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How do you estimate global species richness?

How do you estimate global species richness?


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I know how estimates of species richness in a small area are made. But how do you estimate the total number of species on a global scale?

One example of such a method can be found in Mora et al. 2011.


You might be interested in the below works as well as the references that they cite.

May, R. M. (1988). How many species are there on Earth? Science, 241(4872), 1441-1449.

Millennium Ecosystem Assessment. (2005). Ecosystems and Human Well-Being: Synthesis (p. 137). Washington, D.C.: Island Press.

It's worth noting that many estimates of species richness at large scales are based on the use of the species-area relationship to upscale richness measurements from small scales to large scales.


How to Measure Species Diversity?

Any measure of species diversity, by itself, does not convey much information we appreciate its significance only when we compare with any other measure.

Measures of species diversity can be divided into three categories (Magurran, 1988).

(i) Species richness indices,

(ii) Species abundance models, and

(iii) Species proportional abundance based indices

Species Richness Indices:

Species richness, as measure of diversity, has been used by ecologists. Species density or the number of species per m 2 is most commonly used to measure species richness. However, species richness increases with sample size. The smallest sample size may be 1 km^ and the largest may be the entire region or country.

As the sample sizes are always unequal, Sanders technique called Rarefaction is used to cope with this difficulty.

Sanders’s formula, as modified by Hurlbert (1971) is as follows:

The simplest approach is to take the number of individuals in the smallest sample as the standardized sample size.

This may be explained with the help of the following example:

If in one catch of fish we obtain 9 species with 23 individuals, and in another catch from the same area made for the same duration we obtained only 13 individuals belonging to 6 species, Hurlberts’ formula may be used to find out the number of species we would have expected in the first catch if it too had only 13 individuals. Thus, expected number of species for the first catch x is 6.6 species (Table 7.4).

This index is based on the ratio of number of species (S) and the square root of the total number of individuals (N).

It is claimed that this index may be used to compare samples of different sizes and that the effect of the number of individuals is reduced. However, some authors have shown that this index is not independent of sample size.

Using the data given in Table 7.4, the value of IMn for catch x and catch y will be 1.88 and 1.66 respectively.

This index also relates the number of species to the number of individuals.

The index is influenced by sample size. However, some authors have demonstrated that both this and Manhinick’s index are insensitive to changes in community structure.

Using the data given in Table 7.4, the value of for sample x and sample y will be 2.55 and 1.95 respectively.

Species Abundance Models:

No community has species of equal abundance. Some species are very abundant, others may have medium abundance and still others may be rare or represented by only a few individuals. This observation led to the development of species abundance models.

Species diversity data is frequently described by one or more patterns of distribution (Piclou, 1975), diversity is usually examined in relation to the following four models:

(b) The log normal distribution

(d) The broken stick model (the random niche boundary hypothesis)

When plotted on a rank abundance graph, the four models represent a progression ranging from the geometric series where a few species are dominant with the remaining fairly uncommon, through the log series and log normal distributions where species of intermediate abundance become more common and ending in the conditions represented by the broken stick model in which species are equally abundant as may be hardly observed.

Species Proportional Abundance Based Indices:

These indices provide an alternative approach to the measurement of diversity. These indices are called heterogeneity indices (Peet 1974) as they take both species richness and evenness into consideration. South wood (1978) called them nonparametric indices in view of the fact that no assumptions are made about the shape of the underlying species abundance distribution. The following indices are used.

This index relates the contribution made by each species to the total number of individuals present.

Where pi is the proportion of individuals in the ith species. The equation given by Wilhm (1967) is the following:

Where pi = the number of individuals in the ith species and N= the total number of individuals. The values of Simpson’s index range from zero to 1 (unity) and are inversely proportional to the wealth of species (As I increases, diversity decreases). Pielou (1969) has given the following form of equation.

Therefore, index is usually expressed as 1 – I or l/I. The reciprocal form of Simpson’s index ensures that the value of the index increases with diversity.

The index independently derived by Shannon and Wiener from the application of information theory is known as the Sharmon index of diversity. It is sometimes incorrectly referred to as the Shannon – weaver index (Krebs, 1985).

The index assumes that:

(a) All species are represented in the sample, and

(b) Individuals are randomly sampled from an ‘indefinitely large’ population (Pielou, 1975).

