Species–area relationship

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https://en.wikipedia.org/wiki/Species%E2%80%93area_relationship 

 

The species-area relationship for a contiguous habitat

The species-area relationship or species-area curve describes the relationship between the area of a habitat, or of part of a habitat, and the number of species found within that area. Larger areas tend to contain larger numbers of species, and empirically, the relative numbers seem to follow systematic mathematical relationships. The species-area relationship is usually constructed for a single type of organism, such as all vascular plants or all species of a specific trophic level within a particular site. It is rarely if ever, constructed for all types of organisms if simply because of the prodigious data requirements. It is related but not identical to the species discovery curve.

Ecologists have proposed a wide range of factors determining the slope and elevation of the species-area relationship. These factors include the relative balance between immigration and extinction, rate and magnitude of disturbance on small vs. large areas, predator-prey dynamics, and clustering of individuals of the same species as a result of dispersal limitation or habitat heterogeneity. The species-area relationship has been reputed to follow from the 2nd law of thermodynamics. In contrast to these "mechanistic" explanations, others assert the need to test whether the pattern is simply the result of a random sampling process.

Authors have classified the species-area relationship according to the type of habitats being sampled and the census design used. Frank W. Preston, an early investigator of the theory of the species-area relationship, divided it into two types: samples (a census of a contiguous habitat that grows in the census area, also called "mainland" species-area relationships), and isolates (a census of discontiguous habitats, such as islands, also called "island" species-area relationships). Michael Rosenzweig also notes that species-area relationships for very large areas—those collecting different biogeographic provinces or continents—behave differently from species-area relationships from islands or smaller contiguous areas. It has been presumed that "island"-like species-area relationships have higher slopes (in log–log space) than "mainland" relationships, but a 2006 metaanalysis of almost 700 species-area relationships found the former had lower slopes than the latter.

Regardless of census design and habitat type, species-area relationships are often fitted with a simple function. Frank Preston advocated the power function based on his investigation of the lognormal species-abundance distribution. If ${\displaystyle S}$ is the number of species, ${\displaystyle A}$ is the habitat area, and ${\displaystyle z}$ is the slope of the species area relationship in log-log space, then the power function species-area relationship goes as:

${\displaystyle S=cA^{z}}$

Here ${\displaystyle c}$ is a constant which depends on the unit used for area measurement, and equals the number of species that would exist if the habitat area was confined to one square unit. The graph looks like a straight line on log–log axes, and can be linearized as:

${\displaystyle \log(S)=\log(cA^{z})=\log(c)+z\log(A)}$

In contrast, Henry Gleason championed the semilog model:

${\displaystyle S=\log(cA^{z})=\log(c)+z\log(A)}$

which looks like a straight line on semilog axes, where the area is logged and the number of species is arithmetic. In either case, the species-area relationship is almost always decelerating (has a negative second derivative) when plotted arithmetically.

species-area relationships are often graphed for islands (or habitats that are otherwise isolated from one another, such as woodlots in an agricultural landscape) of different sizes. Although larger islands tend to have more species, a smaller island may have more than a larger one. In contrast, species-area relationships for contiguous habitats will always rise as areas increases, provided that the sample plots are nested within one another.

The species-area relationship for mainland areas (contiguous habitats) will differ according to the census design used to construct it. A common method is to use quadrats of successively larger size so that the area enclosed by each one includes the area enclosed by the smaller one (i.e. areas are nested).

In the first part of the 20th century, plant ecologists often used the species-area curve to estimate the minimum size of a quadrat necessary to adequately characterize a community. This is done by plotting the curve (usually on arithmetic axes, not log-log or semilog axes), and estimating the area after which using larger quadrats results in the addition of only a few more species. This is called the minimal area. A quadrat that encloses the minimal area is called a relevé, and using species-area curves in this way is called the relevé method. It was largely developed by the Swiss ecologist Josias Braun-Blanquet.

Estimation of the minimal area from the curve is necessarily subjective, so some authors prefer to define the minimal area as the area enclosing at least 95 percent (or some other large proportion) of the total species found. The problem with this is that the species area curve does not usually approach an asymptote, so it is not obvious what should be taken as the total. the number of species always increases with area up to the point where the area of the entire world has been accumulated.

 

Storage effect

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Storage_effect 

The storage effect is a coexistence mechanism proposed in the ecological theory of species coexistence, which tries to explain how such a wide variety of similar species are able to coexist within the same ecological community or guild. The storage effect was originally proposed in the 1980s to explain coexistence in diverse communities of coral reef fish, however it has since been generalized to cover a variety of ecological communities. The theory proposes one way for multiple species to coexist: in a changing environment, no species can be the best under all conditions. Instead, each species must have a unique response to varying environmental conditions, and a way of buffering against the effects of bad years. The storage effect gets its name because each population "stores" the gains in good years or microhabitats (patches) to help it survive population losses in bad years or patches. One strength of this theory is that, unlike most coexistence mechanisms, the storage effect can be measured and quantified, with units of per-capita growth rate (offspring per adult per generation).

