It is in this context that Floristic Quality Assessment (FQA)‐based habitat measures have successfully emerged as a bridge between judgment‐based and quantitative assessment. Floristic Quality Assessment is widely used and is highly influential, especially in North America. It is increasingly relied upon to guide ecological policy, regulation, conservation, management, and research. However, its use, study, and philosophical basis have never been simultaneously reviewed. This has led to a disordered FQA literature, which has led to theoretical misunderstanding, methodological errors, and frequent misapplication. Justified skepticism and criticism of FQA has followed. An overview of FQA is long overdue. This review will characterize the relevant literature, research, and application of FQA for its users, with particular a focus on misunderstandings, misuses, and criticisms of it.

To generalize, two kinds of vegetation‐based habitat assessments are used for this type of work, judgment‐based and metric‐based. Professional judgment is fast and it takes advantage of accumulated field experience and knowledge, but it may not seem objective, repeatable, or precise. Alternatively, quantitative metrics can be repeatable and objective, but they too have flaws. For example, simple, familiar numbers like species richness are insufficient for quantifying more complex vegetation properties of interest. The properties responsible for a natural area's uniqueness and value are often immediately apparent to on the ground observers, but they are not captured by diversity measures (Smith and Theberge 1986 , Mace 2005 ). More sophisticated ecological measures, such as those measuring endemism, rarity, regional uniqueness, taxonomic distinctness, specialization, intactness, completeness, uniquity, or more complex types of diversity (Izco 1998 , Ricotta 2004 , Scholes and Biggs 2005 , Hawkins 2006 , Devictor et al. 2008 , Chapman et al. 2009 , Filippi‐Codaccioni et al. 2010 , Ejrnæs et al. 2018 ), may give more nuanced and insightful results. But, these often require large datasets and/or complicated calculations, which few practitioners have the time for or expertise to use. Beyond unwieldy calculations and data, some of these can also seem overly derived, artificial, or synthetic when applied to characterizing a natural area.

A fundamental job of conservation practitioners and land managers is to quickly assess the relative biological value of land parcels and their species. They must evaluate the naturalness, ecological integrity, and conservation value of so much land, so frequently, that assessments can become rote—seemingly occurring unconsciously. However, the outcome of this initial site assessment, often the first step of conservation work, is critical as it dictates which lands are prioritized (e.g., for purchase, protection, restoration), and it identifies target communities for restoration. Repeatable, quantitative, measures—ecological indicators—are valuable to guide and standardize such work. While they are common today, the scientific development of ecological indicators has only occurred recently (Niemi and McDonald 2004 ). Therefore, the best choice for site‐ or community‐level measures is rarely clear, and weaknesses in available measures impede their use.

The relationship between area and Mean. Unlike species richness (gray line), Meanacross different sized, nested, grassland plots shows it to be largely unrelated to the size of the area sampled. Although it does become less precise in very small plots with few species (larger confidence intervals), dashed horizontal line is the 100‐mplot value for reference. ±95% CI;= 20. Figure based on Spyreas ().

When sampling an area's floristic quality, either the flora of the entire habitat patch or plots within it (site sub‐samples) can be used. If the FQI is used, the size of the sample area will influence values because species richness generally increases with the size of the sample area (Matthews et al. 2005 ). Mean C , however, is more immune to biases related to sample area, at least at plot, community, or site scales (Fig. 2 ; Spyreas 2016 ).

Species conservatism values can vary among states. Red pine ( Pinus resinosa ; left) is highly conservative in Illinois ( C = 9), but not in neighboring Michigan ( C = 3). Ebony spleenwort ( Asplenium platyneuron ; right) is highly conservative in Florida ( C = 10), but not in Illinois ( C = 4). Differences in regional ecologies are only one reason that a species’ value may differ. The individual scoring process for each state's flora affects species values. It is crucial to remember that because C values were created as relative comparisons to other species and communities in that state, they are only intended for use there.

