Disturbance & Ecological Succession

Disturbance & Ecological Succession
Add to title slide from pg. 14 of Keddy’s “Water, Earth, Fire: Louisiana’s Natural Heritage” – “As I tell my students, if you live in Louisiana, there are only two possibilities: either your land will eventually flood, or it will eventually burn.” Hurricane Katrina Aug. 29, 2005 Image from

Succession – directional change in community composition at a site (as opposed to simple fluctuations), initiated by natural or anthropogenic disturbance, or the creation of a new site Some biologists restrict the definition to directional replacement of species after disturbance Disturbance – a discrete event that damages or kills residents on a site; either catastrophic or non-catastrophic (Platt & Connell 2003) Photo of W. J. Platt at Camp Whispering Pines, LA from K. Harms; photo of J. H. Connell from UCSB

Catastrophic disturbance – a disturbance that kills all residents of all species on a site; i.e., creates a “blank slate” (Platt & Connell 2003) Mt. St. Helens, Washington, U.S.A. May 18, 1980 Photo of Mt. St. Helens from Wikipedia

Non-catastrophic disturbance – a disturbance that falls short of wiping out all organisms from a site; i.e., leaves “residual organisms” or “biological legacies” (Platt & Connell 2003) Yellowstone Nat’l. Park, U.S.A. just after 1988 fires Luquillo Experimental Forest, Puerto Rico just after 1989 Hurricane Hugo Photo of Yellowstone in 1988 from Wikipedia; Photo of Luquillo Forest, Puerto Rico in 1989 from

Primary Succession – succession that occurs after the creation of a “blank slate,” either through catastrophic disturbance or de novo creation of a new site Mt. St. Helens, Washington, U.S.A. May 18, 1980 Anak Krakatau, Indonesia appeared above water ~ 1930 Photo of Mt. St. Helens in 1980 from Wikipedia; Photo of Anak Krakatau from

Secondary Succession – succession that occurs after non-catastrophic disturbance (including “old fields”) Yellowstone Nat’l. Park, U.S.A. just after 1988 fires Luquillo Experimental Forest, Puerto Rico just after 1989 Hurricane Hugo Photo of Yellowstone in 1988 from Wikipedia; Photo of Luquillo Forest, Puero Rico in 1989 from

Henry David Thoreau (1859) is often credited with coining “succession” as applied to directional changes in plant communities Thoreau made many remarkable observations at a time when many still believed in such phenomena as spontaneous generation “Though I do not believe that a plant will spring up where no seed has been, I have great faith in a seed. Convince me that you have a seed there, and I am prepared to expect wonders.” See: Faith in a Seed Photo of Thoreau from Wikipedia

Connell & Slatyer (1977) – Reacted against an emphasis on life-history strategies & competition alone; recognized a variety of species interactions that could impact succession Three models of succession: 1. Facilitation – Early species enhance the establishment of later species (if it occurs, it is perhaps most likely in primary succession) 2. Tolerance – Early species have no effect on later species 3. Inhibition – Early species actively inhibit later species

Primary succession along the Glacier Bay chronosequence One of the world’s most rapid and extensive glacial retreats in modern times (so far); eliminated ~2500 km2 of ice in ~200 yr, exposing large expanses of nutrient-poor boulder till to biotic colonization Photo of Glacier Bay National Park, Alaska from Wikipedia

Primary succession along the Glacier Bay chronosequence Classical view of Glacier Bay succession based on 50 yr of research, which employed the simple chronosequence assumption: - Mosses - Mountain Avens (Dryas); shallow-rooted herbs - Willows (Salix); first prostrate, then shrubby species - Alder (Alnus crispus); after 50 yr forms thickets to 10 m - Sitka Spruce (Picea sitchensis); invade alder thickets - Hemlock (Tsuga heterophylla); establish last Succession is driven by N-fixation (Dryas & Alnus) Alnus acidifies the soil, allowing Picea invasion Accumulation of soil carbon through succession improves soil texture and water retention, ultimately allowing invasion by Tsuga

Primary succession along the Glacier Bay chronosequence Fastie (1995) – Reconstructed patterns of stand development at several sites within the chronosequence; intensively analyzed tree-rings Figure from Fastie (1995)

Primary succession along the Glacier Bay chronosequence Fastie (1995) – Identified 3 alternative pathways of compositional change (not a single chronosequence of events): 1. Sites deglaciated prior to 1840 were colonized early by Picea & Tsuga 2. Sites deglaciated since 1840 were the only sites colonized early by N-fixing Alnus 3. Sites deglaciated since 1900 were the only sites dominated relatively early by black cottonwood (Populus trichocarpa)