The storage effect can be caused by both temporal and spatial variation. The temporal storage effect (often referred to as simply "the storage effect") occurs when species benefit from changes in year-to-year environmental patterns, while the spatial storage effect occurs when species benefit from variation in microhabitats across a landscape.

The concept

For the storage effect to operate, it requires variation (i.e. fluctuations) in the environment and thus can be termed a "fluctuation-dependent mechanism". This variation can come from a large degree of factors, including resource availability, temperature, and predation levels. However, for the storage effect to function, this variation must change the birth, survival, or recruitment rate of species from year to year (or patch to patch).

For competing species within the same community to coexist, they have to meet one fundamental requirement: the impact of competition from a species on itself must exceed its competitive impact on other species. In other words, intraspecific competition must exceed interspecific competition. For example, jackrabbits living in the same area compete for food and nesting grounds. Such competition within the same species is called intraspecific competition, which limits the growth of the species itself. Members from different species can also compete. For instance, jackrabbits and cottontail rabbits also compete for food and nesting grounds. Competition between different species is called interspecific competition, which limits the growth of other species. Stable coexistence occurs when any one species in the community limits its own growth more strongly than the growth of others.

The storage effect mixes three essential ingredients to assemble a community of competing species that fulfill the requirement. They are 1) correlation between the quality of an environment and the amount of competition experienced by a population in that environment (i.e. covariance between environment and competition), 2) differences in species response to the same environment (i.e. species-specific environmental responses), and 3) the ability of a population to diminish the impact of competition under worsening environment (i.e. buffered population growth). Each ingredient is described in detail below with an explanation why the combination of the three leads to species coexistence.

Covariance between environment and competition

The growth of a population can be strongly influenced by the environment it experiences. An environment consists of not only physical elements such as resource abundance, temperature, and level of physical disturbance, but also biological elements such as the abundance of natural enemies and mutualists. Usually organisms reproduce more in a favorable environment (i.e. either during a good year, or within a good patch), build up their population densities, and lead themselves to a high level of competition due to this increasing crowding. Such a trend means that higher quality environments usually correlate with a higher strength of competition experienced by the organisms in those environments. In short, a better environment results in stronger competition. In statistics, such correlation means that there will be a non-zero covariance between the change of population density in response to the environment and that to the competition. That is why the first ingredient is called "covariance between environment and competition".

Species-specific environmental responses

Covariance between environment and competition suggests that organisms experience the strongest competition under their optimal environmental conditions because their populations grow most rapidly in those conditions. In nature, we often find that different species from the same community respond to the same conditions in distinctive manners. For example, plant species have different preferred levels of light and water availability, which affect their germination and physical growth rates. Such differences in their response to the environment, which is called "species-specific environmental response," means no two species from a community will have the same best environment in a given year or a given patch. As a result, when a species is under its optimal environmental conditions and thus experiencing the strongest intraspecific competition, other species from the same community only experience the strongest interspecific competition coming from that species, but not the strongest intraspecific competition coming from themselves.

Buffered population growth

A population can decline when environmental conditions worsen and when competition intensifies. If a species cannot limit the impact of competition in a hostile environment, its population will crash, and it will become locally extinct. Marvelously, in nature organisms are often able to slow down the rate of population decline in a hostile environment by alleviating the impact of competition. In so doing, they are able to set up a lower limit on the rate of their population decline. This phenomenon is called "buffered population growth", which occurs under a variety of situations. Under the temporal storage effect, it can be accomplished by the adults of a species having long life spans, which are relatively unaffected by environmental stressors. For example, an adult tree is unlikely to be killed by a few weeks of drought or a single night of freezing temperatures, whereas a seedling may not survive these conditions. Even if all seedlings are killed by bad environmental conditions, the long-lived adults are able to keep the overall population from crashing. Moreover, the adults usually adopt strategies such as dormancy or hibernation under a hostile environment, which make them less sensitive to competition, and allows them to buffer against the double blades of the hostile environment and competition from their rivals. For a different example, buffered population growth is attained by annual plants with a persistent seed bank. Thanks to these long-lived seeds, the entire population cannot be destroyed by a single bad year. Moreover, the seeds stay dormant under unfavorable environmental conditions, avoiding direct competition with rivals who are favored by the same environment, and thus diminish the impact of competition in bad years. There are some temporal situations in which buffered population growth is not expected to occur. Namely, when multiple generations do not overlap (such as Labord's chameleon) or when adults have a high mortality rate (such as many aquatic insects, or some populations of the Eastern Fence Lizard), buffered growth does not occur. Under the spatial storage effect, buffered population growth is generally automatic, because the effects of a detrimental microhabitat will only be experienced by individuals in that area, rather than the population as a whole.

Outcome

The combined effect of (1) covariance between environment and competition, and (2) species-specific response to the environment decouple the strongest intraspecific and interspecific competition experienced by a species. Intraspecific competition is strongest when a species is favored by the environment, whereas interspecific competition is strongest when its rivals are favored. After this decoupling, buffered population growth limits the impact of interspecific competition when a species is not favored by the environment. As a consequence, the impact of intraspecific competition on the species favored by a particular environment exceeds that of the interspecific competition on species less favored by that environment. We see that the fundamental requirement for species coexistence is fulfilled and thus storage effect is able to maintain stable coexistence in a community of competing species.