It is calculated from the equation:

Where pi is the proportion of individuals found in the ith species. It is estimated as (ni/N). N is total number of individuals in S species. The value of Shannon index usually varies between 1.5 and 3.5 and rarely exceeds 4.5. The value of H’ is related to species richness but is also influenced by the underlying species abundance distribution. May (1975) has shown that if the underlying distribution is log normal, 10 species will be required to give a value of H’ < 5.0. Log2 is often used to calculate Shannon index. Usually the index is obtained from the series.


Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Peter Soroye, Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, 30 Marie Curie Pvt., Ottawa, ON, K1N 6N5, Canada.

Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, Ottawa, ON, Canada

Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, Ottawa, ON, Canada

Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, Ottawa, ON, Canada

Peter Soroye, Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, 30 Marie Curie Pvt., Ottawa, ON, K1N 6N5, Canada.

Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, Ottawa, ON, Canada

Canadian Facility for Ecoinformatics Research, Department of Biology, University of Ottawa, Ottawa, ON, Canada

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Abstract

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species

35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35–57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding

3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis

A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9–12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4–20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.

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The Fundamental Design of EstimateS: Shared Species and Similarity

For sets of related sampling units, EstimateS computes several measures of compositional similarity, including traditional similarity indices as well as estimators of shared species and similarity indices that take shared, but unobserved species into account by statisical methods. These latter methods require species abundance data for a set of related samples (sample-based abundance data) or for summed incidence data for two or more sets of sampling units. More information here.


2 Types of Diversity Indices of Biodiversity

Brief outlines of the two types of diversity indices of biodiversity are discussed in this article.

The two types are: (1) Dominance Indices, and (2) Information-Statistic Indices.

1. Dominance Indices:

Dominance indices are weighted toward the abundance of the commonest species. A widely used dominance index is Simpson’s diversity index. It takes into account both richness and evenness.

Simpson’s Diversity Indices:

The term “Simpson’s diversity index” can actually refer to any one of 3 closely related indices.

Simpson’s index measures the probability that any two individuals drawn at random from an infinitely large community will belong to same species. There are two versions of the formula for calculating D.

Either is Acceptable but is to be Consistent:

where, n = the total number of individuals of each species, N = the total number of organisms of all species.

The value of D ranges between 0 and 1.

With this index, 0 represents infinite diversity and 1, no diversity. That is, the bigger the value of D, the lower the diversity. This does not sound logical, so to get over this problem, D is often subtracted from 1 or the reciprocal of the index is taken.

Simpson’s Index of Diversity 1-D:

This index represents the probability that two individuals randomly selected from a community will belong to different species. The value of this index also ranges between 0 and 1, but here, the greater the value, the greater the diversity.

Simpson’s Reciprocal Index 1/D:

The value of this index starts with 1 as the lowest possible figure. This figure would represent a community containing only one species. The higher the value, the greater would be the diversity. The maximum value is the number of species in the sample. For example, if there are five species in the sample, then maximum value is 5.

The name Simpson’s diversity index is often very loosely applied and all three related indices described above (Simpson’s index, Simpson’s index of diversity and Simpson’s reciprocal index) have been quoted under term, depending on authors.

As an example, let us consider the following table:

Putting the values into the formula for Simpson’s index:

Then, Simpson’s index of diversity 1 – D = 0.7 and Simpson’s reciprocal index 1/D = 3.3.

All these three values represent the same biodiversity. It is, therefore, important to ascertain which index has actually been used in any comparative studies of biodiversity. The disadvantage of Simpson’s index is that it is heavily weighed toward the most abundant species, as are in all dominance indices.

The addition of rare species with one individual will fail to change the index. As a result, Simpson’s index is of limited value in conservation biology if an area has many rare species with just one individual.

2. Information-Statistic Indices:

Information-statistic indices can take into account rare species in a community. Information- statistic indices are based on the rationale that diversity in a natural system can be measured in a way that is similar to the way information contained in a code or message is measured.

By analogy, if we know how to calculate the uncertainty of the next letter in a coded message, then we can use the same technique to calculate the uncertainty of the next species to be found in a community.

A widely used diversity index is Shannon index.

The Index is given by:

where, pi is the proportion of individuals found in the i th species and In denotes natural logarithm.

