Biogeographical models of species area relationship graph

Species–Area Relationship

biogeographical models of species area relationship graph

The species–area relationship is not only one of ecology's few laws (Schoener, However, there is no doubt that the first plot relating species with area, .. of species–area curves: Between biogeographical provinces, within. The species–area relationship or species–area curve describes the relationship between the Frank W. Preston, an early investigator of the theory of the species –area relationship, divided it into two types: samples (a census of a species– area relationships for very large areas—those collecting different biogeographic . Article (PDF Available) in Journal of Biogeography 30(1) - 27 · January with Reads . However, there is no doubt that the first plot relating species . species–area relation (Eqn 2) and the choros model (Eqn 1).

We see that one of Gleason's data sets is power law, contrary to his claims. The unbroken species—area curve is fitted with log S and the dashed line with untransformed S. From Preston's original bird data set. References Arrhenius O Species and area. Journal of Ecology 9: Arrhenius O On the relation between species and area — a reply.

Darlington PJ Zoogeography: The Geographical Distribution of Animals. A review and empirical evaluation. Journal of Biogeography Diversity and Distributions Deshaye J and Morisset P and Floristic richness, area and habitat diversity in an hemiarctic archipelago. Journal of Animal Ecology Gleason HA On the relation between species and area. May RM Patterns of species abundance and diversity. Preston FW Time and space and the variation of species. Preston FW The canonical distribution of commonness and rarity: Svensk Botanisk Tidsskrift The same sampling artifacts that led to low values for mammals do also apply here.

Models assume island populations are numerically self-sustaining, and have increasing probabilities of extinction on the island as they become rarer. Other green areas, garbage, etc. That 'rescue' is much less likely for oceanic island populations.

Figure - Species-area curves for ants on New Guinea relatively, a mainland and the isolated islands nearby. The island curve is steeper a higher z than the New Guinea curve, as explained above. Is the relationship between species and area linear? There are theoretical reasons to expect a z of 0.

However, in doing so we are accepting that species-area curves are linear. There are some points to consider: When small areas are sampled, species packing limits observed richness. There is only so much space, so much variation in resources When large areas are sampled, samples may incorporate different habitats, communities and species. Given apparent differences in what you might expect, is the linear equation predicting z logical?

biogeographical models of species area relationship graph

There is at least one study that questions the assumption of linearity Crawley and Harral, They sampled Berkshire and the East Berks in England in nested, contiguous quadrats, at and 25km2, also km2 contiguous quadrats and replicates not contiguous from 0.

Initial results, averaging the values obtained, don't differ significantly from the theory. An important point about effects of area on species diversity may have slid by here.

biogeographical models of species area relationship graph

The biological question is why does area affect species numbers? There are two schools of thought: Area is the direct determinant of diversity, since the multiplicity of factors which determine relative abundance and species diversity are prescribed, and independent of the specific island of area being studied. Area fits because area is a good indicator of the amount of habitat diversity present on an island.

Biology Biodiversity & Conservation part 6 (Species - Area , Relationship) class 12 XII

It is really the 'number of niches' that determines the number of species, but there is no established method for counting, or even estimating the number of niches in an environment. Instead, physical variables are usually measured. The assumption is made that the two measures should be highly correlated. One of the frequent approaches is the use of multiple regression models to determine what factors account for differences in species diversity on islands.

Jared Diamond ; Diamond and Mayr used this method to study the distribution of bird species on New Guinea and its satellite islands. The data were first fit to the basic equation; for these bird data the equation then reads: When Diamond instead measured the numbers of species in montane habitat islands on New Guinea proper, only the constant changed, to Figure 4 shows the numbers of species and the locations of montane habitat islands.

The central 'line' of New Guinea is a mountain backbone, and forms the largest single island. Diamond discerned that a part of the effect of increasing area was due to an increase in the maximum elevation observed on larger even habitat islands, and the concommitant differences in the number of habitats resulting from a greater range of elevations, which cause differences in climate. Figure - A map of the montane areas of New Guinea and the diversity of montane birds in those areas.

Variation in species numbers is correlated with variation in altitude.

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In addition to the number of species accounted for by area alone, each m of elevation 'caused' an increase of 2.

After the entry of this term, the regression equation reads: The more distant from the New Guinea source of species, the smaller the number of species when islands similar in size and elevation were compared. The decrease is approximately exponential with distance, and the number decreases by half for each Km on average.

When this factor in incorporated into the multiple regression equation, the result is: Figure 5 shows the basic species-area relationship for the satellite islands around New Guinea. Figure - Species-area relationship for birds of oceanic islands around New Guinea. Differences in isolation are evident both in absolute numbers of species and in different slopes for islands in different distance ranges. Figure compares some of the data for satellite islands to the curve for areas of the New Guinea mainland called a saturation curve.

The slope is clearly steeper for the islands than for mainland areas of similar habitats. The open circles are points for islands relatively near New Guinea, the filled circles for more distant islands. More distant islands have fewer species for the same area. Figure looks at that relationship. Each island diversity is assessed as the fraction of the number of species expected in a mainland habitat of the same area. Other data sets have been analyzed using the same basic approach, but one can significantly add to our understanding of the biology underlying island diversity patterns.

In a study of bird species diversity on the Channel Islands off Santa Barbara, California, Power worked backwards from the physical variables to the biological variables which correlated most closely with bird species diversity, using a method called path coefficient analysis. The method begins with a multiple regression analysis paralleling the study of New Guinea satellite islands.

Power first found the factor which explained the largest portion of the total variation in bird species diversity, entered it into the model equation, then determined the factor which explained the largest portion of the variation which remained after applying the model equation.