For species to coexist in a community, all species must be able to recover from low density. Not surprisingly, being a coexistence mechanism, the storage effect helps species when they become rare. It does so by making the abundant species’ effect on itself greater than its effect on the rare species. The difference between species’ response to environmental conditions means that a rare species’ optimal environment is not the same as its competitors. Under these conditions, the rare species will experience low levels of interspecific competition. Because the rare species itself is rare, it will experience little impact from intraspecific competition as well, even at its highest possible levels of intraspecific competition. Free from the impact of competition, the rare species is able to make gains in these good years or patches. Moreover, thanks to the buffered population growth, the rare species is able to survive the bad years or patches by "storing" the gains from the good years/patches. As a result, the population of any rare species is able to grow due to the storage effect.

One natural outcome from the covariance between environment and competition is that the species with very low densities will have more fluctuation in its recruitment rates than species with normal densities. This occurs because in good environments, species with high densities will often experience large amount of crowding by members of the same species, thus limiting the benefits of good years/patches, and making good years/patches more similar to bad years/patches. Low-density species are rarely able to cause crowding, thus allowing significantly increased fitness in good years/patches. Since the fluctuation in recruitment rate is an indicator of covariance between environment and competition, and since species-specific environmental response and buffered population growth can normally be assumed in nature, finding much stronger fluctuation in recruitment rates in rare and low-density species provides a strong indication that the storage effect is operating within a community.

Mathematical formulation

It is important to note that the storage effect is not a model for population growth (such as the Lotka–Volterra equation) itself, but is an effect that appears in non-additive models of population growth. Thus, the equations shown below will work for any arbitrary model of population growth, but will only be as accurate as the original model. The derivation below is taken from Chesson 1994. It is a derivation of the temporal storage effect, but is very similar to the spatial storage effect.

The fitness of an individual, as well as expected growth rate, can be measured in terms of the average number of offspring it will leave during its lifetime. This parameter, r(t), is a function of both environmental factors, e(t), and how much the organism must compete with other individuals (both of its own species, and different species), c(t). Thus,

${\displaystyle r(t)=g(e(t),c(t))\,}$

where g is an arbitrary function for growth rate. Throughout the article, subscripts are occasionally used to represent functions of a particular species (e.g. r _j(t) is the fitness of species j). It is assumed that there must be some values e* and c*, such that g(e*, c*) = 0, representing a zero-population growth equilibrium. These values need not be unique, but for every e*, there is a unique c*. For ease of calculation, standard parameters E(t) and C(t) are defined, such that

${\displaystyle E(t)=g(e(t),c^{*})\,}$

${\displaystyle C(t)=-g(e^{*},c(t))\,}$

Both E and C represent the effect of deviations in environmental response from equilibrium. E represents the effect that varying environmental conditions (e.g. rainfall patterns, temperature, food availability, etc.) have on fitness, in the absence of abnormal competitive effects. For the storage effect to occur, the environmental response for each species must be unique (i.e. E _j(t) ≠ E _i(t) when j ≠ i). C(t) represents how much average fitness is lowered as a result of competition. For example, if there is more rain during a given year, E(t) will likely increase. If more plants begin to bloom, and thus compete for that rain, then C(t) will increase as well. Because e* and c* are not unique, E(t) and C(t) are not unique, and thus one should choose them as conveniently as possible. Under most conditions (see Chesson 1994), r(t) can be approximated as

${\displaystyle r(t)=E(t)-C(t)+\gamma E(t)C(t)\,}$

where

${\displaystyle \gamma ={\frac {\partial ^{2}r}{\partial E\,\partial C}}}$

γ represents the nonadditivity of growth rates. If γ = 0 (known as additivity) it means that the impact of competition on fitness does not change with the environment. If γ > 0 (superadditivity), it means that the adverse effects of competition during a bad year are relatively worse than during a good year. In other words, a population suffers more from competition in bad years than in good years. If γ < 0 (subadditivity, or buffered population growth), it means that the harm done by competition during a bad year is relatively minor when compared to a good year. In other words, the population is able to diminish the impact of competition as the environment worsens. As stated above, for the storage effect to contribute to species coexistence, we must have buffered population growth (i.e. it must be the case that γ < 0).

The long-term average of the above equation is

${\displaystyle {\bar {r}}={\bar {E}}-{\bar {C}}+\gamma ({\bar {E}}{\bar {C}}+{\text{Cov}}(E(t),C(t)))\,}$

which, under environments with sufficient variation relative to mean effects, can be approximated as

${\displaystyle {\bar {r}}\approx {\bar {E}}-{\bar {C}}+\gamma {\text{Cov}}(E(t),C(t))}$

For any effect to act as a coexistence mechanism, it must boost the average fitness of an individual when they are at below-normal population density. Otherwise, a species at low density (known as an `invader') will continue to dwindle, and this negative feedback will cause its extinction. When a species is at equilibrium (known as a `resident'), its average long-term fitness must be 0. For a species to recover from low density, its average fitness must be greater than 0. For the remainder of the text, we refer to functions of the invader with the subscript i, and to the resident with the subscript r.

