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Human Populations in the biodiversity hotspots

Authors:
  • Population Institute

Abstract and Figures

Biologists have identified 25 areas, called biodiversity hotspots, that are especially rich in endemic species and particularly threatened by human activities. The human population dynamics of these areas, however, are not well quantified. Here we report estimates of key demographic variables for each hotspot, and for three extensive tropical forest areas that are less immediately threatened. We estimate that in 1995 more than 1.1 billion people, nearly 20% of world population, were living within the hotspots, an area covering about 12% of Earth's terrestrial surface. We estimate that the population growth rate in the hotspots (1995-2000) is 1.8% yr(-1), substantially higher than the population growth rate of the world as a whole (1.3% yr(-1)) and above that of the developing countries (1.6% yr(-1)). These results suggest that substantial human-induced environmental changes are likely to continue in the hotspots and that demographic change remains an important factor in global biodiversity conservation. The results also underline the potential conservation significance of the continuing worldwide declines in human fertility and of policies and programs that influence human migration.
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Acknowledgements
We thank the HSDP team for providing samples. We thank S. Simakin for ion probe
analyses of inclusions; L.V. Danyushevsky for the access to PETROLOG thermodynamic
modelling software; S.V. Sobolev for modelling phase compositions at high T-P;
P. Kelemen for providing unpublished data on Oman Gabbro; E. Macsenaere-Riester for
help with the electron microprobe analyses; F. Ku
Ènstler for preparing doubly polished
sections; and F. Frey, J. Eiler, L.V. Danuyshevsky, V. S. Kamenetsky, A. A. Gurenko and
S. R. Hart for comments that helped to improve the clarity of the manuscript. This work
was supported by Deutsche Forschungsgemeinschaft and the Russian Foundation of Basic
Research (A.V.S. and I.K.N.) and an Alexander von Humboldt award (A.V.S.).
Correspondence and requests for materials should be addressed to A.V. S.
(e-mail: asobolev@mpch-mainz.mpg.de)
.................................................................
Human population in the
biodiversity hotspots
Richard P. Cincotta, Jennifer Wisnewski & Robert Engelman
Research Department, Population Action International, 1300 19th Street, NW,
2nd Floor, Washington DC 20036, USA
..............................................................................................................................................
Biologists have identi®ed 25 areas, called biodiversity hotspots,
that are especially rich in endemic species and particularly
threatened by human activities. The human population dynamics
of these areas, however, are not well quanti®ed. Here we report
estimates of key demographic variables for each hotspot, and for
three extensive tropical forest areas1that are less immediately
threatened. We estimate that in 1995 more than 1.1 billion people,
nearly 20% of world population, were living within the hotspots,
an area covering about 12% of Earth's terrestrial surface. We
estimate that the population growth rate in the hotspots (1995±
2000) is 1.8% yr
-1
, substantially higher than the population
growth rate of the world as a whole (1.3% yr
-1
) and above that
of the developing countries (1.6% yr
-1
). These results suggest that
substantial human-induced environmental changes are likely to
continue in the hotspots and that demographic change remains an
important factor in global biodiversity conservation. The results
also underline the potential conservation signi®cance of the
continuing worldwide declines in human fertility and of policies
and programs that in¯uence human migration.
In 1988, ecologist Norman Myers introduced the term `biodi-
versity hotspots' to distinguish a global set of high-priority terres-
trial ecoregions for conservation2. Myers and others argue that,
because their 25 hotspots are high in species endemism and low in
pristine vegetation (,30% remaining), wise conservation invest-
ments in these ecoregions could help minimize future extinctions2±4.
Primatologist Russell Mittermeier subsequently developed a com-
plementary concept, the `major tropical wilderness areas'5. These
three areas of tropical forest (Upper Amazonia/Guyana Shield, the
Congo Basin, and the New Guinea/Melanesian Islands) are the most
pristine of all terrestrial ecoregions exhibiting a high degree of
species endemism. Together they cover 6.3% of Earth's terrestrial
surface, an area larger than the United States or China. Myers,
Mittermeier and others propose a strategy of conservation invest-
ments in these areas as a back-up strategy for the hotspot approach1.
