Conservation Science in the Anthropocene
The field of conservation science is continuously changing as its practitioners strive to understand and provide data on long-known (e.g., climate change, deforestation, invasive species) and newly recognized (e.g., consequences of gene editing, mercury release from thawing permafrost, microplastics) threats to biodiversity and ecosystem services (Kareiva and Marvier 2012; Bennett et al. 2015; Brodie et al. 2018; Phelps et al. 2019; Sutherland et al. 2019). Among those changes has been a shift in thinking on the place of humans in conservation science (e.g., Marris 2011; Kareiva and Marvier 2012; Naeem et al. 2016). Humans and their needs now figure more prominently—as demonstrated by an increasing emphasis on human-facing goals (e.g., maintaining useful resources for present and future generations, providing durable opportunities for nonconsumptive environmental experiences) in conservation (Camacho et al. 2010; Bennett et al. 2015; Naeem et al. 2016; Barnosky et al. 2017; Hevia et al. 2017; Watson et al. 2019)—even as outcomes from human actions lead us toward an uncertain future with novel ecosystems (Marris 2011; Corlett 2015; Collier and Devitt 2016).
Recognizing that future ecosystems will be made up of novel combinations of species, the methods and metrics being applied in conservation are becoming less species-centric (e.g., Borja et al. 2000; Simboura and Zenetos 2002; de Bello et al. 2010; Mouillot et al. 2013; Barnosky et al. 2017; Brodie et al. 2018). Where approaches to conserving and restoring the composition and abundance of species to some ideal, “natural” state of the past has long been the focus, efforts have expanded to include approaches that can more readily evaluate changing ecosystems, and changing combinations of species in those ecosystems, that inevitably lie ahead as the climate changes in the coming decades (Camacho et al. 2010; Marris 2011; Hobbs et al. 2014; Stein et al. 2014; Lawler et al. 2015). For instance, reflecting on the intertwined futures of humans, climate, and terrestrial ecosystems in the Anthropocene, Barnosky et al. (2017) advocated for increased use of taxon-free, along with—and often in place of—traditional species-centric conservation approaches (see also Bremner et al. 2003; Caswell and Frid 2013). Here, we extend Barnosky et al.'s (2017) advocacy to marine conservation paleobiology (CPB). We focus on how the application of geohistorical data to established benthic indices can both add new taxon-free options to the traditional live–dead analysis tool kit and potentially increase the salience of geohistorical data in the eyes of resource managers and other stakeholders.
Live–Dead Studies in Marine Conservation Paleobiology
CPB has the potential to provide data that are salient—relevant to the needs of decision makers and timely (Cash et al. 2003)—through its unique long-term perspective (e.g., Jackson et al. 2001; Volety et al. 2009; Rick and Lockwood 2013; Wingard and Lorenz 2014; Dietl et al. 2015; Durham and Dietl 2015; Smith et al. 2018; Leonard-Pingel et al. 2019; Powell et al. 2020). To date, the contributions of marine CPB have mainly consisted of providing geohistorical baselines of past species' composition and abundance in communities (Kowalewski et al. 2000; Jackson et al. 2001; Dietl and Flessa 2011, 2017; Louys et al. 2012; Rick and Lockwood 2013; Dietl et al. 2015; Tyler and Schneider 2018). Although this focus on the historical fidelity of communities (i.e., “maintaining the same ecosystem”; sensu Barnosky et al. 2017) has mirrored the broader field of conservation science, now, just as the broader field has expanded its focus and is developing tools for assessing and managing today's rapidly changing ecosystems, so too must marine CPB (Fig. 1; Barnosky et al. 2017; Dietl and Flessa 2017).