A long-term average growth rate of an invader is often written as

${\displaystyle {\bar {r_{i}}}=\Delta E-\Delta C+\Delta I\,}$

where,

${\displaystyle \Delta E={\bar {E_{i}}}-\sum _{i\neq r}q_{ir}{\bar {E_{r}}}\,}$

${\displaystyle \Delta C={\bar {C_{i}}}-\sum _{i\neq r}q_{ir}{\bar {C_{r}}}\,}$

and, ΔI, the storage effect,

${\displaystyle \Delta I=\gamma _{i}{\text{Cov}}(E_{i},C_{i})-\sum _{i\neq r}q_{ir}\gamma _{r}{\text{Cov}}(E_{r},C_{r})\,}$

where

${\displaystyle q_{ir}={\frac {\partial C_{i}}{\partial C_{r}}}}$

In this equation, q_ir tells us how much the competition experienced by r affects the competition experienced by i.

The biological meaning of the storage effect is expressed in the mathematical form of ΔI. The first term of the expression is covariance between environment and competition (Cov(E C)), scaled by a factor representing buffered population growth (γ). The difference between the first term and the second term represents the difference in species responses to the environment between the invader and the sum of the residents, scaled by the effect each resident has on the invader (q_ir).

Predation

Recent work has extended what is known about the storage effect to include apparent competition (i.e., competition mediated through a shared predator).

These models showed that generalist predators can undermine the benefits of the storage effect that from competition. This occurs because generalist predators depress population levels by eating individuals. When this happens, there are fewer individuals competing for resources. As a result, relatively abundant species are less constrained by competition for resource in favorable years (i.e., the covariance between environment and competition is weakened), and therefore the storage effect from competition is weakened. This conclusion follows the general trend that the introduction of a generalist predator will often weaken other competition-based coexistence mechanisms, and which result in competitive exclusion.

Additionally, certain types of predators can produce a storage effect from predation. This effect has been shown for frequency-dependent predators, who are more likely to attack prey that are abundant, and for generalist pathogens, who cause outbreaks when prey are abundant. When prey species are especially numerous and active, frequency-dependent predators become more active, and pathogens outbreaks become more severe (i.e., there was a positive covariance between the environment and predation, analogous to the covariance between the environment and competition). As a result, abundant species are limited during their best years by high predation – an effect that is analogous to the storage effect from competition.

Empirical studies

The first empirical study that tested the requirements of the storage effect was done by Pake and Venable, who looked at three desert annual plants. They experimentally manipulated density and water availability over a two-year period, and found that fitness and germination rates varied greatly from year to year, and over different environmental conditions. This shows that each species has a unique environmental response, and implied that likely there is a covariance between environment and competition. This, combined with the buffered population growth that is a product of a long-lived seed bank, showed that a temporal storage effect was probably an important factor in mediating coexistence. This study was also important, because it showed that variation in germination conditions could be a major factor promoting species coexistence.

The first attempt made at quantifying the temporal storage effect was by Cáceres in 1997. Using 30 years of water-column data from Oneida Lake, New York, she studied the effect the storage effect had on two species of plankton (Daphnia galeata mendotae and D. pulicaria). These species of plankton lay diapausing eggs which, much like the seeds of annual plants, lay dormant in the sediment for many years before hatching. Cáceres found that the size of reproductive bouts were fairly uncorrelated between the two species. She also found, in the absence of the storage effect, D. galeata mendotae would have gone extinct. She was unable to measure certain important parameters (such as the rate of egg predation), but found that her results were robust to a wide range of estimates.

The first test of the spatial storage effect was done by Sears and Chesson in the desert area east of Portal, Arizona. Using a common neighbor-removal experiment, they examined whether coexistence between two annual plants, Erodium cicutarium and Phacelia popeii, was due to the spatial storage effect or resource partitioning. The storage effect was quantified in terms of number of inflorescences (a proxy for fitness) instead of actual population growth rate. They found that E. cicutarium was able to outcompete P. popeii in many situations, and in the absence of the storage effect, would likely competitively exclude P. popeii. However, they found a very strong difference in the covariance between environment and competition, which showed that some of the most favorable areas for P. popeii (the rare species), were unfavorable to E. cicutarium (the common species). This suggests that P. popeii is able to avoid strong interspecific competition in some good patches, and that this may be enough to compensate for losses in areas favorable to E. cicutarium.