By using mapped world distributions of humans (Fig. 1), various
census sources and ecoregional boundary data, we calculated
population density and growth rates for each of the biodiversity
hotspots and major tropical wilderness areas (see Methods).
We estimate that in 1995 population density in the hotspots was
73 people km
-2
, a ®gure 71% greater than that of the world as a
whole (excluding ice- or rock-covered land). We found that 16 of
the 25 hotspots (Fig. 2a) have population densities at or above the
world average (42 people km
-2
). According to our estimates, from
1995 to 2000, human population was still growing in all but one of
the hotspots (the Caucasus), with 19 of the hotspot populations
300
150–300
50–150
15–50
5–15
1–5
0–1
Wilderness areas
Biodiversity hotspots
Population density (km–2)
Figure 1 World population density (1995) and the 25 biodiversity hotspots (outlined in
red, numbered), and three major tropical wilderness areas (outlined in green, lettered).
Hotspots: (1) Tropical Andes; (2) Mesoamerica; (3) Caribbean; (4) Atlantic Forest Region;
(5) Choco
Â-Darie
Ân-Western Ecuador; (6) Brazilian Cerrado; (7) Central Chile; (8) California
Floristic Province; (9) Madagascar; (10) Eastern Arc Mountains and Coastal Forests of
Tanzania and Kenya; (11) West African Forests; (12) Cape Floristic Region; (13) Succulent
Karoo; (14) Mediterranean Basin; (15) Caucasus; (16) Sundaland; (17) Wallacea; (18)
Philippines; (19) Indo-Burma; (20) Mountains of South-Central China; (21) Western Ghats
and Sri Lanka; (22) Southwest Australia; (23) New Caledonia; (24) New Zealand; and (25)
Polynesia and Micronesia. Major tropical wilderness areas: (A) Upper Amazonia and
Guyana Shield; (B) Congo River Basin; and (C) New Guinea and Melanesian Islands.
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growing more rapidly than that of the world as a whole (Fig. 2b).
Although population growth rates were, in general, highest in the 19
hotspots wholly within developing countries, growth rates in the
hotspots within developed countries were in most cases substan-
tially higher than the worldwide average for developed regions
(0.3% yr
-1
).
In 1995, nearly 75 million people (1.3% of world population)
were living within the three major tropical wilderness areas,
representing an average density of about 8 people km
-2
. (Area
boundaries enclose several major cities.) These areas are experien-
cing population growth at a rate of 3.1% yr
-1
, which is more than
twice the global rate.
If population numbers are examined in isolation of other factors,
the three hotspots with the most elevated risks, as assessed by high
human population density, are the Western Ghats/Sri Lanka,
Philippines and Caribbean hotspots. Choco
Â-Darie
Ân-Western Ecua-
dor, Tropical Andes and Madagascar head a list of hotspots facing
elevated risks on the basis of rapid population growth alone.
Notably, the latest hotspot analysis by Mittermeier et al. concludes
that the Philippines, Caribbean and Madagascar hotspots appear to
be the highest-priority of these ecoregions on the basis of their
extreme endemism and the intense packing of species into a much
reduced area of original vegetation6.
Human population variables are imperfect indicators of risk
to biodiversity. Population density ®gures, for example, obscure
patterns of population distribution within areas. Roughly 90% of
the population of the southwest Australia hotspot lives in and
around Perth, a single metropolitan area covering less than 2% of
the ecoregion; however, such uneven distribution does not negate
risk to biodiversity. There is considerable evidence of the capacity of
urban populations to alter ecosystems, which are sometimes more
than 100 km away, through demand for wood fuel (principally in
developing countries), water, food (including wild foods), waste
disposal and recreation (mostly in developed countries)7,8.
Another problem is that disturbance caused by humans can occur
in the absence of widespread human settlement. This is the frequent
result of over-logging, burning, grazing, mining and commercial
hunting that have extracted or degraded natural resources, abetted
biological invasion or polluted soil and water resources9. Population
density remains low, for example, in the most arid hotspot, the
Succulent Karoo, which experiences heavy grazing and the over-
harvesting of its ¯ora for the international trade in ornamental
plants.