During the short tenure of CPB, live–dead comparisons—considered here, generally, to be quantitative analyses of differences and similarities between an assemblage of living individuals and the corresponding time-averaged assemblage of biological remains accumulated through turnover of individuals in the local community—have been, arguably, the most commonly applied approach in studies of human impacts on marine ecosystems (e.g., Kidwell 2002; Lockwood and Chastant 2006; Weber and Zuschin 2013; Casey et al. 2014; Korpanty and Kelley 2014; Albano et al. 2015; Leshno et al. 2015; Dietl and Smith 2017; Martinelli et al. 2017; Wingard 2017). Building on earlier efforts (e.g., Davis 1923; Johnson 1965; Warme 1969), Kidwell (2001, 2007) demonstrated that communities preserved in time-averaged death assemblages can reasonably be expected to match their associated living assemblages when evaluated with abundance-based metrics (e.g., taxonomic similarity, rank-order abundance) unless human actions (e.g., eutrophication, dredging) have impacted the community. Subsequent live–dead comparisons in CPB have often employed the analytical approach developed by Kidwell (2001, 2007), plotting Jaccard-Chao taxonomic similarity (Chao et al. 2005) against Spearman's rank-order abundance to evaluate whether species' compositions and abundances in communities have changed. High values for both metrics indicate agreement between living and death assemblages, signaling fidelity with historical conditions and limited anthropogenic effects—though false negatives are not uncommon (Kidwell 2007), as the approach is relatively conservative (Casey et al. 2014; Dietl and Smith 2017). Low values for either metric are expected when the community has experienced recent human impacts and suggest that action may be required to restore a community that is consistent with historical baseline conditions (Kidwell 2009). The purpose of this approach is, largely, to detect change and establish targets for restoration (Kidwell 2009). As such, the approach is best suited to inform the traditional conservation goal of “maintaining the same ecosystem” (Fig. 1), reflecting the inherent assumption that natural systems fluctuate within an unchanging envelope of variability (Milly et al. 2008).
Expanding circle of conservation goals and metrics in the rapidly changing Anthropocene. If the environment has changed (outer circle), business-as-usual goals (e.g., restore or retain the same ecosystem state) may no longer be possible. In such cases, it may be more appropriate to set other goals, such as mimicking the structure of the past ecosystem or maximizing ecological quality status. Adaptable taxon-free metrics with the capacity to detect changes based on species' tolerances and sensitivities to human disturbance (e.g., AZTI Marine Biotic Index [AMBI], multivariate AMBI [M-AMBI]) are more suited to addressing these new goals than more traditional metrics (e.g., Jaccard-Chao, richness). If, however, the environment has remained relatively stable (shaded, inner circle) and the goal is to maintain the ecosystem, traditional metrics are also useful.
The live–dead approach developed by Kidwell (2001, 2007) for the purpose of evaluating community composition with respect to anthropogenic eutrophication implicitly requires as a prerequisite that the environment in which the community lives did not change appreciably across the duration of time (e.g., decades, centuries) represented by the living and death assemblages (Kidwell 2013). Before eutrophication—or another agent of change—can be accepted as a causal factor driving differences between living and death assemblages, all other possible explanations must be considered, whether by collecting additional data or making a priori assumptions. In the rapidly changing Anthropocene world, however, change is the new norm (Halpern et al. 2008; Stein et al. 2014). As species move in response to, and because of, human actions and multiple environmental stressors (e.g., Aronson et al. 2007; Greenstein and Pandolfi 2008; Sunday et al. 2012; Pinsky et al. 2013; Smith and Dietl 2016; Morley et al. 2018; Saupe et al. 2019; Powell et al. 2020), the underlying assumption of environmental (i.e., climatic) stability inherent to Kidwell's approach becomes increasingly untenable for establishing restoration targets (Wolkovich et al. 2014; Corlett 2015; Kopf et al. 2015).