Colleen Kelly and colleagues have used congeneric species pairs to examine storage dynamics where species similarity is a natural outcome of relatedness and not dependent on researcher-based estimates. Initial studies were of 12 species of trees coexisting in a tropical deciduous forest at the Chamela Biological Station in Jalisco, Mexico. For each of the 12 species they examined age structure (calculated from size and species-specific growth rate), and found that recruitment of young trees varies from year to year. Grouping the species into 6 congeneric pairs, the locally rarer species of each pair unanimously had a more irregular age distributions than the more common species. This finding strongly suggests that between closely competing tree species, the rarer species experiences stronger recruitment fluctuation than the commoner species. Such difference in recruitment fluctuation, combined with evidence of greater competitive ability in the rarer species of each pair, indicates a difference in covariance between the environment and competition between rare and common species. Since species-specific environmental response and buffered population growth can be naturally assumed, their finding strongly suggests that the storage effect operates in this tropical deciduous forest so as to maintain the coexistence between different tree species. Further work with these species has shown that the storage dynamic is a pairwise, competitive relationship, between congeneric species pairs, and possibly extending as successively nested pairs within a genus.

Angert and colleagues demonstrated the temporal storage effect occurring in the desert annual plant community on Tumamoc Hill, Arizona. Previous studies had shown the annual plants in that community exhibited a trade-off between growth rate (a proxy for competitive ability) and water use efficiency (a proxy for drought tolerance). As a result, some plants grew better during wet years, while others grew better during dry years. This, combined with variation in germination rates, produced an overall community average storage effect of 0.103. In other words, the storage effect is expected to help the population of any species at low density to increase, on average, by 10.3% each generation, until it recovers from low density.

Species richness

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Species_richness 

 

Global mammal richness (2015)

Global amphibian richness (2015)

Species richness is the number of different species represented in an ecological community, landscape or region. Species richness is simply a count of species, and it does not take into account the abundances of the species or their relative abundance distributions. Species richness is sometimes considered synonymous with species diversity, but the formal metric species diversity takes into account both species richness and species evenness.

Sampling considerations

Depending on the purposes of quantifying species richness, the individuals can be selected in different ways. They can be, for example, trees found in an inventory plot, birds observed from a monitoring point, or beetles collected in a pitfall trap. Once the set of individuals has been defined, its species richness can be exactly quantified, provided the species-level taxonomy of the organisms of interest is well enough known. Applying different species delimitations will lead to different species richness values for the same set of individuals.

In practice, people are usually interested in the species richness of areas so large that not all individuals in them can be observed and identified to species. Then applying different sampling methods will lead to different sets of individuals being observed for the same area of interest, and the species richness of each set may be different. When a new individual is added to a set, it may introduce a species that was not yet represented in the set, and thereby increase the species richness of the set. For this reason, sets with many individuals can be expected to contain more species than sets with fewer individuals.

If species richness of the obtained sample is taken to represent species richness of the underlying habitat or other larger unit, values are only comparable if sampling efforts are standardised in an appropriate way. Resampling methods can be used to bring samples of different sizes to a common footing. Properties of the sample, especially the number of species only represented by one or a few individuals, can be used to help estimating the species richness in the population from which the sample was drawn.

Trends in species richness

The observed species richness is affected not only by the number of individuals but also by the heterogeneity of the sample. If individuals are drawn from different environmental conditions (or different habitats), the species richness of the resulting set can be expected to be higher than if all individuals are drawn from similar environments. The accumulation of new species with increasing sampling effort can be visualised with a species accumulation curve. Such curves can be constructed in different ways. Increasing the area sampled increases observed species richness both because more individuals get included in the sample and because large areas are environmentally more heterogeneous than small areas.

Many organism groups have most species in the tropics, which leads to latitudinal gradients in species richness. There has been much discussion about the relationship between productivity and species richness. Results have varied among studies, such that no global consensus on either the pattern or its possible causes has emerged.

Applications

Species richness is often used as a criterion when assessing the relative conservation values of habitats or landscapes. However, species richness is blind to the identity of the species. An area with many endemic or rare species is generally considered to have higher conservation value than another area where species richness is similar, but all the species are common and widespread.

Ecosystem management

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Ecosystem_management 

 

Prescribed burning is a technique used in ecosystem management. This indirectly benefits society via the maintenance of ecosystem services and the reduction of severe wildfires.

Ecosystem management is an approach to natural resource management that aims to ensure the long-term sustainability and persistence of an ecosystems function and services while meeting socioeconomic, political, and cultural needs. Although indigenous communities have employed sustainable ecosystem management approaches for millennia, ecosystem management emerged formally as a concept in the 1990s from a growing appreciation of the complexity of ecosystems, as well as humans' reliance and influence on natural systems (e.g., disturbance, ecological resilience).

Building upon traditional natural resource management, ecosystem management integrates ecological, socioeconomic, and institutional knowledge and priorities through diverse stakeholder participation. In contrast to command and control approaches to natural resource management, which often lead to declines in ecological resilience, ecosystem management is a holistic, adaptive method for evaluating and achieving resilience and sustainability. As such, implementation is context-dependent and may take a number of forms, including adaptive management, strategic management, and landscape-scale conservation.

Formulations

El Yunque National Forest 5 months pre-Hurricane Maria (top) and 7 months after the hurricane (bottom). Although ecosystem management goals often differ on their specifics, achieving resilience to disturbance is a common aim.