Population growth rates can also be misleading indicators of risk
to biodiversity. Because growth rates are calculated as the annual
percentage change in a population, low rates of growth in dense
populations add more individuals than much higher rates of growth
in sparse populations. Population growth rates mask spatial dis-
tributions of growth and the trend of that rate. And both density
and growth rates hide the culture, af¯uence and technology of the
numbers of people they represent.
Despite these caveats, however, population trends in the biodi-
versity hotspots and major tropical wilderness areas indicate a high
risk that habitats will continue to degrade as ecosystems dominated
by humans expand and species become extinct in the world's most
biologically diverse terrestrial regions. Results of the analysis also
suggest that, whatever species conservation strategies ultimately
emerge, conservation scientists and policymakers should take
human population dynamics into account. Especially relevant are
trends and potential changes in population growth, density and
migration, and the social and economic factors known to in¯uence
population variables. One hopeful sign for the conservation of
biodiversity is that declines in human fertility are gradually slowing
population growth worldwide. M
Methods
Population density
Weestimated population densities for biodiversity hotspots and major tropical wilderness
areas (ecoregions) using the Gridded Population of the World, 1995, a geographic
information systems (GIS) layer developed by geographers at the National Center for
Geographic Information and Analysis, University of California, Santa Barbara10. The
authors of this layer call attention to numerous sources of potential error in these data,
including extrapolation from census-year estimates to 1995 projections, the mapping of
census geographical boundaries and census estimates themselves. For census data in most
industrialized countries, demographers regularly assume an error (most often an under-
count) of less than 3% of the actual population11. Errors exceeding 10% occasionally occur
in censuses in the poorest countries, particularly those experiencing political instability.
Moreover the populations enumerated vary from country to country, with some countries
including, for example, military personnel living outside the country.
Population growth
We partitioned hotspots into countries and sub-national political divisions (provinces or
states), used available growth rates or census and projection data to determine the growth
of each unit, and then calculated average growth rates for the composite ecoregion. Data
on a provincial level were used in ecoregions covering parts of Argentina, Australia, Brazil,
Bolivia, Colombia, China, Ecuador, France, India, Indonesia, Mexico, Panama, Peru,
South Africa, Spain, Turkey, United States and Venezuela. Where provincial data were
unavailable or unnecessary (where the entire country fell within the hotspot), country
population growth rates were obtained from estimates generated by the United Nations
Population Division12. These United Nations data are also the source for 1995 world
population density and 1995± 2000 world population growth rates.
Received 20 October 1999; accepted 17 February 2000.
1. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots
for conservation priorities. Nature 403, 853±858 (2000).
2. Myers, N. Threatened biotas: hotspots in tropical forests. Environmentalist 8, 178 ±208 (1988).
-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
C
B
A
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
Population growth rate (% yr
–1
)
World population
growth rate,
1995–2000
0 50 100 150 200 250 300 350
C
B
A
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
Population density (km
–2
)
World population
density, 1995
a
b
Figure 2 Human population densities (a) and annual growth rates (b) in the 25 global
biodiversity hotspots (1± 25; see map for names and locations, Fig. 1)and the three major
tropical wilderness areas (A, B, C).
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3. Myers, N. The biodiversity challenge: expanded hot-spot analysis. Environmentalist 10, 243± 256
(1990).
4. Mittermeier, R. A., Myers, N., Thomsen, J. B., da Fonesca, G. A. B.& Olivi eri, S.Biodiversity hotspots
and major tropical wilderness areas: approaches to setting conservation priorities. Conserv. Biol. 12,
516±520 (1998).
5. Mittermeier, R. A. in Biodiversity (eds Wilson, E. O.& Peter, F. M.)145 ±154 (National Academy Press,
Washington, DC, 1988).
6. Mittermeier, R. A., Myers, N., Robles Gil, P. & Mittermeier, C. G. Hotspots: Earth's Biologically Richest
and Most Threatened Ecosystems (Cemex, Mexico, D.F., 1999).
7. Repetto, R. The ``Second India'' Revisited: Population, Poverty, and Environmental Stress Over Two
Decades (World Resources Institute, Washington, DC, 1994).