Floristic Quality Assessment uses the plant species found in an area to measure its floristic quality. It is calculated using one or both of FQA's primary metrics—the mean coefficient of conservatism (Mean) or the Floristic Quality Index (FQI).where Meanis the average conservatism value (value) of the plant species in an area, andis the number of plant species. Floristic Quality Index is the product of Meanand the square root of the area's plant richness. These are the basic, standard FQA measures (Swink and Wilhelm). By using them, only two pieces of information are needed to calculate an area's FQA value: a plant species list and the publishedvalues for those species. Regional botanical experts pre‐assign each plant species avalue. The value ranges from 0 to 10 for each plant in a region (often a state, which is used henceforth). Species values are only calibrated for, and valid in, the state they are assigned (Fig. 1 ). Native species may be assigned any value from 0 to 10, whereas non‐native species receive zeros. Note that non‐native species may or may not be included in either equation, with similar performance either way (Spyreas et al.). Of further note is that many variant FQA measures have been created, but these show either inconsistent, or decreased performance, compared to the standard measures (reviewed in Spyreas, Kutcher and Forrester).

The second fundamental concept is an area's floristic quality (measured by Mean C or FQI). Sitewide floristic quality is traditionally used for two related purposes. The first is to ask, “How degraded by human disturbance is a site overall?” By definition, the presence/absence of disturbance‐sensitive species provides an accurate measure of the disturbance and stress an area has incurred. The second more common use for site floristic quality stems from its relatively unique ability to quantify an area's conservation value. Because pristine, native plant communities are so rare in most modern landscapes, remnant habitats that harbor conservative plant assemblages are special and valuable for conservation purposes (often along with their associated fauna, Panzer and Schwartz 1998 ). This is the basic definition of floristic quality.

What does floristic quality look like? Low floristic quality habitats may be beautiful, diverse, and valuable for ecosystem services and wildlife. However, the conservation value of their floras is not special. Two sets of habitats are pictured. The first wetland (A) had been used for row crop agriculture for 100 yr prior to being left fallow for two years before being photographed. The plants in view are not conservative and could be found in nearly any wet area in the region, disturbed or not (Mean C = 2.2). The second wetland (B) is a high‐quality remnant wetland. Plants in view are indicative of an undegraded native habitat (Mean C = 6). The first upland (C) is an abandoned field. Its plants are not conservative and could be found in any upland field in the region, ruderal or not (Mean C = 1.5). The second upland (D) is a centuries‐old assemblage of conservative tallgrass prairie plants that have never been plowed (Mean C = 6.5).

Similar species, dissimilar conservatism values. Closely related species often vary in their C values. For example, Solidago speciosa (left) and S. canadensis (right) are widespread across Illinois (dark counties map inset), and they are closely related. However, S. speciosa is restricted to relatively pristine native habitats ( C = 7), whereas S. canadensis can be found in almost any upland situation ( C = 0). Phylogeny (relatedness), rarity, showiness, and other species traits are often incorrectly associated with species conservatism.

There are two related concepts that underlie FQA and are critical to understanding it: a species’ conservatism and a site's floristic quality. Species conservatism—estimated via C values—is assigned based on a species’ fidelity to habitats that are more, or less, degraded by human disturbances (Fig. 3 ). High values are assigned to plants that are exclusive to undegraded, relictual, native habitats in the region (Fig. 4 ). Users can assume that such species are more sensitive to anthropogenic disturbances (Swink and Wilhelm 1994 ). The conservatism concept assumes that anthropogenic disturbances are generally of a type and intensity unlike that of natural disturbance regimes in their effects on native plant communities. While many other species traits or ecological characteristics—rarity, successional stage, r vs. K syndromes, niche breadth—are loosely correlated with species conservatism, none of these completely characterizes species values, and it is important to note that they do not define them (see You're doing it wrong below).

There are five primary reasons why FQA has become so popular (reviewed in Spyreas 2014 ): (1) its simplicity and ease of use. Only a plant species list and C values are needed; (2) its ability to quantify and distill years of botanical knowledge and field experience into an objectively applied number; (3) its flexibility and variety of uses; (4) its effectiveness; it accurately measures what it purports to; and (5) its uniqueness; it measures ecological information that is not captured by other ecological metrics.