That factor enters second, and forms part of a new model equation. Subsequent factors are entered in order of importance, using the variation about the model equation fitted using factors already incorporated.

Species–area relationship

It seems logical that habitat heterogeneity or diversity for bird species would be measured by the biotic diversity of their residences, food sources, or residences of their food resources, i. Having used plant species diversity as the first factor entered stepwise into the model, what was the next most important factor? Once effects on plant species diversity are removed, area doesn't even account for a significant fraction of remaining variation, let alone be the most important factor in residual variation.

Only one factor is significant in explaining residual variation of a model including plant species diversity. This is a solid indication that measures of habitat heterogeneity diversity are the critical predictors of island species diversity.

Power, however, went further. Since plant species diversity, used as a predictor variable or factor is itself a biological variable, he asked what, in turn, explains plant species diversity. Using the same stepwise regression method, Power found 3 factors were significant, and they were 3 factors that correspond closely to those which Diamond found significant in explaining bird species diversity in the New Guinea satellite islands: Latitude effects are detectable only after effects of area have been removed, especially since the latitudes of the Channel Islands don't differ by much.

This factor apparently measures position of islands relative to North-South air and water currents off the California coast, rather than a direct impact of latitude itself. Taking this analysis at face value, we can see the origin of path coefficients.

The Theory of Island Biogeography

It suggests that the underlying factors which explain bird species diversity on the California Channel Islands are abiotic, the same factors which Diamond found in his studies, particularly area and factors important in assessing isolation, but that the proximal factors important to the birds are biological as well. A statistical path then takes the following form: While we may be accustomed to thinking of islands strictly in 'geological' terms, it is clear that islands take many forms, including lakes, forest patches in agricultural lands, or even zebra mussels colonies.

The key aspect of islands is that they are favourable habitat surrounded by inhospitable habitat. Looking at how zebra mussel colonies on soft sediments in Lake Erie can be 'islands', Bially and MacIsaac looked at invertebrate species diversity in relation to island area.

A very clear image emerged. The invertebrates utilize gaps between mussel shells as habitat, and mussel feces and pseudofeces as food. The Basic Model of Island Biogeography The model is one of a dynamic equilibrium between immigration of new species onto islands and the extinction of species previously established.

There are 2 things to note immediately: Species continue to immigrate over an indefinite period, not all are successful in becoming established on the island. Some that have been resident on the island go extinct. The model predicts only the equilibrium number of species, will remain 'fixed'. The species list for the island changes; those changes are called turnover.

Initially, at least, we will consider only events and dynamics over an ecological time scale, and one which assumes ecological interactions on the island occur as a result of random filling of niches, without adaptations to the presence of interacting species developing there.

Evolution is clearly excluded. The variables used in the basic model are Is, the immigration rate, which is clearly indicated by the subscript to be species specific, i. Here we're not counting noses, but rather the rate at which new species those not already present on the island immigrate. Phrased explicitly, it is the number of species immigrating per unit time onto an island already occupied by S species.

Also Es, the extinction rate, measured in species lost per unit time from an island occupied by S species. Finally, we need to know the size of the pool of species in the source area available to colonize the island. The immigration rate Is must certainly decrease monotonically on average as the number of species on the island increases, since as S increases there are fewer and fewer species remaining to immigrate from the pool P of potential immigrants at the source.

biogeographical models of species area relationship graph

If all species were equally likely to immigrate successfully i. There are, however, considerable differences in the dispersal abilities of species in source areas. Those with the highest dispersal capacities are likely to colonize an island rapidly have a higher immigration rateand later, on average, those with lower dispersal capacities will follow.

They will not only immigrate later, but the rate at which they immigrate will be lower because they have lower dispersal capacities. The rate at which species accumulate on islands is, therefore, initially rapid and then slower. Also, among those species with lower dispersal capacities the successful immigration of any one species should have less effect on the immigration rates of species remaining in the source pool we have not removed a likely immigrant from the pool than would the earlier immigration of a highly dispersible species.

Therefore, this part of the curve should be 'flatter'; the rate of immigration should be little affected by the arrival of one of these poor dispersers.

The result is an observed immigration rate curve which is concave. The actual or theoretical curve for any island is dependent on its isolation. For any source pool, the observed rate, while similar in shape, will be lower for more distant islands than for closer ones. Immigration rates are graphed from the left hand edge of figure 1, declining from the y axis with an increasing number of species already present.

Figure - The basic graphical model of equilibrium in the MacArthur-Wilson model.

biogeographical models of species area relationship graph

Figure from Brown and Gibson -Biogeography. The extinction rate Es should be, from parallel reasoning, a monotonically increasing function of S. If area, for example, acts only through its effects on population sizes, and extinctions are the chance result of small population sizes and demographic stochasticity, then as the number of species increases, the number of species with small populations and subject to chance extinctions increases in proportion, i. However, if we consider a more realistic biological scenario, then as the number of species increases, depressant interactions within and between species competition, predation, parasitism are more likely to occur, and extinctions are more likely as a result.

Remember that these are not species that have evolved adaptations to interactions.


Effects are direct and unmoderated. Since any extinctions resulting from interaction are in addition to those resulting from demographic stochasticity, the more realistic shape for the extinction curve is concave upwards. The extinction rate begins at 0 when there are no species on the island, then increases as species accumulate. At least for purposes of simplicity in looking at the basic implications of the model, the extinction curve can be thought of as a mirror image of the immigration curve.

We now have all the information to produce the basic graphical model. At that diversity on the island species are immigrating at a rate equal to disappearances due to extinction. The result is constant change in the species list on the island; that change in names occurs at a rate called x, the turnover rate.

The length of the species list, however, should remain constant.