The following table gives an example:

Putting the values into the formula for Shannon index, Hs = 1.201

Even the rare species with one individual (species E) contributes some value to the Shannon index, so if an area has many rare species, their contributions would accommodate. Shannon index has a minus sign in the calculation, so the index actually becomes 1.201, not-1.201. Values of Shannon index for real communities are often found to fall between 1.5 and 3.5. The value obtained from a sample is in itself of no significance. The index becomes useful only while comparing two or more sites.

A second information-statistic index, designed to reflect species abundance.

The Brillouin index and is given by:

where, N is the total number of individuals in the community, ni is the number of individuals in the i th species.

The following table gives an example:

This index describes a known population. There is no room for uncertainty while using this index. It places more emphasis on species richness and is moderately sensitive to sample size.


Global Extinction Rates: Why Do Estimates Vary So Wildly?

Is it 150 species a day or 24 a day or far less than that? Prominent scientists cite dramatically different numbers when estimating the rate at which species are going extinct. Why is that?

Most ecologists believe that we are in the midst of the sixth mass extinction. Humanity’s impact on nature, they say, is now comparable to the five previous catastrophic events over the past 600 million years, during which up to 95 percent of the planet’s species disappeared. We may very well be. But recent studies have cited extinction rates that are extremely fuzzy and vary wildly.

The Millennium Ecosystem Assessment, which involved more than a thousand experts, estimated an extinction rate that was later calculated at up to 8,700 species a year, or 24 a day. More recently, scientists at the U.N. Convention on Biological Diversity concluded that: “Every day, up to 150 species are lost.” That could be as much as 10 percent a decade.

But nobody knows whether such estimates are anywhere close to reality. They are based on computer modeling, and documented losses are tiny by comparison. Only about 800 extinctions have been documented in the past 400 years, according to data held by the International Union for the Conservation of Nature (IUCN). Out of some 1.9 million recorded current or recent species on the planet, that represents less than a tenth of one percent.

Nor is there much documented evidence of accelerating loss. In its latest update, released in June, the IUCN reported “no new extinctions,” although last year it reported the loss of an earwig on the island of St. Helena and a Malaysian snail. And some species once thought extinct have turned out to be still around, like the Guadalupe fur seal, which “died out” a century ago, but now numbers over 20,000.

Moreover, the majority of documented extinctions have been on small islands, where species with small gene pools have usually succumbed to human hunters. That may be an ecological tragedy for the islands concerned, but most species live in continental areas and, ecologists agree, are unlikely to prove so vulnerable.

But the documented losses may be only the tip of the iceberg. That’s because the criteria adopted by the IUCN and others for declaring species extinct are very stringent, requiring targeted research. It’s also because we often simply don’t know what is happening beyond the world of vertebrate animals that make up perhaps 1 percent of known species.

One way to fill the gap is by extrapolating from the known to the unknown. In June, Gerardo Ceballos at the National Autonomous University of Mexico — in collaboration with luminaries such as Paul Ehrlich of Stanford and Anthony Barnosky of the University of California, Berkeley — got headlines around the world when he used this approach to estimate that current global extinctions were “up to 100 times higher than the background rate.”

Ceballos looked at the recorded loss since 1900 of 477 species of vertebrates. That represented a loss since the start of the 20th century of around 1 percent of the 45,000 known vertebrate species. He compared this loss rate with the likely long-term natural “background” extinction rate of vertebrates in nature, which one of his co-authors, Anthony Barnosky of UC Berkeley recently put at two per 10,000 species per 100 years. This background rate would predict around nine extinctions of vertebrates in the past century, when the actual total was between one and two orders of magnitude higher.

Ceballos went on to assume that this accelerated loss of vertebrate species would apply across the whole of nature, leading him to conclude that extinction rates today are “up to a hundred times higher” than background.

A few days earlier, Claire Regnier, of the National Museum of Natural History in Paris, had put the spotlight on invertebrates, which make up the majority of known species but which, she said, currently “languish in the shadows.”

Regnier looked at one group of invertebrates with comparatively good records — land snails. And to get around the problem of under-reporting, she threw away the IUCN’s rigorous methodology and relied instead on expert assessments of the likelihood of extinction. Thus, she figured that Amastra baldwiniana, a land snail endemic to the Hawaiian island of Maui, was no more because its habitat has declined and it has not been seen for several decades. In this way, she estimated that probably 10 percent of the 200 or so known land snails were now extinct — a loss seven times greater than IUCN records indicate.