Primary succession along the Glacier Bay chronosequence Oldest sites: Dryas  Picea & Tsuga Intermediate sites: Dryas  Alnus  Picea Youngest sites: Dryas  Alnus  Populus  Picea What accounts for these among-site differences in composition? Differences are unrelated to soil parent material Strong effect of seed source: Refugial Picea stands are concentrated at the mouth of the bay; distance from the nearest seed source explains 58% of among-site variance in early Picea recruitment Younger sites received more of their seed rain from new communities colonizing exposed surfaces than from refugial populations

Primary succession along the Glacier Bay chronosequence What about facilitation? Succession of Alnus to Picea was considered a textbook example of facilitation in the mid- to-late 20th century The real pattern is more complex! Alnus was absent on older sites, so Picea does not require it for establishment Alnus may either inhibit or facilitate seedling establishment of Picea Chapin et al. (1994) – Found net positive effects of Alnus on Picea on glacial moraines, but net negative effects on floodplains

Facilitation along cobble beaches of New England Bruno (2000) – Determined mechanisms by which Spartina alterniflora is a facilitator of relatively large impact on the community (i.e., a “foundation species” - Drayton [1972]; “keystone modifier” - Bond [1993]; “ecosystem or keystone engineer” - Jones et al. [1994]) Observations: Spartina occurs along the shore; cobble-beach plants occur behind Spartina Cobble-beach community is absent along breaks in the Spartina phalanx Photo by J. Bruno

Facilitation along cobble beaches of New England Bruno (2000) Question: At which life stage(s) is colonization of cobble-beach plants limited to sites behind Spartina? Experiment: Addition experiments to determine limiting life stages (seed supply, seed germination, seedling emergence, seedling establishment & adult survival) for cobble-beach plants Results: Only seedling emergence & establishment were adversely affected by the absence of Spartina

Facilitation along cobble beaches of New England Bruno (2000) Question: By what mechanism(s) does Spartina facilitate seedling emergence & establishment of cobble-beach plants? Experiment: Conducted manipulations of water velocity, substrate stability, herbivory & soil quality in sites lacking Spartina Results: Substrate stability increased seedling emergence & establishment, whereas manipulations of the other factors had limited influence

Facilitation along cobble beaches of New England Bruno (2000) Conclusions: Spartina alterniflora acts as a foundation species, keystone modifier & ecosystem engineer by stabilizing the substrate, enabling seedlings of cobble-beach plants to emerge & survive Photo by J. Bruno

Primary succession on Krakatau & Anak Krakatau Explosion of Krakatau (1883) The loudest explosion ever heard by humans Created tsunamis that killed 30,000 people on larger islands & mainland The island was effectively “sterilized” Anak Krakatau Anak Krakatau (“Child of Krakatau”) appeared out of the ocean in ~1930 & has been growing ever since First analyses of colonizing vegetation were by Doctors van Leeuwen (~1930s); more recent expeditions by Robert J. Whittaker Photo of Anak Krakatau from

Primary succession on Krakatau & Anak Krakatau Whittaker (1994) – Examined dispersal characteristics of plant arrivals Nearest mainland site is Sumatra (~ 50 km away); Nearest island is ~ 21 km away First arrivals (within 4 yr of eruption) were either wind or water dispersed Early zoochorous plants were dominated by figs; 17 of 24 fig species on the island arrived in the first 30 yr and are now canopy dominants, which suggests that bats have been very important dispersal vectors or mobile links (Old World bats have gut-retention times up to 12 hr)

Primary succession on Krakatau & Anak Krakatau Whittaker (1994) – There are now 124 zoochorous species on Anak Krakatau Doves and pigeons (> 4 hr gut retention time) have been important dispersers subsequent to colonization of the island by figs (an indirect mechanism of facilitation by bats operating through figs?) Many large-seeded species are absent relative to Sumatra & the mainland flora

Primary succession on the flanks of Mount St. Helens May 18, 1980 – the north face of the previously symmetrical mountain collapsed in a rock-debris avalanche that essentially wiped clean 60 km2 of forest Fagan & Bishop (2000) – Examined the influence of herbivores on the rate of spread of lupines (Lupinus lepidus), the site’s main “colonizing” species Mt. St. Helens, Washington, U.S.A. May 18, 1980 Photo of Mt. St. Helens from Wikipedia