North American regions withvalues assigned to their flora. For several states/regions, there are multiple versions ofvalues available (dark gray). While this may make it confusing to know which version to use, different versions result in similar relative results. Stripes indicate regions where only parts of the flora have been assigned values (e.g., wetland plants). There are ongoing efforts to assign values to the floras of the remaining sections of the western USA. Mostvalue lists can be found at the Universal Floristic Quality Assessment website, ( https://universalfqa.org/view_databases ) or by contacting the author directly.

Floristic Quality Assessment was originally developed in the late 1970s in the Chicago region (USA) to identify protection‐worthy lands with a simple, repeatable, quantitative method (Swink and Wilhelm 1979 ). It has since expanded geographically, and most of North America now has C values assigned to its flora (Fig. 5 ), as do areas of at least three other continents (Zinnen et al., unpublished manuscript ). Its literature has grown exponentially, especially over the past 20 yr (Zinnen et al., unpublished manuscript ). The breadth of application of FQA has also expanded; it is now used for basic ecological research, as well as many different types of habitat assessment. This includes habitat monitoring over time, assessing restoration success, and guiding ecological management practices and techniques. It is also a legally codified regulatory criterion used by municipal, county, state, and federal agencies (Matthews and Endress 2008 , Chu and Molano‐Flores 2013 ).

Everyone's a Critic

With FQA's popularity has come skepticism. It has been criticized as imprecise, inconsistent, biased, subjective, romantic, tautological, untested, and unsubstantiated by ecological theory. This section addresses the more common and trenchant criticisms, moving from the conceptually focused to the more methodological. A synopsis of the 10 criticisms (italics) is followed by a response.

Circularity A species’ conservatism value is based on its propensity to occur in high‐quality habitats. However, these nice habitats are identified by their floras in the first place. This tautology to C values highlights how FQA does not measure an independent ecological property; it essentially measures itself. Some circularity or non‐independence is inherent in species C values; it is also not necessarily problematic. Part of assessing the natural quality or biological degradation of a habitat is observing the community of plants in it. As long as FQA users (1) understand that to some extent, the observed species co‐occurrences in high‐quality habitats underlie how species C values were assigned, and (2) agree that the native remnant habitats identified as high quality (where conservative species co‐occur) are correctly identified as high quality; then, circularity is not problematic. Indeed, one desirable aspect of FQA is that it provides a very simple way to measure the interesting propensity for conservative plants to occur with each other, which is an ecological phenomenon that is otherwise not easily measured (Matthews et al. 2015). To the second point, few would argue with the ability of experienced botanists to identify exceptional, undegraded native habitats. But, habitat properties besides the plant community influence a botanist's identification of high‐quality natural areas. Observed disturbance legacies from pollution, farming, grazing, logging, alteration of hydrology, and other site factors factor into how expert botanists have judged habitats they have encountered over the years, and therefore, they inform species C values. Not surprisingly, studies have found that FQA measures correlate very highly with measured site disturbance variables and indices (Fig. 6). Indeed, these measured anthropogenic disturbance levels could be seen as the independent and objective property that FQA captures. Figure 6 Open in figure viewer PowerPoint C vs. anthropogenic disturbance across 47 emergent wetlands in Illinois (r2 = 0.61). The x‐axis was created by combining dozens of human disturbance measures into a disturbance index. When compared with other measures, FQA metrics are almost always found to be the best predictor of site disturbance, typically capturing between 60% and 90% of the variation. Note that predictability declines at the low (left) and high (right) end of the spectrum, either suggesting weak Mean C performance there (the highest and lowest quality sites are difficult to predict) or failure to quantify disturbance levels there. Figure adapted from Spyreas ( 2014 How well do Floristic Quality Assessment (FQA) measures perform? Site Meanvs. anthropogenic disturbance across 47 emergent wetlands in Illinois (= 0.61). The‐axis was created by combining dozens of human disturbance measures into a disturbance index. When compared with other measures, FQA metrics are almost always found to be the best predictor of site disturbance, typically capturing between 60% and 90% of the variation. Note that predictability declines at the low (left) and high (right) end of the spectrum, either suggesting weak Meanperformance there (the highest and lowest quality sites are difficult to predict) or failure to quantify disturbance levels there. Figure adapted from Spyreas ().