Extrapolated to the wider world of invertebrates, and making allowances for the preponderance of endemic land snail species on small islands, she concluded that “we have probably already lost 7 percent of described living species.” That could mean, she said, that perhaps 130,000 of recorded invertebrates have gone.

Several leading analysts applauded the estimation technique used by Regnier. But others have been more cautious about reading across taxa. They say it is dangerous to assume that other invertebrates are suffering extinctions at a similar rate to land snails. Mark Costello, a marine biologist of the University of Auckland in New Zealand, warned that land snails may be at greater risk than insects, which make up the majority of invertebrates. “Because most insects fly, they have wide dispersal, which mitigates against extinction,” he told me.

The same should apply to marine species that can swim the oceans, says Alex Rogers of Oxford University. Only 24 marine extinctions are recorded by the IUCN, including just 15 animal species and none in the past five decades. Some think this reflects a lack of research. But Rogers says: “Marine populations tend to be better connected [so] the extinction threat is likely to be lower.”

Whatever the drawbacks of such extrapolations, it is clear that a huge number of species are under threat from lost habitats, climate change, and other human intrusions. And while the low figures for recorded extinctions look like underestimates of the full tally, that does not make the high estimates right.

Can we really be losing thousands of species for every loss that is documented? Some ecologists believe the high estimates are inflated by basic misapprehensions about what drives species to extinction. So where do these big estimates come from?

Mostly, they go back to the 1980s, when forest biologists proposed that extinctions were driven by the “species-area relationship.” This relationship holds that the number of species in a given habitat is determined by the area of that habitat. The biologists argued, therefore, that the massive loss and fragmentation of pristine tropical rainforests — which are thought to be home to around half of all land species — will inevitably lead to a pro-rata loss of forest species, with dozens, if not hundreds, of species being silently lost every day. The presumed relationship also underpins assessments that as much as a third of all species are at risk of extinction in the coming decades as a result of habitat loss, including from climate change.

But, as rainforest ecologist Nigel Stork, then at the University of Melbourne, pointed out in a groundbreaking paper in 2009, if the formula worked as predicted, up to half the planet’s species would have disappeared in the past 40 years. And they haven’t. “There are almost no empirical data to support estimates of current extinctions of 100, or even one, species a day,” he concluded.

He is not alone. In 2011, ecologist Stephen Hubbell of UC Los Angeles concluded, from a study of forest plots around the world run by the Smithsonian Institution, that as forests were lost, “more species always remained than were expected from the species-area relationship.” Nature is proving more adaptable than previously supposed, he said. It seems that most species don’t simply die out if their usual habitats disappear. Instead they hunker down in their diminished refuges, or move to new habitats.

Claude Martin, former director of the environment group WWF International — an organization that in his time often promoted many of the high scenarios of future extinctions — now agrees that the “pessimistic projections” are not playing out. In his new book, On The Edge, he points out that El Salvador has lost 90 percent of its forests but only three of its 508 forest bird species. Meanwhile, the island of Puerto Rico has lost 99 percent of its forests but just seven native bird species, or 12 percent.

Some ecologists believe that this is a temporary stay of execution, and that thousands of species are living on borrowed time as their habitat disappears. But with more than half the world’s former tropical forests removed, most of the species that once populated them live on. If nothing else, that gives time for ecological restoration to stave off the losses, Stork suggests.

But we are still swimming in a sea of unknowns. For one thing, there is no agreement on the number of species on the planet. Researchers have described an estimated 1.9 million species (estimated, because of the risk of double-counting). But, allowing for those so far unrecorded, researchers have put the real figure at anywhere from two million to 100 million.

Last year Julian Caley of the Australian Institute of Marine Sciences in Townsville, Queensland, complained that “after more than six decades, estimates of global species richness have failed to converge, remain highly uncertain, and in many cases are logically inconsistent.”

That may be a little pessimistic. Some semblance of order is at least emerging in the area of recorded species. In March, the World Register of Marine Species, a global research network, pruned the number of known marine species from 418,000 to 228,000 by eliminating double-counting. Embarrassingly, they discovered that until recently one species of sea snail, the rough periwinkle, had been masquerading under no fewer than 113 different scientific names.