Primary succession on the flanks of Mount St. Helens Lupines are efficient N-fixers & trap detritus; they are often facilitators in ecological succession Lupines colonized from remnant populations elsewhere on the volcano to form patches Spread rapidly initially and then slowed Why? Figure from Fagan & Bishop (2000)

Primary succession on the flanks of Mount St. Helens Fagan & Bishop (2000) – Ruled out various alternative explanations for slowed population growth rates & focused on the effect of insect herbivores, whose colonization lagged behind the lupines by 10 yr Experimental test: Established plots at the center of lupine patches (core) and at the edge of expanding patches (edge) Sprayed half of the plots with pyrethroid insecticide

Primary succession on the flanks of Mount St. Helens Much higher incidence of damaging insects at patch edges Higher leaf damage at patch edges Figure from Fagan & Bishop (2000)

Primary succession on the flanks of Mount St. Helens Lower seed production at patch edges Edge Site Core Site Why was there more herbivore activity at the edge? Densities of predators (e.g., spiders) & parasitoids (e.g., a tachinid fly) were 4x higher at the core vs. edge Predators may be more abundant in the core where plant density & productivity are higher Figure from Fagan & Bishop (2000)

Primary succession on the flanks of Mount St. Helens Fagan and Bishop (2000) – Diffusion model showed how reduced seed production at the edge affects rates of lupine spread (assuming no long-distance, jump-dispersal events) Figure from Fagan & Bishop (2000)

Modeling secondary succession – Horn (1975) Developed simple Markov models of successional replacement of temperate-zone tree species Forest consists of cells, each occupied by a single tree Probability of replacing an individual tree with a new individual of a given species is calculated from a transition matrix Example of transition matrix for four species (GB=grey birch; BG=black gum; RM=red maple; BE=beech) GB BG RM BE GB BG RM BE The values are just examples to illustrate the process! Initial composition vector: (100, 0, 0, 0) After 1 time step: (5, 36, 50, 9) Iterate this process & plot the changes in relative abundance…

Modeling secondary succession – Horn (1975) BE GB RM BG Figure from Horn (1975)

Modeling secondary succession One approach for estimating transition probabilities: proportional to the fraction of each species as saplings beneath adults, e.g., if 5% of saplings beneath GB are GB, then P(GB|GB)=0.05 – Horn (1975) If the same transition matrix is used throughout, then a stable composition (the dominant Eigenvector) will result (here dominated by BE) However, the Markov approach is phenomenological, so… Why do recruitment probabilities vary, i.e., what biological traits lead to different colonization rates & relative abundances?

Modeling secondary succession – Pacala et al. (1996; SORTIE) The most recent generation of forest simulation models; precursors include FORET (Shugart & West 1977) Spatially explicit, mechanistic simulation model developed to predict dynamics of succession for 9 species of northeastern U.S.A. hardwoods Early occupation by Red Oak (Quercus rubra) & Black Cherry (Prunus serotina) followed by late dominance by Beech (Fagus grandifolia) & Hemlock (Tsuga canadensis), with Yellow Birch (Betula alleghaniensis) present in gaps

Modeling secondary succession – Pacala et al. (1996; SORTIE) Basics of SORTIE: Spatially explicit model predicting the fate of every individual tree throughout its life Individual performance is affected by resource availability at each tree’s location (original SORTIE only included competition for light) Species-specific functions predict each individual’s growth, mortality, fecundity & dispersal; estimated from data collected in the field Four sub-models determine the fate of each individual throughout its life

Modeling secondary succession – Pacala et al. (1996; SORTIE) (1) Resource (light) submodel: Calculates light available to an individual based on its neighborhood; the process is analogous to taking a fisheye photo above each plant Calculates a projected cylindrical crown for each individual based on data relating crown diameter & depth to stem diameter Computes whole-season photosynthetically active radiation (PAR) for each plant based on the location & identity of neighbors Figure from Pacala et al. 1996

Modeling secondary succession – Pacala et al. (1996; SORTIE) (2) Growth sub-model: Species-specific equations predict radial growth from diameter & light availability Figure from Pacala et al. 1996

Modeling secondary succession – Pacala et al. (1996; SORTIE) (3) Mortality sub-model: Species-specific equations predict probability of death from an individual’s growth rate over the past 5 yr Figure from Pacala et al. 1996

Modeling secondary succession – Pacala et al. (1996; SORTIE) (4) Recruitment sub-model: Species-specific equations predict the number & spatial locations of seedlings based on the sizes of adult trees Figure from Pacala et al. 1996