8. Myers, N. in PopulationÐ The Complex Reality (ed. Graham-Smith, F.) 117±135 (Royal Society,
London, 1994).
9. Stedman-Edwards, P. The Root Causes of Biodiversity Loss: An Analytical Approach (Worldwide Fund
for Nature, Washington, DC, 1997).
10. Tobler, W., Deichmann, U., Gottsegen, J. & Maloy, K. The Global Demography Project Tech. Rep. No.
95±6 (National Center for Geographic Information Analysis, Univ. California, Santa Barbara, 1995).
11. Newman, J. L. & Matzke, G. E. Population: Patterns, Dynamics, and Prospects (Prentice-Hall,
Englewood Cliffs, 1984).
12. U.N. Population Division World Population Prospects: the 1998 Revision (United Nations, New York,
1998).
Acknowledgements
We thank A. Bornbusch, D. Blockstein, F. Meyerson, R. Mittermeier, N. Myers and
D. Sperling for comments on the manuscript, and K. Sebastian and M. Bartels for solving
numerous GIS problems encountered during this research.
Correspondence and requests for materials should be addressed to R.P.C.
(e-mail: cincotta@popact.org).
.................................................................
Identi®cation of sleep-promoting
neurons in vitro
Thierry Gallopin*²³, Patrice Fort²³, Emmanuel Eggermann*³,
Bruno Cauli§, Pierre-Herve
ÂLuppi², Jean Rossier§, Etienne Audinat§,
Michel Mu
Èhlethaler*& Mauro Sera®n*
*De
Âpartement de Physiologie, Centre Me
Âdical Universitaire, 1 rue Michel-Servet,
1211 Gene
Áve 4, Switzerland
²Neurobiologie des Etats de Sommeil et d'Eveil, 8 avenue Rockefeller, 69373, Lyon,
cedex 08, France
§Laboratoire de Neurobiologie et Diversite
ÂCellulaire, CNRS UMR 7637, ESPCI,
10 rue Vauquelin, 75005, Paris, France
³These authors contributed equally to this work
..............................................................................................................................................
The neurons responsible for the onset of sleep are thought to be
located in the preoptic area1±3 and more speci®cally, in the
ventrolateral preoptic nucleus (VLPO)4±6. Here we identify
sleep-promoting neurons in vitro and show that they represent
an homogeneous population of cells that must be inhibited by
systems of arousal during the waking state. We ®nd that two-
thirds of the VLPO neurons are multipolar triangular cells that
show a low-threshold spike. This proportion matches that of cells
active during sleep in the same region6. We then show, using
single-cell reverse transcriptase followed by polymerase chain
reaction, that these neurons probably contain g-aminobutyric
acid (GABA). We also show that these neurons are inhibited by
noradrenaline and acetylcholine, both of which are transmitters
of wakefulness3,7,8. As most of these cells are also inhibited by
serotonin but unaffected by histamine, their overall inhibition by
transmitters of wakefulness is in agreement with their relative
inactivity during waking with respect to sleep6. We propose
that the reciprocal inhibitory interaction of such VLPO
neurons with the noradrenergic, serotoninergic and cholinergic
waking systems to which they project5,9,10 is a key factor for
promoting sleep.
Intracellular recordings in slices revealed only two cell types
within the VLPO. Of 102 recorded cells, most (n= 70, 68.6%)
were characterized by a potent low-threshold spike (LTS)11 (asterisk
and inset in Fig. 1a, LTS cells) that was calcium dependent, as it
persisted in tetrodotoxin ( TTX, 1 mM) and was eliminated (n=3)
by nickel (200 ±500 mM). However, we found no evidence for an
intrinsic rhythmicity driven by the LTS11 in these cells. The second,
less numerous cell type (n= 32, 31.4%) lacked an LTS (Fig. 1b, non-
LTS cells) and was usually characterized by a more or less prominent
recti®cation apparent upon depolarization from a hyperpolarized
level (Fig. 1b, arrow). Basic membrane parameters, such as resting
potential, membrane input resistance and action potential width
did not differ between the two cell types. Injection of the intracel-
lular tracer neurobiotin into VLPO neurons indicated that whereas
both cell types were medium-sized (LTS cells, n= 14; mean large
diameter 6s.d., 19.1 62.0 mm; mean small diameter, 13.46
1.3 mm; Fig. 1c, d; non-LTS cells, n= 6; 21.3 63.1 mm versus
11.8 61.3 mm, respectively; Fig. 1e, f), their shapes and dendritic
arbours were completely different. All LTS cells were triangular
(Fig. 1d) and multipolar (mean number of primary dendrites: 3.0
60.0, n= 14), whereas non-LTS cells were fusiform (Fig. 1f) and
bipolar (1.8 60.4, n= 6).