It works in practice, but not in theory Floristic Quality Assessment is invalid because it is not based in, or substantiated by, ecological theory. Many authors have recently tried to move beyond the standard, basic, definition for floristic quality or species conservatism by inserting ecological theories or properties into their definitions (e.g., succession, rarity, niche breadth, specialization, Grime's CSR). Such theory‐based floristic quality definitions have proliferated, but they suffer from two problems. First, they lack empirical support or evidence, whereas the standard, non‐theoretical conceptual grounding in anthropogenic disturbance accurately predicts FQA values (Mack 2007; Fig. 6). The second problem with new FQA explanations is that they are incomplete. Their theories do not fit, or explain, every species’ C value. (For example, some high C value species in the flora will have a wide niche breadth or they will be early‐successional. But, such inconsistent species are never explained.) To bolster the weakness of any one theory, some authors have incorporated multiple theories into new FQA definitions, where FQA is attributable to some indeterminate combination of them. Some have even used different theories for the different integer levels. For example, species with values from 0 to 3 relate to rarity, whereas values from 8 to 10 are explained by niche breadth, but not rarity. These frameworks have never explained why one theory would relate to some C values and species behaviors, but not others. Alternatively, within the standard definition framework, every C value, for every species, can be explained by remnant habitat dependence and disturbance tolerance. Unfortunately, once published, new definitions have proliferated. This has muddled the literature and confused FQA users. There is little doubt that existing ecological theories, concepts, and properties will eventually prove to useful for expanding our understanding of why species are more, or less, conservative (Bauer et al. 2018), but their ability to define FQA is currently speculative and incomplete. One hundred and fifty years of the study of ecology shows that no theory will explain the behavior of thousands of plant species across untold habitat types (Lawton 1999). Whether FQA's originators were heeding this lesson, or whether it was because FQA was foremost intended to be practical, they explicitly avoided associating it with existing ecological theory. Floristic quality “… is clearly a human concept and not a true ecosystem property” (Bourdaghs et al. 2006), albeit one that characterizes the ecology and behavior of species occurrences in natural vs. anthropogenic habitats very well. Thus, the simple standard conceptual framework remains the soundest approach to using and understanding it.

Subjectivity Plant species C values are subjectively assigned, yet FQA is presented as an objective measure. It is completely opinion‐based and it is therefore not scientific. C values are subjectively assigned. However, they are based on botanists’ experience with where plants occur and what types of habitats they occur in, which is accumulated over decades of study. Subjectivity in any professional judgment is a problem when it leads to inaccuracy or bias. Biases exist in even the best‐trained professionals. However, bias has yet to be shown for FQA (Matthews et al. 2015). Inaccuracies (or biases) that do occur in C values can be mitigated at two levels. First, during the original C value assignment process other botanists on the panel moderate any one person's biases. Second, the collective species values at a site are aggregated by FQA metrics, which moderates the bias contained in any individual species value (Matthews 2003).

The bias of rarity Rare species in particular are biased toward higher C value assignments. The earliest versions of FQA actually did incorporate rarity directly into species C values, where designated threatened or endangered species automatically received the highest values (Swink and Wilhelm 1979, Wilhelm and Ladd 1988, Ladd 1993). However, those values were subsequently replaced and modern evaluations consciously eschew considerations of rarity. It is true, however, that C values and species rarity are highly correlated (Fig. 7). In modern highly disturbed landscapes, this general trend is to be expected, but there are also many examples of non‐conservative species that are quite rare, and vice versa. Indeed, conservative species can either be common, naturally rare (e.g., edge of their range), or rare because their habitat has been destroyed. Beyond individual species, some have suggested an FQA bias toward particular habitat types, especially rarer habitats. For example, pristine fens, a naturally rare wetland type, may achieve higher FQA scores than pristine marshes, a rather common wetland community (Bried et al. 2013). Assumedly this happens because the C values of species found in these unique communities were overvalued by scorer bias. However, it is also quite possible that these habitat types simply have different relative disturbance levels (e.g., different disturbance legacies that are difficult to detect). Future tests will need to determine whether certain vegetation types, or even certain regions within states, have higher (biased) floristic quality scores because of higher C values assigned to their species, or whether these habitats are simply less degraded. Figure 7 Open in figure viewer PowerPoint C values. The relationship between C values and the number of Illinois counties in which a species occurs (natives only). How widely a species is distributed is highly correlated with conservatism. P < 0.0001, r adj 2 = 0.50, ±95% CI, N = 2090. Figure adapted from Spyreas ( 2014 Rarity andvalues. The relationship betweenvalues and the number of Illinois counties in which a species occurs (natives only). How widely a species is distributed is highly correlated with conservatism.0.0001,= 0.50, ±95% CI,= 2090. Figure adapted from Spyreas ().