Modeling secondary succession – Pacala et al. (1996; SORTIE) Community-level output: From randomly seeded initial composition Hemlock & Beech clearly dominated after 500 yr Figure from Pacala et al. 1996

Modeling secondary succession – Pacala et al. (1996; SORTIE) The mechanistic approach taken in this model allows one to ask: Which key traits define species performance? How sensitive are model predictions to parameter values (and therefore sampling errors in parameter estimation)? How would hypothetical species with different parameter values perform in this community? What would constitute a “superspecies” (i.e., one of J. Silvertown’s ecological / evolutionary “demons”)? How many species could potentially coexist, e.g., > 50 spp. for > 10,000 yr? How would changing abiotic / biotic conditions affect forest trajectories?

Modeling secondary succession – Pacala et al. (1996; SORTIE) Baseline without disturbance Heavy disturbance Large, circular clear-cut Figures from Deutschman et al. 1997

Succession may involve changes beyond species composition… Community and Ecosystem Properties: Diversity – often increases throughout succession Standing-crop biomass – often increases throughout succession Elemental cycling & other biogeochemical processes – e.g., the Hubbard Brook experiments in New Hampshire, and Peter Vitousek’s work in Hawaii Susceptibility to disturbance – may be a function of time since last disturbance, e.g., fire and the accumulation of fuel loads

Anthropogenic Disturbance & Ecological Succession
If “all species have evolved in the presence of disturbance, and thus are in a sense matched to the recurrence pattern of the perturbation”, why are anthropogenic disturbances often so damaging? (Paine et al. 1998) Anthropogenic disturbances often differ from the natural disturbance regime in timing, frequency, or intensity Paine et al. (1998) also argued that: “more serious ecological consequences result from compounded perturbations within the normative recovery time of the community in question”

A marine example: Corals in the Caribbean Hughes (1994, Science) One-two punch of overfishing (“selective disturbance”) & “natural” mass mortality of dominant urchins (Diadema) has created a “phase-shift” from coral-dominated to macroalgae-dominated reefs Caribbean coral reefs may never recover! Photo of macroalgae-dominated reef from

A terrestrial example: Dipterocarps in southeast Asia Curran et al. (1999, Science) One-two punch of logging & increased frequency of El Niño events (due to anthropogenically induced climate change?) resulted in elimination of recruitment by dipterocarps in forests of Borneo! May result in a large-scale “phase-shift” away from dipterocarp domination of the forests [dipterocarps are the principal food of giant squirrels, bearded pigs, several species of parakeet & myriad specialist insects, etc.] Photo of dipterocarp forest from

Diversity-Productivity, Diversity-Invasibility, & Diversity-Stability Relationships
Warmer sea-surface temperatures (indicated by warmer colors) = higher productivity Image from Committee on Earth Observation Satellites (CEOS):

Diversity-Productivity Relationship
Many shapes for this relationship have been observed in nature Rosenzweig & Abramsky (1993)

Many shapes for this relationship have been observed in nature I. II. III. S or D S or D S or D Productivity Productivity Productivity Sometimes curves like I and III may arise from sampling opposite ends of productivity gradients in which curve II is the overall relationship II. a. II. b. S or D S or D Productivity Productivity

Many shapes for this relationship have been observed in nature Mittelbach et al. (2001) Methods: Examined the relationship between productivity & diversity for 171 studies Observations: Even though many researchers are enamored of hump-shaped curves, the curves vary dramatically from site-to-site, as well as within & among taxonomic groups Suggestions and conclusions: Don’t assume a particular relationship – measure it Be wary of the independent variable used as a surrogate for “productivity”

Many shapes, but what are the mechanisms? Tilman (1982, 1988), Tilman et al. (1996), etc. Explained hump-shaped curves by the changes in heterogeneity that sometimes accompany changes in resource availability A A A A B B A A B B B B A A A B A A B B B A A A B B B A B B A B A A A A B B B E.g., soil fertility / productivity gradient = poorest soil; species A out- competes species B = richest soil; species B out- competes species A

Rosenzweig & Abramsky (1993) Summarized several mechanistic hypotheses for hump-shaped curves Suggested that separate mechanisms account for the rising vs. falling portions Preferred mechanism for the rising portion: “A poor environment supplies too meager a resource base for its would-be rarest species, and they become extinct” In other words “poor environments support lower population sizes, and population size is inversely related to extinction probability” No well-supported mechanism for the falling portion: Provided several potential mechanisms, but claimed that none are well-supported by observations or experiments; even so, Tilman’s heterogeneity hypothesis has some empirical support

