. We noted whether the study showed a positive, neutral, or negative correlation between each response variable and a genetic diversity metric. The outcome was listed solely on the basis of its correlative relationship with genetic diversity and not on the basis of the nature of the response variable. For example, a paper that reports a positive correlation between herbivory rates and genetic diversity in a focal species would still be counted as positive even though increased herbivory might have a detrimental effect on the focal species. In many cases, researchers measured a suite of response variables and genetic diversity metrics, which can lead to a combination of positive, negative, and neutral results. These cases are noted accordingly in tables 1a–1c.