The high percentage (68%) of LTS cells in the VLPO matches that
of cells active during sleep in this region4,6 and indicates that the LTS
cells may correspond to these sleep-active cells. To test this proposal
we measured the effects of noradrenaline, an important transmitter
of wakefulness3,7,8, and found that 18 out of 20 LTS cells (Fig. 2a, c)
were hyperpolarized by noradrenaline (two were depolarized),
whereas all (n= 8) non-LTS cells were depolarized (Fig. 2b, c).
These results indicate that the LTS cells in the VLPO should be
inhibited during waking, when noradrenaline is preferentially
released3,7,8, and thus are well suited to correspond to the sleep-
active cells recorded in vivo1,12,13. Non-LTS cells, in contrast, are not
well quali®ed for that role and will not be considered further here.
The results described above were obtained from intracellular
recordings using sharp electrodes. We wanted to test whether
VLPO cells are inhibited by noradrenaline in a condition closer to
the in vivo situation, that is, with minimal perturbation of the cells'
properties. We therefore used infrared videomicroscopy14 to record
extracellularly from VLPO triangular multipolar neurons (Fig. 3a)
in a loose-attached cell con®guration15. All neurons (n= 9) tested
in this way were inhibited by noradrenaline (Fig. 3b, c). We then
tested whether neurons inhibited by noradrenaline were also
inhibited by acetylcholine, another important transmitter of
arousal3,7,8; in every case (n= 5), these neurons were inhibited by
both transmitters (Fig. 3d, e). In addition, the effects of both
transmitters were postsynaptic, as they persisted (n=2)inahigh
magnesium (10 mM)/low calcium (0.1 mM) solution.
We also investigated the two other transmitters (serotonin and
histamine) usually associated with arousal3,8. Serotonin (100 mM, n
= 10), like acetylcholine, inhibited the majority of cells (7 out of 10)
previously inhibited by noradrenaline (Fig. 3f, g) and excited only a
minority (3 out of 10). Both effects persisted (respectively, n=2
and n= 1) in a high magnesium/low calcium solution. In contrast,
histamine (100 mM, n= 5), which was also tested on neurons
inhibited by noradrenaline, had no inhibitory or excitatory effect
(not shown).
To establish the possible functional role of the LTS cells we needed
to identify their neurotransmitter. Most of the VLPO cells, retro-
gradely labelled from the histaminergic tuberomammillary
nucleus5, the noradrenergic locus coeruleus9or the cholinergic
magnocellular preoptic nucleus10, are immunoreactive to glutamic
acid decarboxylase (GAD) and thus contain GABA. We investigated
the expression of GAD in LTS cells using single-cell reverse tran-
scriptase followed by polymerase chain reaction (RT±PCR)16± 19.In
addition to GAD65 and GAD67, the synthesizing enzymes for
GABA, we examined the expression of choline acetyltransferase
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An updated list confirms the presence of 134 species of wild mammals in the Western Ghats, India. The superimposed distribution range of all, and threatened species of mammals depicts the potential mammalian key diversity areas for the Western Ghats, which can be prioritized for long-term conservation. These mammalian key diversity areas are confined to the central and the southern Western Ghats. The most crucial key diversity areas for both threatened and all mammalian species occur in Pushpagiri-Talakaveri, the Nilgiri Biosphere, the Anamalai Hills, the Periyar landscape and the Agas-tyamalai Hills.