An Adaptable Live–Dead Approach for a Changing World
If marine CPB is to contribute salient data to the conservation and restoration of biodiversity and ecosystem services, the set of tools used to conduct live–dead analyses must include methods and metrics with the capacity to evaluate novel, changing communities in the Anthropocene world. Without such methods and metrics, the invaluable long-term perspective of live–dead CPB studies is at risk of being relegated to the wrong (i.e., academic) side of the research-implementation gap (Arlettaz et al. 2010). Taxon-free approaches (e.g., trait-based approaches, benthic indices), which focus on ecosystem structure and function (i.e., natural processes) as conservation targets rather than the abundance of particular species, can help bridge the gap, because they are a means for consistently comparing data from different times and places (Eronen et al. 2010; Polly et al. 2011; Dietl et al. 2016; Barnosky et al. 2017; Dietl and Flessa 2017). Inherently taxon-free metrics, such as richness and evenness (i.e., probability of interspecific encounter; Hurlbert 1971), have been applied in live–dead studies to evaluate ecosystem structure (e.g., Olszewski and Kidwell 2007; Casey et al. 2014; Dietl and Smith 2017); however, the diagnostic power of the information conveyed by these measures is limited, because, as discussed by Kröncke and Reiss (2010), these metrics will respond to any change in species' presence and abundance no matter the ecological role of those species. In contrast, taxon-free benthic indices (e.g., Karr 1981, 1991; Kerans and Karr 1994; Borja et al. 2000; Simboura and Zenetos 2002) are based in organismal biology (e.g., life histories and sensitivities to environmental factors; Schaeffer et al. 1985; Borja et al. 2000; Simboura and Zenetos 2002; Adakole and Anunne 2003; Bellinger et al. 2006; Sutherland et al. 2007; Hess et al. 2020), leading to a more direct stressor–response relationship and a corresponding increase in the power to detect eutrophication (or other types of disturbances; Borja et al. 2015). As such, integrating geohistorical data with taxon-free benthic indices (e.g., Dietl et al. 2016) may be a fruitful opportunity to increase the salience of geohistorical data in a rapidly changing Anthropocene world (Dietl et al. 2016; Leshno et al. 2016; Tweitmann and Dietl 2018; Caswell et al. 2019).
For the last two decades, taxon-free benthic indices have been used in Europe to assess the effects of eutrophication after the European Union (European Commission 2000, 2008) mandated an ecological quality status (EcoQS) assessment of all European coastal and estuarine waters. Among the many metrics that have been developed, the AZTI Marine Biotic Index (AMBI; Borja et al. 2000) has been one of the most widely used, including in the United States (e.g., Gillett et al. 2015; Pelletier et al. 2018; Tweitmann and Dietl 2018)—now facilitated more readily by the expansion of the software's species list to include species of North and South America. Dietl et al. (2016) demonstrated that the results of AMBI for the entirety of the living benthic community (i.e., annelids, arthropods, mollusks) can frequently be reproduced using solely the molluscan component of the community. Examining the benthic community data from 12 studies, Dietl et al. (2016) calculated AMBI with only mollusks, emulating the type of data expected to be found in a death assemblage, and compared the results with AMBI values calculated from the whole macrobenthic invertebrate community. They developed a correction based on the relationship between whole-community and mollusk-only values that, when applied to the mollusk-only data, successfully recovered whole-community EcoQS for 78% of stations (n = 45), validating the use of AMBI for the evaluation of the effects of eutrophication with the geohistorical record (e.g., Leshno et al. 2016; Tweitmann and Dietl 2018; Caswell et al. 2019).
Using the mollusk-only approach, AMBI can be applied in live–dead comparisons using sampling methods and data collection identical to those traditionally applied in live–dead studies. With AMBI, benthic species are assigned to one of five ecological groups, and the relative abundances of individuals in these five groups are used to calculate a single AMBI value for the community according to:
where GI through GV are ecological groups with increasing tolerance for disturbance (especially eutrophication; Borja et al. 2000). Although species' identities are used, interpretations and assessments are made on the basis of the community's AMBI value. Output values for AMBI range from zero to seven, and these values map to EcoQS categories of High (0 < AMBI < 1.2), Good (1.2 < AMBI < 3.3), Moderate (3.3 < AMBI < 4.3), Poor (4.3 < AMBI < 5.5), and Bad (5.5 < AMBI 7.0; Borja et al. 2004). Unlike any other metric applied in marine live–dead studies, samples with AMBI values indicating Moderate, Poor, and Bad EcoQS require remediation in the European Union (European Commission 2000)—though there are no regulatory implications for this benthic index outside the European Union at this time.