The term “ecosystem management” was formalized in 1992 by F. Dale Robertson, the then Chief of the U.S. Forest Service. Robertson stated, “By ecosystem management, we mean an ecological approach… [that] must blend the needs of people and environmental values in such a way that the National Forests and Grasslands represent diverse, healthy, productive and sustainable ecosystems.” A variety of additional definitions of ecosystem management exist, although definitions of this concept are typically vague. For example, Robert T. Lackey emphasizes that ecosystem management is informed by ecological and social factors, motivated by societal benefits, and implemented over a specific timeframe and area. F. Stuart Chapin and co-authors highlight that ecosystem management is guided by ecological science to ensure the long-term sustainability of ecosystem services, while Norman Christensen and coauthors underscore that it is motivated by defined goals, employs adaptive practices, and accounts for the complexities of ecological systems. Peter Brussard and colleagues suggest ecosystem management balances preserving ecosystem health while sustaining human needs.

As a concept of natural resource management, ecosystem management remains both ambiguous and controversial, in part because some of its formulations rest on contested policy and scientific assertions. These assertions are important to understanding much of the conflict surrounding ecosystem management. Professional natural resource managers, typically operating from within government bureaucracies and professional organizations, often mask debate over controversial assertions by depicting ecosystem management as an evolution of past management approaches.

Principles of ecosystem management

A fundamental principle of ecosystem management is the long-term sustainability of the production of goods and services by ecosystems, as "intergenerational sustainability [is] a precondition for management, not an afterthought". Ideally, there should be clear, publicly-stated goals with respect to future trajectories and behaviors of the system being managed. Other important requirements include a sound ecological understanding of the system, including connectedness, ecological dynamics, and the context in which the system is embedded. An understanding of the role of humans as components of the ecosystems and the use of adaptive management is also important. While ecosystem management can be used as part of a plan for wilderness conservation, it can also be used in intensively managed ecosystems (e.g., agroecosystem and close to nature forestry).

Core principles and common themes of ecosystem management:

1. Systems thinking: Management has a holistic perspective, instead of focusing on a particular level of biological hierarchy in an ecosystem (e.g., only conserving a specific species; only preserving ecosystem functioning).
2. Ecological boundaries: Ecological boundaries are clearly and formally defined, and management is place-based and may require working across political or administrative boundaries.
3. Ecological integrity: Management is focused on maintaining or reintroducing native biological diversity, and on preserving natural disturbance regimes and other key processes that sustain resilience.
4. Data collection: Broad ecological research and data collection is needed to inform effective management (e.g., species diversity, habitat types, disturbance regimes, etc.).
5. Monitoring: The impacts of management methods are tracked, allowing for their outcomes to be evaluated and modified, if needed.
6. Adaptive management: Management is an iterative process in which methods are continuously reevaluated as new scientific knowledge is gained.
7. Interagency cooperation: As ecological boundaries often cross administrative boundaries, management requires cooperation among a range of agencies and private stakeholders.
8. Organizational change: Successful implementation of management requires shifts in the structure and operation of land management agencies.
9. Humans and nature: Nature and people are intrinsically linked, and humans shape, and are shaped by, ecological processes.
10. Values: Humans play a key role in guiding management goals, which reflect a stage in the continuing evolution of social values and priorities.

History

Sustainable harvest of glaucous-winged gull eggs maintains the species' population size, while preserving traditional Huna Tlingit customs.

Pre-industrialization

Sustainable ecosystem management approaches have been used by societies throughout human history. Prior to colonization, Indigenous cultures often sustainably managed their natural resources through intergenerational traditional ecological knowledge (TEK). In TEK, cultures acquire knowledge of their environment over time and this information is passed on to future generations through cultural customs, including folklore, religion, and taboos. Traditional management strategies vary by region, and examples include the burning of the longleaf pine ecosystem by Native Americans in what is today the southeastern United States; the ban of seabird guano harvest during the breeding season by the Inca; the sustainable harvest practices of glaucous-winged gull eggs by the Huna Tlingit; and the Maya milpa intercropping approach, which is still used today.

Post-industrialization

In industrialized Western society, ecosystems have been managed primarily to maximize yields of a particular natural resource. This method to managing ecosystems can be seen by the U.S. Forest Service's shift away from sustaining ecosystem health and toward maximizing timber production to support residential development following World War II. Further, underlying traditional natural resource management is the view that each ecosystem has a single equilibrium and minimizing variation around this equilibrium results in more dependable, greater yields of natural resources. For example, this perspective informed the long-held belief in forest fire suppression in the United States, which has driven a decline in populations of fire-tolerant species as well as fuel buildup, leading to higher intensity fires. Additionally, traditional approaches to managing natural systems tended to be site- and species-specific, rather than considering all components of an ecosystem collectively; employ a “command and control” approach; and exclude stakeholders from management decisions.

The latter half of the 20th century saw a paradigm shift in how ecosystems were viewed, with a growing appreciation for the importance of disturbance and for the intrinsic link between natural resources and overall ecosystem health. Simultaneously, there was acknowledgement of society's resilience on ecosystem services, beyond provisioning goods, and of the inextricable role human-environment interactions play in ecosystems. In sum, ecosystems were increasingly seen as complex systems, shaped by non-linear processes, and thus, they could not be managed to achieve a single, predictable outcome. As a result of these complexities and often unforeseeable feedbacks from management strategies, DeFries and Nagendra deem ecosystem management to be a “wicked problem”. Thus, the outcome of traditional natural resource management's "evolution" over the course of the 20th century is ecosystem management, which explicitly recognizes that technical and scientific knowledge, though necessary in all approaches to natural resource management, are insufficient alone.