Imprecision Species C values and site FQA scores are imprecise. Imprecision is inherent in any measure. However, for such a broad, highly encompassing ecological measure, FQA has shown to be accurate and precise enough for its users. Tests of several hundred individual species values have shown high precision in them (Matthews et al. 2015, Bried et al. 2018). Furthermore, any imprecision in species‐level values is dampened by FQA metrics that average all the plants at a site, because the “… overlap of ecological tolerances of multiple species is smaller than the amplitude of a single one” (LaPaix et al. 2009). There is some evidence that FQA metrics are less able to discern floristic quality differences at the highest and lowest ranges—that is, small difference among the highest conservation value, least biologically degraded sites (Bowles and Jones 2006; Fig. 6). Therefore, while FQA has frequently been shown to be an accurate measure, its predictive ability and precision at the extremes requires further study.

Species abundance There is no accommodation in FQA for how abundant or frequent species are in habitats. This has the effect of over‐weighing the importance of rare species at sites. It also ignores that relative abundances within communities can by themselves reflect a sites conservation value and disturbance level (Nielsen et al. 2007). There are several reasons why FQA's original authors created a measure that could be used irrespective of relative plant abundances or frequencies. The primary one is that the abundance of any given species was considered “… irrelevant or only ambiguously related to” a habitat's floristic quality (Swink and Wilhelm 1994). Second, potentially over‐emphasizing diminutive or less‐common species could be as problematic as over‐emphasizing large or frequent species. Third, presuming to know the ideal evenness or relative species abundances in natural areas is untenable. Finally, the botanists who initially assign C values assumedly do not incorporate community abundance into their valuations. The relative abundance of species at a site is clearly important for both ecological and conservation matters. However, the original authors of FQA suspected that abundance was largely redundant information within FQA metrics. It was their observation that when “a few plants are inordinately abundant in a community, the mean floristic quality is also depressed” (Wilhelm and Ladd 1988). Nonetheless, subsequent authors have created several new FQA calculations that incorporate abundance. However, because few of these calculations have even marginally improved upon the performance of simpler standard metrics when tested, the original author's intuition about redundancy has proven correct (Francis et al. 2000, Cohen et al. 2004, Bourdaghs et al. 2006, Hopple and Craft 2013). While there is little harm in incorporating abundance, users should consider qualifications. First, abundance is rather susceptible to the natural variation that occurs in species by season, year, or with natural disturbance. This would make abundance‐weighted scores less comparable across time or across different sites sampled in different years (Ervin et al. 2006). Second, collecting and calculating abundance values takes more time and effort, all without consistent evidence for improved performance.

Overreliance This single measure is both too influential (e.g., dictating regulatory and land conservation decisions), and incomplete (e.g., does not consider faunas). Floristic Quality Assessment is inadequate for assessing habitats on its own (Taft et al. 1997). This criticism is justifiable. Distilling the complexity and value of natural systems into a single ecological measure is not ideal, especially considering the breadth of uses and consequences to those uses that FQA has come to assume. There are many reasons that FQA should not be used as a stand‐alone measure to evaluate areas. For example, it cannot account for wildlife, ecosystem function, rare plants, or population sizes in an area. Furthermore, users now assume a level of exactness and certainty to scores that is not justified. For example, laws frequently mandate exact score thresholds (e.g., FQI = 20; Streever 1999, Herman et al. 2001, Matthews and Endress 2008), which implies a precision that ignores, among other things, that there is normal variability (intra‐ or inter‐annual) in natural areas. Nevertheless, the charge of overreliance is a critique of how FQA is interpreted and used, rather than what it measures or how it performs.