Costello says double-counting elsewhere could reduce the real number of known species from the current figure of 1.9 million overall to 1.5 million. That still leaves open the question of how many unknown species are out there waiting to be described. But here too some researchers are starting to draw down the numbers.

Back in the 1980s, after analyzing beetle biodiversity in a small patch of forest in Panama, Terry Erwin of the Smithsonian Institution calculated that the world might be home to 30 million insect species alone — a far higher figure than previously estimated. His numbers became the received wisdom. But new analyses of beetle taxonomy have raised questions about them.

In June, Stork used a collection of some 9,000 beetle species held at London’s Natural History Museum to conduct a reassessment. He analyzed patterns in how collections from particular places grow, with larger specimens found first, and concluded that the likely total number of beetle species in the world might be 1.5 million. From this, he judged that a likely figure for the total number of species of arthropods, including insects, was between 2.6 and 7.8 million.

Some researchers now question the widely held view that most species remain to be described — and so could potentially become extinct even before we know about them. Costello thinks that perhaps only a third of species are yet to be described, and that “most will be named before they go extinct.”

Does all this argument about numbers matter? Yes, it does, says Stork. “Success in planning for conservation … can only be achieved if we know what species there are, how many need protection and where. Otherwise, we have no baseline against which to measure our successes.” Or indeed to measure our failures.

None of this means humans are off the hook, or that extinctions cease to be a serious concern. Extinction rates remain high. And, even if some threats such as hunting may be diminished, others such as climate change have barely begun. Moreover, if there are fewer species, that only makes each one more valuable.

But Stork raises another issue. He warns that, by concentrating on global biodiversity, we may be missing a bigger and more immediate threat — the loss of local biodiversity. That may have a more immediate and profound effect on the survival of nature and the services it provides, he says.

Ecosystems are profoundly local, based on individual interactions of individual organisms. It may be debatable how much it matters to nature how many species there are on the planet as a whole. But it is clear that local biodiversity matters a very great deal.


Measuring Biodiversity

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Biodiversity. The word evokes the splendor of a great forest, or the teeming richness of the ocean, and is simply defined as the variety of organisms in an ecosystem of interest. To protect biodiversity, scientists must be able to measure it. This means figuring out how many different species are living together in a particular space. What is a convenient way to count species?

Trying to count everything in an entire ecosystem would be impossible, so scientists use a tool called the quadrat, which is a frame of fixed size placed randomly in the environment in which to do the counting. After cataloging the species and individuals found in this small section, the process is repeated, placing more quadrats at random, or alternatively, at set positions along a line through the environment, referred to as a transect.

In order to then estimate the total number of species in an area, species accumulation curves are used. If the cumulative number of species found in a quadrat are plotted against the number of quadrats sampled, a curve will emerge. For example, in this data set, when four quadrats were investigated, it was found that there were 10 unique species. Six contained 17 and so on. The asymptote of this type of curve represents an estimate of the number of species supported by an environment. In this case, it's about 30. But while measuring diversity at a single site is incredibly useful, comparing sites over a greater area can give us an even larger scale indication of diversity.

In 1972, the ecologist Robert Whittaker described three major kinds of biodiversity, alpha, beta, and gamma. Alpha diversity refers simply to the number of species in an area and is often referred to as species richness. For example, at this site there are seven different species, so the alpha score is seven. A second site, site B, has five species, and a third, site C, has seven. But by comparing between sites, we can determine what is called the beta diversity, the sum of species unique to each area. So if we compare site A with site B, we see three species in common between the two. Counting the remaining species, we find that there are six. This means that there is a beta diversity between site A and site B of six. Sites A and C also have three species in common, leaving eight unique ones. This is a beta diversity of eight. Sites B and C have two common species between them, or a beta diversity value of eight. Finally, gamma diversity is the number of different species in all sites combined. In this example, there is a gamma diversity of 12. So to summarize the three kinds of biodiversity, we can look at them this way, alpha, beta, and gamma. As well as recording diversity, scientists often refer to species evenness, meaning how many individuals of each type are present. For example, these two sites have the same richness, or alpha diversity, as they both have seven species. But site A is relatively overrun by rabbits with low numbers of the other species, whereas site B has a pretty even distribution of species, so it is considered to have greater evenness compared to site A. Scientists generally considered ecosystems with higher richness and evenness, i.e. many evenly distributed species, to be the healthiest. Disturbed habitats, often due to the actions of humans, like farming or pollution, often have poor richness and evenness. Being able to compare sites is critical because it allows researchers to determine the relative health of ecosystems.
In this laboratory, you will carry out quadrat and transect sampling at three different environmental sites, as well as carrying out a laboratory simulation, and then analyze the data collected to describe the observed biodiversity.