Stakeholders

Stakeholders are individuals or groups who are affected by or have an interest in ecosystem management decisions and actions. Stakeholders may also have power to influence the goals, policies, and outcomes of management. Ecosystem management stakeholders fall into the following groups based on their diverse concerns:

1. Stakeholders whose lives are directly tied to the ecosystem (e.g., members of local community)
2. Stakeholders who are not directly not impacted, but have an interest in the ecosystem or its ecosystem services (e.g., NGOs, recreational groups)
3. Stakeholders concerned with the decision-making processes (e.g., environmental advocacy groups)
4. Stakeholders funding management plans (e.g., taxpayers, funding agencies)
5. Stakeholders representing public interest (e.g., public officials)

Strategies to stakeholder participation

The complexity of ecosystem management decisions, ranging from local to international scales, requires the participation of stakeholders with diverse understandings, perceptions, and values of ecosystems and ecosystem services. Due to these complexities, effective ecosystem management is flexible and develops reciprocal trust around issues of common interest, with the objective of creating mutually beneficial partnerships. Key attributes of successful participatory ecosystem management efforts have been identified:

• Stakeholder involvement is inclusive, equitable, and focused on trust-building and empowerment.
• Stakeholders are engaged early on, and their involvement continues beyond decision and into management.
• Stakeholder analysis is performed to ensure parties are appropriately represented. This involves determining the stakeholders involved in the management issue; categorizing stakeholders based on their interest in and influence on the issue; and evaluating relationships between stakeholders.
• Stakeholders agree upon the aims of the participatory process from its beginning, and the means and extent of stakeholder participation are case-specific.
• Stakeholder participation is conducted through skilled facilitation.
• Social, economic, and ecological goals are equally weighed, and stakeholders are actively involved in decision making, which is arrived at by collective consensus.
• Stakeholders continually monitor management plan’s effectiveness.
• Multidisciplinary data are collected, reflecting multidisciplinary priorities, and decisions are informed by both local and scientific knowledge.
• Economic incentives are provided to parties responsible for implementing management plans.
• To ensure long-term stakeholder involvement, participation is institutionalized.

Ecosystem management decisions for the Malpai Borderlands were determined through active participation of diverse stakeholder groups.

Examples of stakeholder participation

Malpai Borderland management:

In the early 1990s, there was ongoing conflict between the ranching and environmentalist communities in the Malpai Borderlands. The former group was concerned about sustaining their livelihoods, while the latter was concerned about the environmental impacts of livestock grazing. The groups found common ground around conserving and restoring rangeland, and diverse stakeholders, including ranchers, environmental groups, scientists, and government agencies, were engaged in management discussions. In 1994, the rancher-led Malpai Borderlands Group was created to collaboratively pursue the goals of ecosystem protection, management, and restoration.

Helge å River & Kristianstads Vattenrike Biosphere Reserve:

In the 1980s, local government agencies and environmental groups noted declines in the health of the Helge å River ecosystem, including eutrophication, bird population declines, and deterioration of flooded meadows areas. There was concern that the Helge å, a Ramsar Wetland of International Importance, faced an imminent tipping point. In 1989, led by a municipal organization, a collaborative management strategy was adopted, involving diverse stakeholders concerned with the ecological, social, and economic facets of the ecosystem. The Kristianstads Vattenrike Biosphere Reserve was established in 2005 to promote the preservation of the ecosystem's socio-ecological services.

Strategies to ecosystem management

Several strategies to implementing the maintenance and restoration of natural and human-modified ecosystem exist. Command and control management and traditional natural resource management are the precursors to ecosystem management. Adaptive management, strategic management, and landscape-level conservation are different methodologies and processes involved in implementing ecosystem management:

Command and control management

Wolf reintroduction into Yellowstone National Park in January 1995. Observed increases in ecological resilience since wolf return demonstrate the potential cascading impacts of command and control management.

Command and control management utilizes a linear problem solving approach, in which a perceived problem is resolved through controlling devices such as laws, threats, contracts, and/or agreements. This top-down approach is used across many disciplines, and it is best suited for addressing relatively simple, well-defined problems, which have a clear cause and effect, and for which there is broad societal agreement as to policy and management goals. In the context of natural systems, command and control management attempts to control nature in order to improve natural resource extractions, establish predictability, and reduce threats. Command and control strategies include the use of herbicides and pesticides to improve crop yields; the culling of predators to protect game bird species; and the safeguarding of timber supply, by suppressing forest fires.