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Western Ghats of India is one of the global biodiversity hotspot that seeks extreme care and conservation. Unique geography and climatic condition in different locations of Western Ghats entertain the existence of a number of endemic species that are under extreme threat. Study of microbial diversity in biodiversity hotspots is a challenging field. The difficulties associated with sample collection, culturing of microbes, identification and metabolite purification renders the microbial pool of biodiversity hotspots untouched. This chapter aimed to summarize the microbial diversity of Indian Western Ghats and their potential applications in various fields. Instead of reviewing the microbial diversity of Western Ghats, this chapter provides a complete guide on the tools and techniques involved in exploration of microflora of any biodiversity hotspots. A number of sampling techniques, microbial enumeration and identification methods have been discussed in which Metagenomics approach and library construction is a turning point in the study of cultivable and non-cultivable microflora of an ecosystem. With the advanced techniques like flow cytometry, microautoradiography and the use fluorescent probes, the metabolic status of the microbial population can be monitored in situ. Molecular identification techniques such as Polymerase Chain Reaction (PCR), sequencing and Restriction Fragment Length Polymorphism (RFLP) are useful for the phylogenetic analysis and species identification. Special focus was given to microbial enzymes, industrial pollutants degrading microbes, pesticide degraders, quorum sensing and quorum quenching molecules reported so far from Western Ghats. The pieces of information provided in this chapter signify the applications of unique microflora of Western Ghats and importance of conservation.KeywordsWestern GhatsMicrobial diversityBiodiversityPGPR
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The peninsular bound rivers in the state of Bihar join the median principal drainage, River Ganga along its south bank flowing from south to north. These emanate mostly from the rocky uplands of the Chotanagpur Gneissic Complex, a few from the Vindhyans, Bihar Mica Belt, Gondwanas and other geological formations. Amongst them, River Falgu has its emergence from the Chotanagpur Gneissic Complex and is a promising repository for sand used for construction purposes. Understandably, there is a composite licence awarded by the state government to harness the sand potential of the river along its fertile stretches which is sometimes unchecked from the point of view of mining complying with the requisites of existing guidelines. The region which experiences hot and humid climate conditions has suffered irreversible vagaries in the past few decades with a conspicuous decrease in the overall precipitation, thus influencing the groundwater conditions. The recent past is replete with climatic surprises from unexpected rainfall in a couple of seasons to intimidating drought-like conditions in others. Undoubtedly, these events cannot be segregated from short-term climatic changes which are now affecting different parts of the country with a certain amount of regularity. This has influenced the sedimentation pattern and carrying capacity of the river. The aspect of sedimentation in a stretch of one of the peninsular bound rivers viz. Phalgu, originating from Chotanagpur Gneissic Complex (CGC) and debouching into the River Ganga near Patna district, forms the soul of the present study in light of fluctuation in sedimentation pattern, anthropogenic interference (particularly aberrations in sand mining), the effect of cross drainage structures (viz. barrage, bridge, weir) and diversion of the streamflow for various irrigational purposes which together or in isolation have greatly influenced the ecological health of the river. The study has been carried out on 11 no Sand Mining ‘Ghats’ officially identified as lease areas for minor minerals in Jehanabad district, Bihar along the course of River Phalgu. Studies reveal that anthropogenic activities are significantly affecting the river bed morphology by influencing sediment supply and transportation in the river basin. Transportation of sediment is the predominant work of the river through various hydrological processes such as a solution, suspension, saltation and traction under highly fluctuating hydrodynamic conditions, but decreased precipitation has affected the carrying capacity of the river. This chapter deals with the tight rope walking in arriving at the best possible balance resulting from environmental safety guideline procedures involved in mining, safeguarding the ecology of the river amidst challenges and ways to minimize the unprecedented effects of short-term climatic change. It also highlights aspects like drivers responsible for the reduced flow, erratic modes of sand deposition vis-a-vis undisciplined extraction and recommendations towards sustainable exploitation in tune with the expectation and guidelines served by the Hon’ble National Green Tribunal in this case along Phalgu River, in Jehanabad district, Bihar.