Restorations and manipulability (or, how to cheat using FQA) FQA numbers are vulnerable to manipulation. As the legal requirements and financial incentives for achieving high FQA scores have increased, so have the incentives for abuse. For example, consider a planted restoration that is required to reach or maintain a target FQA score to trigger payment for restoration work. Planting a few high C value species/individuals that are cheap/easy to cultivate and establish at the last minute, artificially inflates the score of an otherwise sub‐par restoration. Such quick‐fix species may be used regardless of whether they are ecologically appropriate for the region, site, or whether they are likely to survive over the long term. Floristic Quality Assessment's overemphasis of high C value species leaves it more prone to manipulation than other ecological metrics. A fundamental downside to any numerical indicator is their tendency to incentivize gaming the system (Muller 2018). Shortcuts to compliance, or even cheating, are a concern for FQA and habitat restoration (DeBerry and Perry 2015), and anecdotal accounts of it are common. But, it can be difficult to prove such malfeasance. Steps to prevent this could include (1) critical examination of site lists for inappropriate species, and (2) requiring long‐term monitoring to assure sustained site scores (Matthews and Spyreas 2010). The possibility of manipulating scores warrants attention, but again, it is mainly a function of how FQA is used rather than a criticism of whether it works as intended. Regarding FQA's use, it is worth noting here that it has never been clear how planted restorations fit within FQA's original conception (e.g., species are scored with regard to occurrence in remnant habitats not modern restorations). Therefore, it can be unclear how scores from novel, created, restored, or de novo habitat should be interpreted. Nonetheless, because they are now widely used to evaluate these habitats, the utility and meaning of FQA metrics need study and consideration regarding them.

When (and where) to draw the line There are no guidelines for what constitutes the area to sample for FQA. The site is offered as the operant term, but sites are not ecological entities. Determining the appropriate sample unit in any ecological inquiry is often not an obvious or trivial matter. And, FQA has its own specific set of sampling considerations. Chief among these is the question of where to draw the sample area boundary. (This is not to be confused with the choice of creating the species list from site sub‐sampling, i.e., plots, or sampling the full habitat.) When the area of interest is small and has well‐defined boundaries such as property lines, rivers, and roads, the sample area is obvious; it is usually the whole habitat patch (Taft et al. 1997). The species from two or more discrete habitat fragments like this should not be combined in a single site score calculation. On the other hand, determining the boundary within a large, contiguous habitat is more complicated. Should users confine sampling to a single plant community type (e.g., relevé), or physiographic unit (e.g., hydrology, soil type, slope, aspect)? Should users include the species from ecotones (habitat edges or boundaries) in the species list? Few have weighed in on these questions. Floristic Quality Assessment's originators did not warn against crossing vegetation types when compiling a species list, and they suggested including ecotones in the sample area (Swink and Wilhelm 1994). This liberal perspective probably arose because FQA was originally used to assess the value of entire land parcels. Since it has expanded in scope and use, stricter or clearer conventions are needed going forward. When using the FQI metric specifically, users must consider factors that affect richness if they break up habitats into sample units. This includes the size of the area, environmental gradients, ecotones, or any other contributors to α‐ or β‐diversity within or across the units. Discerning sample boundaries is further complicated by suggestions that different types of vegetation (e.g., forest vs. prairie) may inherently differ in their FQA scoring potential (see The bias of rarity). One rule that can universally guide where to delineate sample units is to consider whether they encompass a single land‐use history or disturbance level. A final consideration is what should limit the size of sites or sample areas? At the largest scale, FQA scores generated from an entire state's flora are not useful because state scores are not comparable with one another. But, the floristic quality of county‐level floras has successfully been compared over time (Leitner et al. 2008). When comparing habitats, the practicality of surveying increasingly large areas will limit site size. At smaller scales, the minimum size of effective sample plots can be quite small, although usefulness quickly declines at plot sizes of 0.1 m2 or less, because having so few species in plots greatly inflates the variance (Fig. 2). Swink and Wilhelm (1994) advise that Mean C is a meaningful measure from areas 0.25 m2 up to 100 acres or more, which is a reasonable rule of thumb.