However, due to the complexities of ecological systems, command and control management may result in unintended consequences. For example, wolves were extirpated from Yellowstone National Park in the mid-1920s to reduce elk predation. Long-term studies of wolf, elk, and tree populations since wolf reintroduction in 1995 demonstrate that reintroduction has decreased elk populations, improving tree species recruitment. Thus, by controlling ecosystems to limit natural variation and increase predictability, command and control management often leads to a decline the resilience of ecological, social, and economic systems, termed the “pathology of natural resource management”. In this “pathology”, an initially successful command and control practice drives relevant institutions to shift their focus toward control, over time obscuring the ecosystem’s natural behavior, while the economy becomes reliant on the system in its controlled state. Consequently, there has been a transition away from command and control management, and increased focus on more holistic adaptive management approaches and on arriving at management solutions through partnerships between stakeholders.

Natural resource management

Shelterwood cutting allows for timber extraction, while maintaining ecosystem structure and allowing forest regeneration.

The term natural resource management is frequently used in relation to a particular resource for human use, rather than the management of a whole ecosystem. Natural resource management aims to fulfill the societal demand for a given resource without causing harm to the ecosystem, or jeopardizing the future of the resource. Due to its focus on natural resources, socioeconomic factors significantly affect this management approach. Natural resource managers initially measure the overall condition of an ecosystem, and if the ecosystem's resources are healthy, the ideal degree of resource extraction is determined, which leaves enough to allow the resource to replenish itself for subsequent harvests. The condition of each resource in an ecosystem is subject to change at different spatial and time scales, and ecosystem attributes, such as watershed and soil health, and species diversity and abundance, need to be considered individually and collectively.

Informed by natural resource management, the ecosystem management concept is based on the relationship between sustainable ecosystem maintenance and human demand for natural resources and other ecosystem services. To achieve these goals, ecosystem managers can be appointed to balance natural resource extraction and conservation over a long-term timeframe. Partnerships between ecosystem managers, natural resource managers, and stakeholders should be encouraged in order to promote the sustainable use of limited natural resources.

Historically, some ecosystems have experienced limited resource extraction and have been able to subsist naturally. Other ecosystems, such as forests, which in many regions provide considerable timber resources, have undergone successful reforestation and consequently, have accommodated the needs of future generations. As human populations grow, introducing new stressors to ecosystems, such as climate change, invasive species, land-use change, and habitat fragmentation, future demand for natural resources is unpredictable. Although ecosystem changes may occur gradually, their cumulative impacts can have negative effects for both humans and wildlife. Geographic information system (GIS) applications and remote sensing can be used to monitor and evaluate natural resources and ecosystem health.

Adaptive management

Adaptive management is based on the concept that predicting future influences and disturbances to an ecosystem is limited and unclear. Therefore, an ecosystem should be managed to it maintain the greatest degree of ecological integrity and management practices should have the ability to change based on new experience and insights. In an adaptive management strategy, a hypotheses about an ecosystem and its functioning is formed, and then management techniques to test these hypotheses are implemented. The implemented methods are then analyzed to evaluate if ecosystem health improved or declined, and further analysis allows for the modification of methods until they successfully meet the needs of the ecosystem. Thus, adaptive management is an iterative approach, encouraging “informed trial-and-error”.

This management approach has had mixed success in the field of ecosystem management, fisheries management, wildlife management, and forest management, possibly because ecosystem managers may not be equipped with the decision-making skills needed to undertake an adaptive management methodology. Additionally, economic, social, and political priorities can interfere with adaptive management decisions. For this reason, for adaptive management to be successful it must be a social and scientific process, focusing on institutional strategies while implementing experimental management techniques.

Strategic management

As it relates to ecosystem management, strategic management encourages the establishment of goals that will sustain an ecosystem while keeping socioeconomic and politically relevant policy drivers in mind. This approach differs from other types of ecosystem management because it emphasizes stakeholders involvement, relying on their input to develop the best management strategy for an ecosystem. Similar to other methods of ecosystem management, strategic management prioritizes evaluating and reviewing any impacts of management intervention on an ecosystem, and flexibility in adapting management protocols as a result of new information.

Landscape-level conservation

The designation of Gold Butte National Monument in 2016 established a wildlife corridor linking Lake Mead National Recreation Area and Grand Canyon-Parashant National Monument.

Landscape-level (or landscape-scale) conservation is a method that considers wildlife needs at a broader landscape scale when implementing conservation initiatives. By considering broad-scale, interconnected ecological systems, landscape-level conservation acknowledges the full scope of an environmental problem. Implementation of landscape-scale conservation is carried out in a number of ways. A wildlife corridor, for example, provides a connection between otherwise isolated habitat patches, presenting a solution to habitat fragmentation. In other instances, the habitat requirements of a keystone or vulnerable species is assessed to identify the best strategies for protecting the ecosystem and the species. However, simultaneously addressing the habitat requirements of multiple species in an ecosystem can be difficult, and as a result, more comprehensive approaches have been considered in landscape-level conservation.

In human-dominated landscapes, weighing the habitat requirements of wild flora and fauna versus the needs of humans presents challenges. Globally, human-induced environmental degradation is an increasing problem, which is why landscape-level approaches play an important role in ecosystem management. Traditional conservation methods targeted at individual species may need to be modified to include the maintenance of habitats through the consideration of both human and ecological factors.