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Several studies have assessed droughts and vegetation considering climatic factors, particularly El Niño-Southern Oscillation (ENSO) at different latitudes. However, there are knowledge gaps in the tropical Andes, a region with high spatiotemporal climatic variability. This research analyzed the relationships between droughts, vegetation, and ENSO from 2001–2015. Meteorological drought was analyzed using the Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3 and 6 months. Normalized Difference Vegetation Index (NDVI) was used to evaluate vegetation, and ENSO indexes were used as climate drivers. The Wavelet coherence method was used to establish time-frequency relationships. This approach was applied in the Machángara river sub-basin in the Southern Ecuadorian Andes. The results showed significant negative correlations during 2009–2013 between the SPEI and NDVI, with the SPEI6 lagging by nine months and a return period of 1.5 years. ENSO–SPEI presented the highest negative correlations during 2009–2014 and a return period of three years, with ENSO leading the relationship for around fourteen months. ENSO-NDVI showed the highest positive correlations during 2004–2008 and a return period of one year, with the ENSO indexes continually delayed by approximately one month. These results could be a benchmark for developing advanced studies for climate hazards.
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Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify 'biodiversity hotspots' where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a 'silver bullet' strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.
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This paper aims to throw light on the mass extinction that is overtaking Earth's species. Using an analytic methodology developed for an earlier partial assessment, it focuses on a series of "hotspot" areas, these being areas that a) feature exceptional concentrations of species with high levels of endemism and b) face exceptional threats of destruction. The paper identifies another 8 such areas, 4 of them in tropical forests and 4 in Mediterranean-type zones. The analysis reveals that the 4 tropical-forest areas contain at least 2835 endemic plant species in 18,700 sq. km, or 1.1% of Earth's plant species in 0.013% of Earth's land surface; and that the 4 Mediterranean-type areas contain 12,720 endemic plant species in 435,700 sq. km, or 5.1% of Earth's plant species in 0.3% of the Earth's land surface. Taken together, these 8 hotspot areas contain 15,555 endemic plant species in 454,400 sq. km, or 6.2% of Earth's plant species in 0.3% of Earth's land surface. This is to be compared with the earlier hotspot analysis of 10 tropical-forest areas, with 34,400 endemic plant species in 292,00 sq. km, or 13.8% of Earth's plant species in 0.2% of Earth's land surface. Taking all 18 hot-spot areas together, the authors find they support 49,995 endemic plant species, or 20% of Earth's plant species, in 746,400 sq. km, or 0.5% of Earth's land surface. This means that one-fifth of Earth's plant species are confined to 0.5% of the Earth's land surface--and they occur in habitats that are mostly threatened with imminent destruction. By concentrating on these hotspot areas where needs are greatest and where the pay-off from safeguard measures would be greatest, conservationists can engage in a more systematized response to the challenge of large-scale impending extinctions.
Revisited: Population, Poverty, and Environmental Stress Over Two Decades (World Resources Institute
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Tropical Andes; (2) Mesoamerica; (3) Caribbean; (4) Atlantic Forest Region
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Hotspots: (1) Tropical Andes; (2) Mesoamerica; (3) Caribbean; (4) Atlantic Forest Region;
The Root Causes of Biodiversity Loss: An Analytical Approach (Worldwide Fund for Nature
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Stedman-Edwards, P. The Root Causes of Biodiversity Loss: An Analytical Approach (Worldwide Fund for Nature, Washington, DC, 1997).
Myers and D. Sperling for comments on the manuscript, and K. Sebastian and M. Bartels for solving numerous GIS problems encountered during this research. Correspondence and requests for materials should be addressed to R
  • Acknowledgements We
  • A Bornbusch
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Acknowledgements We thank A. Bornbusch, D. Blockstein, F. Meyerson, R. Mittermeier, N. Myers and D. Sperling for comments on the manuscript, and K. Sebastian and M. Bartels for solving numerous GIS problems encountered during this research. Correspondence and requests for materials should be addressed to R.P.C. (e-mail: cincotta@popact.org).
Hotspots: Earth's Biologically Richest and Most Threatened Ecosystems
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Mittermeier, R. A., Myers, N., Robles Gil, P. & Mittermeier, C. G. Hotspots: Earth's Biologically Richest and Most Threatened Ecosystems (Cemex, Mexico, D.F., 1999).