Diverse ecosystems are important for the health of the planet and our survival as humans it is therefore incredibly important for us to understand and measure biodiversity, which is defined as the variability among living organisms in an ecosystem. Biodiversity can be measured at many different levels including genetic, species, community, and ecosystem. One way to measure biodiversity is to assess species richness of an ecosystem, which is the total number of distinct species within a local community. While having many species generally coincides with having a diverse and healthy ecosystem, the evenness also needs to be considered. Evenness refers to the equality of the proportion of each species within an area or community. For instance, when one species dominates the area while the others are very rare, the biodiversity in this area is lower than in an area with species of equal abundance. Therefore, areas with many species that are relatively equal in abundance have the highest values of biodiversity.

Estimating Biodiversity

The differences in richness and evenness between two communities can be visualized by rank-abundance curves. If the number of species is equal, the shape of the line can tell us which community is more diverse. If the line is flat, there is high evenness among species. However, if the line quickly dips, the evenness is low. If richness and evenness are both different between two communities, biologists must use equations to calculate diversity. These equations weight the importance of each component differently, and a consensus on which equation is the best at calculating diversity is still debated.

Sometimes there are too many species in an area that it is unrealistic to count every single species. For example, a single tree in the Amazon Rainforest may contain hundreds of species of beetles. To circumvent this problem, ecologists use sampling tools called quadrats. A quadrat is simply a frame with a known internal area. For example, to measure the species richness of a one-acre field of grass, ecologists randomly place the quadrat in the field and count the species within the quadrat, instead of counting all of the species within the acre. They may also systematically sample by using transect tapes. Transects are stretched across the field, and quadrats are then placed along the transect at regular intervals. This method is semi-random and ensures ample coverage of sampling across the entire field to estimate its biodiversity.

While quadrats and transects may pick up most of the species, some rare species may go unnoticed. In this case, ecologists may use a species accumulation curve, which represents the cumulative number of species seen in a series of quadrats. The y-axis of the curve represents the total number of observed species, whereas the x-axis represents the number of quadrats for which species have been enumerated. The total number of species in the first quadrat represents the first point on the graph. Each successive point represents the number of new species found in each new quadrat sampled, plus all of the species from the previous quadrats. At some point, there will be few or no additional species found in each new quadrat sampled, and the curve will approach an asymptote, which is an estimate of the total number of species present. Even if the asymptote is never reached because of many rare species, biologists can estimate the total number based on this curve.

If comparisons need to be made among different areas or scales, alpha, beta, and gamma diversity measures are used. Alpha-diversity (α) refers to the number of species in an area. Beta-diversity (β) compares two different areas and is the sum of species unique to each area. Gamma-diversity (γ) is the number of species in many areas combined into a region. By using these measures, biologists can get an idea of diversity over space, including both small and large scales.

Threats to Biodiversity and their Implications

Biodiversity around the world is threatened by pollution, climate change, and invasive species. A main underlying reason for efforts to maintain biodiversity is based on ecosystem functioning. Ecosystems are made up of many working parts, including primary producers, herbivores, carnivores, and detritivores, all of which contribute to ecosystem function. If species are lost, the ecosystem may collapse. And if the ecosystem collapses, the services that it provides to humans will as well. Tropical coral reefs are a good example of this concept 1 . Spikes in water temperatures cause corals to lose their symbiotic algae cells. Without the algae, corals begin to starve, die, then degrade and lose their structure. When corals decay, they no longer provide cover for fish and the abundance of fish species declines, which in turn affects local fishermen, and the people that rely on fish for sustenance. Over time, dead coral reefs degrade on a larger scale and no longer provide a buffer for adjacent coastlines, eventually eroding the coast and destroying islands. A highly diverse community is less likely to collapse because of functional redundancy 2 . For example, corals may vary in their sensitivity to high temperatures. If one coral is extremely sensitive to temperature, another may take its place in the community, but if there are only a few species, it is less likely that such a substitute will be available.