Stevens & Carson (2001) Declining curves could result simply from size differences; if sites are sampled with the same sized plots, more productive sites may have fewer species because they have fewer individuals, especially owing to the ubiquitous clumping that occurs in natural populations A A A A A A A B B B A A B A B A E.g., cloudiness-induced productivity gradient = lowest light availability = highest light availability How might this problem be avoided? Use an index that is insensitive to sample size

Kyle’s conjecture… If disturbance, predation, competitive equivalence, or dispersal limitation occur alone or in combination such that competitive exclusion does not occur among the recruits of species within a guild (especially plants), then sites with conditions in which more species are capable of surviving and reproducing will contain more species, i.e., diversity will increase up the resource (e.g., fertility) gradient Resource availability, e.g., soil fertility Species 1 Species 2 Species 3 Species 4 Species 5 Species 6 Species-rich community at high end of resource gradient Species-poor community at low end of resource gradient In other words, if species’ realized niches closely track their fundamental niches, and fundamental niches overlap primarily at the high end of resource availability, then there should be a positive diversity-productivity relationship.

A hump-shaped diversity-productivity relationship could result in the “Paradox of Enrichment” within trophic levels… Community sampled before fertilization Community sampled after moderate-level fertilization Community sampled after high-level fertilization Species diversity Productivity

A hump-shaped diversity-productivity relationship could result in the “Paradox of Enrichment” within trophic levels… Gough et al. (2001) Methods: Examined long-term experiments from 7 Long-Term Ecological Research (LTER) sites in North America Observations: Nearly all demonstrated a decline in diversity after fertilization Suggestions & conclusions: The results have utility for similar situations, but little relevance to natural productivity gradients, since species distributions along natural gradients are influenced by long-term ecological & evolutionary processes, e.g., species may preferentially colonize or originate within sites of high productivity, giving rise to a positive relationship

Diversity-Productivity Relationship (Productivity-Diversity)
So far we have considered productivity gradients due to gradients in resource availability, e.g., physical gradients What happens when we reverse the axes, and ask how diversity in a given site, i.e., one set of physical conditions, influences productivity?

Examples from artificial communities… Loreau et al. (2001) “Biodiversity and Ecosystem Functioning…” Methods: Compiled data from a variety of field, Ecotron & other mesocosm experiments in which S or D were varied experimentally Observations: Sites of high intrinsic resource availability Productivity Sites of low intrinsic resource availability S or D

Examples from artificial communities… Loreau et al. (2001) “Biodiversity and Ecosystem Functioning…” Conclusions: A monotonic or saturating curve almost always results from experimental settings examining the influence of diversity on productivity At least two mechanisms can create a positive relationship between diversity and productivity: 1. Complementarity – species use complementary niche space 2. Sampling – random sampling a large species pool is more likely to select a key (highly productive) species than sampling a small pool How might these two mechanisms differ in their implications for conservation, global change, etc., especially with respect to redundancy?

Latitudinal gradient Productivity Diversity Biomass gradient Productivity Productivity Diversity Experimental diversity gradient Diversity Productivity For plants, the relationship may change with scale (see Mittelbach et al. 2001) Experimental manipulations of plant diversity within habitats generally yield positive relationships Figure from Purvis & Hector (2000)

Diversity-Invasibility Relationship
This is especially germane in today’s world of rampant spread of exotic species, i.e., the “homogenization of biodiversity” Charles Elton first proposed that more diverse communities should be less invasible Photo of Charles Elton from

This is especially germane in today’s world of rampant spread of exotic species, i.e., the “homogenization of biodiversity” Fargione et al. (2003) Methods: Experimental, grassland plots containing mixtures of plants from four “functional guilds”: C3 (cool-season) grasses, C4 (warm-season) grasses, legumes, non-N-fixing forbs Experimentally introduced seeds of representatives of each guild Results and conclusion: C4 grasses exhibited the greatest inhibitory effect on introduced species (i.e., they were competitive dominants); established species from each functional guild most strongly inhibited species from its own guild Diversity reduces invasibility, both by increasing the chances of encountering established plants of the same guild (“close competitors cannot invade”), as well as established plants of the dominant guild (a “sampling” effect)

This is especially germane in today’s world of rampant spread of exotic species, i.e., the “homogenization of biodiversity” Levine (2000) Methods: Removed non-Carex species from sedge tussocks along streams in California, and subsequently added 1, 3, 5, 7, 9 native species, but kept the total cover of plants identical After one year, added 200 seeds of 3 exotic species to each tussock Results and conclusion: Fewer exotic seedlings established on more species-rich tussocks Increased diversity provides increased “immunity” to invasion Levine also found that diversity of native species was positively related to diversity of exotics in unmanipulated tussocks In light of his experimental results, how could this happen?

