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Journal ArticleDOI

Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal

TLDR
The results reject the idea of corresponding maxima in endemic species and species richness in the lowlands tentatively deduced from Stevens’ elevational Rapoport effect, but hard-boundaries should be viewed as dynamic rather than static when broad-scale biogeographical patterns with a historical component are being interpreted.
Abstract
Aim Species richness and endemic richness vary along elevation gradients, but not necessarily in the same way. This study tests if the maxima in gamma diversity for flowering plants and the endemic subset of these plants are coherent or not. Location The study was conducted in Nepal, between 1000 and 5000 m a.s.l. Methods We used published data on distribution and elevational ranges of the Nepalese flora to interpolate presence between maximum and minimum elevations. Correlation, regression and graphical analyses were used to evaluate the diversity pattern between 1000 and 5000 m a.s.l. Results The interval of maximum species endemic to Nepal or the Himalayas (3800‐4200 m) is above the interval of maximum richness (1500‐2500 m). The exact location of maximum species density is uncertain and its accuracy depends on ecologically sound estimates of area in the elevation zones. There is no positive statistically significant correlation between log-area and richness (total or endemic). Total richness is positively correlated with log-area-adjusted, i.e. estimated area adjusted for the degree of topographic heterogeneity. The proportion of endemic species increases steadily from low to high elevations. The peak in endemism (c. 4000 m) corresponds to the start of a rapid decrease in species richness above 4000 m. This may relate to the last glacial maximum (equilibrium line at c. 4000 m) that penetrated down to 2500‐3000 m. This dynamic hard boundary may have caused an increase in the extinction rate above 4000 m, and enhanced the probability of isolation and facilitated speciation of neoendemics, especially among genera with a high proportion of polyploids. Main conclusions The results reject the idea of corresponding maxima in endemic species and species richness in the lowlands tentatively deduced from Stevens’ elevational Rapoport effect. They confirm predictions based on hard boundary theory, but hard-boundaries should be viewed as dynamic rather than static when broad-scale biogeographical patterns with a historical component are being interpreted.

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Journal ArticleDOI

The use of ‘altitude’ in ecological research

TL;DR: There are two categories of environmental changes with altitude: those physically tied to meters above sea level, such as atmospheric pressure, temperature and clear-sky turbidity; and those that are not generally altitude specific, suchAs moisture, hours of sunshine, wind, season length, geology and even human land use.
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The mid-domain effect and species richness patterns:what have we learned so far?

TL;DR: An overview of the 21 MDE studies published to date reveals a substantial signature of MDE in natural patterns and justifies continued work, and calls for assessment of Mde on an equal statistical footing with other candidate explanations for richness gradients.

ARTICLE Spatial species-richness gradients across scales: a meta-analysis

TL;DR: This paper surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent, and found that climate and productivity play an important role in determining species richness at large scales.
Journal ArticleDOI

Global analysis of bird elevational diversity

TL;DR: In this paper, the authors used a comprehensive set of bird elevational gradients to test the main drivers of diversity, including sampling, area, mid-domain effect, temperature, temperature and water availability, and hypotheses of evolutionary history.
References
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Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
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Journal ArticleDOI

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Robert H. Whittaker
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Book ChapterDOI

Detrended correspondence analysis: an improved ordination technique

TL;DR: DCA consistently gives the most interpretable ordination results, but as always the interpretation of results remains a matter of ecological insight and is improved by field experience and by integration of supplementary environmental data for the vegetation sample sites.
Journal ArticleDOI

Spatial Autocorrelation: Trouble or New Paradigm?

Pierre Legendre
- 01 Sep 1993 - 
TL;DR: The paper discusses first how autocorrelation in ecological variables can be described and measured, and ways are presented of explicitly introducing spatial structures into ecological models, and two approaches are proposed.
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