A significant number of medicines that we benefit from are a direct result of the diversity of life. The medicines that we now synthesize were once isolated from animals, plants, fungi, and bacteria. There is a whole industry devoted to the discovery of new potential medicines by scanning various species for the presence of bioactive compounds. For example, plants produce chemicals for defense against infection and herbivores. Spiders and snakes produce diverse venoms. Both classes of organisms have been the source of important medicines, like Taxol from yew trees, which treats breast, lung and ovarian cancers, or Ohanin from King Cobra venom, which is a painkiller 3-4 . Each species that becomes extinct may hold the key to curing currently untreatable diseases. The faster we lose those species, the smaller the chance of discovering solutions.

Once a species goes extinct, we will never be able to experience them. This type of thinking has driven the conservation of pandas, sea otters, and other charismatic animals. These species are called flagship species, and their conservation can result in protection of biodiversity. Even though these animals are only a small part of the whole ecosystem, preserving them means preserving the ecosystem they occupy. Efforts to save the sea otter on the West Coast of North America have resulted in healthy kelp forests housing many thousands of other species 5 . Without protection of the sea otters, herbivores like sea urchins, which are usually eaten by the otters, are capable of completely devouring kelp forests leaving barren rocks where very few species could survive.

References

  1. Knowlton, Nancy. The future of coral reefs. PNAS. 2001, Vol. 98 , (10) 5419-5425.
  2. Andrea S. Downing, Egbert H. van Nes, Wolf M. Mooij, Marten Scheffer. The Resilience and Resistance of an Ecosystem to a Collapse of Diversity. PLoS One. . 2012 , Vol. 7(9): e46135.
  3. Wall, Monroe E. Camptothecin and taxol: Discovery to clinic. Med Res Rev. 1998, Vol. 18, 5 (299-314).
  4. Yuh Fen Pung, Peter T. H. Wong, Prakash P. Kumar, Wayne C. Hodgson, R. Manjunatha Kini. Ohanin, a Novel Protein from King Cobra Venom, Induces Hypolocomotion and Hyperalgesia in Mice. J Biol Chem. 2005, 280, 13137-13147.
  5. Estes, J.A., et al. Complex Trophic Interactions in Kelp Forest Ecosystems. Bulletin of Marine Science, Volume . 2004, Vol. 7, 3: 621-638.

This Map Shows You the Odds of Finding a New Species in Your Neighborhood

Almost a decade ago, researchers at Yale University launched a global database called Map of Life to track biodiversity distributions across the planet. Now, the team added a new feature to the database that predicts where species currently unknown to scientists may be hiding, reports Elizabeth Pennisi for Science.

The interactive map identifies biodiversity hotspots organized by vertebrate groups: birds, reptiles, amphibians and mammals. (Fish are not included in the map.) The team's work was published in the journal Nature Ecology and Evolution this week.

In 2018, ecologist Mario Moura of the Federal University of Paraíba in Brazil teamed up with Yale ecologist Walter Jetz, who spearheaded the initial creation of the Map of Life. The pair set out to identify where 85 percent of Earth's undiscovered species may be, Science reports. For two years, the team collected information about 32,000 vertebrate species. Data on population size, geographical range, historical discovery dates and other biological characteristics were used to create a computer model that estimated where undescribed species might exist today, reports Peter Dockrill for Science Alert.

The model found tropical environments in countries including Brazil, Indonesia, Madagascar, and Colombia harbor the most undiscovered species, Science Alert reports. The model also predicts that new species of amphibians and reptiles are the most undiscovered animals today, reports Science Alert. Smaller animals have limited ranges that may be inaccessible, making their detection more difficult. In contrast, larger animals that occupy greater geographic ranges are more likely to be discovered, the researchers explain in a statement.

"It is striking to see the importance of tropical forests as cradles of discoveries, reinforcing the urgent need to protect tropical forests and stop deforestation rates if we want a chance to truly discover our biodiversity," said Moura to Isaac Schultz for Gizmodo.

The map comes at a crucial time when Earth is facing a biodiversity crisis. In the Living Planet Index (LPI) constructed by the World Wildlife Fund and the Zoological Society of London to track biodiversity and species populations, it was reported that there was a 68 percent decrease in vertebrae species populations between 1970 and 2016. The report also noted a 94 percent decline in animal populations in the Americas' tropical subregions.