Diversity-Stability Relationship
Initial empirical guess… MacArthur (1955) a.k.a. “complexity-stability” relationship Alternative energy pathways in complex food webs might favor more constant population sizes with reduced fluctuations, thus promoting stability

Early modeling results… May (1973) Challenged MacArthur’s intuition & verbal arguments with mathematical models that showed no theoretical basis for the relationship to necessarily be in any particular direction (all possibilities could be obtained)

Back to empiricism, with potential reasons for differences of opinion… Pimm (1984; 1991) – Three levels of organization at which to measure “stability” Population Community (especially community composition) Ecosystem (especially biomass, energy flux, or the flux of matter, e.g., C, N, etc.)

Back to empiricism, with potential reasons for differences of opinion… Pimm (1984; 1991) – Five definitions for “stability” Stability (in the strict mathematical sense) – a system is stable if, and only if, the variables all return to equilibrium conditions after displacement from them Resilience – the rapidity with which a variable that has been displaced from equilibrium returns to it Persistence – the duration that a variable maintains a given value until it changes to a new value Resistance – the degree to which other variables change when a given variable is permanently changed to a new value Variability – the degree to which a variable varies over time

Back to empiricism, with potential reasons for differences of opinion… Pimm (1984; 1991) – At least three definitions for “complexity” Species richness – S Connectance – the degree to which all nodes interconnect with other nodes in a food web Relative abundance – D …and this isn’t an exhaustive list! (3 levels of ecological organization) x (5 definitions of “stability”) x (3 definitions of “complexity”) = at least 45 different questions that could be asked about the relationship between community complexity & stability!

Mechanisms that could generate a positive relationship between species diversity & ecosystem-level stability… McCann (2000) – Averaging effect – “Assume covariances between species are zero and variance (si2) in abundance of individual species i in a plant community is equal to cmiz, where c and z are constants and mi is the mean density of species i. Given that all k species in a community are equal in abundance and sum to m (that is, mi=m/k), then the coefficient of variation (CV) of community abundance can be determined as: CV = 100s/m = 100(c/k)1/z For the case z > 1, increasing k (species number) decreases the variation in biomass for the plant community”

Mechanisms that could generate a positive relationship between species diversity & ecosystem-level stability… McCann (2000) – Negative-covariance effect – “If covariances between species (say, species a and b) are negative (that is, cov(a,b)<0), then the variance in the abundance of two species: s2(a+b) = sa2 + sb2 + 2cov(a,b) will be less than the sum of the individual variances (that is, sa2 + sb2), and so will decrease overall biomass variance in the plant community”

Mechanisms that could generate a positive relationship between species diversity & ecosystem-level stability… McCann (2000) – Insurance effect – “An ecosystem’s ability to buffer perturbations, loss in species and species invasions is dependent on the redundancy of the species having important stabilizing roles, as well as on the ability of the species in the community to respond differently to perturbations. Increasing diversity increases the odds that such species exist in an ecosystem. This idea has been extended to suggest that the greater the variance of species’ responses in a community then the lower the species richness required to buffer an ecosystem. …increasing diversity increases the odds that at least some species will respond differentially to variable conditions and perturbations… greater diversity increases the odds that an ecosystem has functional redundancy by containing species that are capable of functionally replacing important species… taken together, these two notions have been called the insurance hypothesis”

Mechanisms that could generate a positive relationship between species diversity & ecosystem-level stability… McCann (2000) – Weak-interaction effect – “Weak interactions serve to limit energy flow in a potentially strong consumer-resource interaction and, therefore, to inhibit runaway consumption that destabilizes the dynamics of food webs. In addition, the weak interactions serve to generate negative covariances between resources that enable a stabilizing effect at the population & community level. The negative covariances ensure that consumers have weak consumptive influences on a resource when the resource is at low densities.”

Disturbance & Ecological Succession

Similar presentations

Presentation on theme: "Disturbance & Ecological Succession"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Disturbance & Ecological Succession

Similar presentations

Presentation on theme: "Disturbance & Ecological Succession"— Presentation transcript:

Similar presentations

About project

Feedback