The subsequent development of multivariate AMBI (M-AMBI; Muxika et al. 2007) offers additional potential to improve the salience of geohistorical data (e.g., Tweitmann and Dietl 2018). Building on the AMBI framework, the M-AMBI calculation includes richness and Shannon-Wiener diversity and, of particular relevance to CPB, a reference condition (Muxika et al. 2007). M-AMBI, as with all indices developed under the Water Framework Directive (European Commission 2000) and Marine Strategy Framework Directive (European Commission 2008), is calibrated to the same five EcoQS categories as AMBI, though its output values vary between 0 and 1 instead of 0 and 7. There is a clear opportunity to integrate live–dead data as reference conditions in M-AMBI, particularly as identifying appropriate reference conditions can be challenging (Borja et al. 2004; Halpern et al. 2008; Van Hoey et al. 2010).
The AMBI and M-AMBI indices can readily accommodate the inclusion of geohistorical data (e.g., Dietl et al. 2016; Leshno et al. 2016; Tweitmann and Dietl 2018; Caswell et al. 2019) and, because they are calculated based on relative proportions of ecological groups that are independent of particular species' identities, they do not require the assumption that the environment has remained stable (Kröncke and Reiss 2010; Van Hoey et al. 2010). This feature, combined with the grounding of ecological group assignments in species' tolerances and sensitivities to human disturbance (Kröncke and Reiss 2010; Borja et al. 2013; Elliott et al. 2015), makes both AMBI metrics useful tools for assessing EcoQS, irrespective of environmental changes. These benthic indices also distill a complex suite of biological information into a single EcoQS score that is easily interpretable and readily translatable (e.g., as required by law in the European Union; Muxika et al. 2007; Borja et al. 2012, 2013), which is an advantage for managers and decision makers over other metrics whose interpretation requires additional data and/or assumptions. As described by Borja et al. (2012): “Politicians and managers need information derived from simple and pragmatic, but scientifically sound methodologies, to show clearly to society the change in the ecological quality of a given geographical zone (estuary, coastal area, etc.) reflective of human pressures or recovery processes” (p. 1).
Examples of potentially biasing factors introduced to benthic indices when using data from molluscan death assemblages.
Though there will still be cases where historical fidelity (i.e., maintaining the same ecosystem) is the primary conservation goal, methods and metrics that can be more broadly applied in the Anthropocene and that address human-facing goals (e.g., maximizing potential resource use; Camacho et al. 2010) are becoming more prominent in conservation science (Fig. 1). Live–dead studies in CPB have unrealized potential to address the expanding set of goals (e.g., Marris 2011; Barnosky et al. 2017) associated with this trend. To do so, marine CPB must expand the live–dead tool kit to include assessment approaches capable of handling rapidly changing environments.
For the field of marine CPB, the application of geohistorical data with taxon-free benthic indices is a promising avenue for both academic research and real-world application. If this integration is to be effective and impactful, several potentially biasing factors must be addressed (Table 1). For instance, issues related to time averaging (e.g., increased evenness and richness in death assemblages; Olszewski and Kidwell 2007), preservation (e.g., life span bias; Cronin et al. 2018), and sensitivity to disturbance (e.g., the majority of mollusks are categorized into the more sensitive ecological groups; Dietl et al. 2016) have the potential to either increase or decrease the EcoQS that is calculated. Furthermore, although Dietl et al. (2016) evaluated the prospect of integrating geohistorical data with two commonly used indices, AMBI (Borja et al. 2000) and Bentix (Simboura and Zenetos 2002), it remains to be seen whether these indices or other indices will perform best in marine CPB. At the very least, it will be informative to understand differences in the applicability of different benthic indices (e.g., Benthic Index based on Taxonomic Sufficiency [BITS], which uses family- instead of species-level identifications; Mistri and Munari 2008) with death assemblage data, as differences in the performance of indices may provide insight on where more research is needed.
Benthic indices for EcoQS are well established, have clear policy implications, and can be applied using the data already collected by marine conservation paleobiologists. By integrating geohistorical data with these established indices, CPB can add new taxon-free options to the traditional live–dead analysis tool kit and increase its salience in the eyes of resource managers and other stakeholders.
We thank the editor, Wolfgang Kiessling, and three anonymous reviewers for their comments, which improved the quality of this manuscript.
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