"At the current pace of global environmental change, there is no doubt that many species will go extinct before we have ever learned about their existence and had the chance to consider their fate," Jetz says in a statement. "I feel such ignorance is inexcusable, and we owe it to future generations to rapidly close these knowledge gaps."

The team is working on three other maps still in beta testing that estimate species richness and rarity, biodiversity facets, and discovery potential. The researchers note that the maps can be used as a conservation tool and used to prioritize investigation in areas that may be affected most by climate change, Science reports. The team also plans on expanding their map to cover plant, marine, and invertebrate species as well.

"We hope to motivate citizen scientists and biodiversity enthusiasts about the importance of species discovery and ignite discussions and agreements from those responsible for decision-making and conservation planning," Moura tells Gizmodo.

About Elizabeth Gamillo

Elizabeth Gamillo is a science journalist based in Milwaukee, Wisconsin. She has written for Science magazine as their 2018 AAAS Diverse Voices in Science Journalism Intern.


How do scientists know we've only discovered 14% of all living species?

EDIT: WOW, this got a lot more response than I thought. Thank you all so much!

There have been many different estimates given for the total number of species on planet Earth. Some estimates are mere educated guesses by experts, while others are more grounded in statistics. A famous estimate was provided by Terry Erwin, an entomologist working for the Smithsonian Institute. He sampled beetles from the Amazon basin by pumping insecticides into large rainforest trees and catching the dead insects that rained down into nets (this method is now called ɿogging'). Using these samples, he observed that many species of beetles were only found within a single species of tree. By sampling lots of different species of tree, he found that on average, each species of canopy tree had roughly 160 species of beetle that were only found on a single tree species. So then, estimating that there are about 50,000 species of canopy trees, he simply multiplied 160 x 50,000 to come up with 8 million. Since it is relatively well known that beetles make up approximately 25% of all described species on Earth, he then multiplied 8 million x 4 to come up with 32 million. This estimate received a lot of attention because of how large it was. It also received quite a lot of criticism, given the extrapolations that he used. For example, his estimate of 50,000 Amazon tree species is likely too high, and the number of endemic beetles per tree species is also highly variables from one tree species to the next. Today, most scientists think the Erwin estimate is probably too high.

There have thus been many other estimates provided by different groups over the years. A good one that comes to mind is described in a paper by Mora et al. 2011 in PlosONE (http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001127). The authors identify an important relationship that helped them to derive an accurate estimation of global species diversity. That is, there tends to be a linear relationship between the log number of taxonomic units found within different taxonomic hierarchies (i.e., from species, to genus, to family, to order, etc.). While we have a poor idea of the total number of species on Earth, we do have very good estimates for the total number of genera and families, etc. So, using these numbers, the authors simply plotted the number of taxonomic units found within all hierarchies above the species level (i.e., from genera to phylum). Using the linear model obtained from this procedure, they extrapolated their data to the species level and found the model to land on the number 8.7 million. Given the fact that about 1.2 million species have been described, 1.2/8.7 = 14%, bringing us to your original question.

This number is widely regarding as being a fairly accurate estimation of global species richness. Most biologists expect this number to be somewhere between 6 and 12 million now. However, it is important to point out that these estimates ignore microbes! We really don't have a clue what the diversity of prokaryotes looks like, so they are largely left out of these types of estimations. Advances in genomic sequencing will hopefully help us get closer to an answer, but we are still in the very early stages of developing techniques for describing microbial diversity.


A Globally Consistent Richness‐Climate Relationship for Angiosperms

Species richness, the simplest index of biodiversity, varies greatly over broad spatial scales. Richness‐climate relationships often account for >80% of the spatial variance in richness. However, it has been suggested that richness‐climate relationships differ significantly among geographic regions and that there is no globally consistent relationship. This study investigated the global patterns of species and family richness of angiosperms in relation to climate. We found that models relating angiosperm richness to mean annual temperature, annual water deficit, and their interaction or models relating richness to annual potential evapotranspiration and water deficit are both globally consistent and very strong and are independent of the diverse evolutionary histories and functional assemblages of plants in different parts of the world. Thus, effects of other factors such as evolutionary history, postglacial dispersal, soil nutrients, topography, or other climatic variables either must be quite minor over broad scales (because there is little residual variation left to explain) or they must be strongly collinear with global patterns of climate. The correlations shown here must be predicted by any successful hypothesis of mechanisms controlling richness patterns.


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