A synthesis of empirical plant dispersal kernels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F17%3A00478128" target="_blank" >RIV/67985939:_____/17:00478128 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/1365-2745.12666" target="_blank" >http://dx.doi.org/10.1111/1365-2745.12666</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/1365-2745.12666" target="_blank" >10.1111/1365-2745.12666</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A synthesis of empirical plant dispersal kernels

Popis výsledku v původním jazyce

We synthesized empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes. * A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height. We fitted 11 widely used probability density functions to each of the 168 data sets to provide a statistical description of the dispersal kernel. The exponential power (ExP) and log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the log-normal and Weibull performed poorly, while the 2Dt and power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the exponential performed better. No function was among the best-fitting for all data sets. * For 10 plant growth form/dispersal mode combinations for which we had >3 data sets, we fitted ExP and LogS functions across multiple data sets to provide generalized dispersal kernels. We also fitted these functions to subdivisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped data sets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process. Potential uses of our synthesis include the following: (i) choosing appropriate dispersal functions in mathematical models, (ii) selecting informative dispersal kernels for one's empirical study system, and (iii) using representative disper

Název v anglickém jazyce

A synthesis of empirical plant dispersal kernels

Popis výsledku anglicky

We synthesized empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes. * A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height. We fitted 11 widely used probability density functions to each of the 168 data sets to provide a statistical description of the dispersal kernel. The exponential power (ExP) and log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the log-normal and Weibull performed poorly, while the 2Dt and power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the exponential performed better. No function was among the best-fitting for all data sets. * For 10 plant growth form/dispersal mode combinations for which we had >3 data sets, we fitted ExP and LogS functions across multiple data sets to provide generalized dispersal kernels. We also fitted these functions to subdivisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped data sets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process. Potential uses of our synthesis include the following: (i) choosing appropriate dispersal functions in mathematical models, (ii) selecting informative dispersal kernels for one's empirical study system, and (iii) using representative disper

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Ecology

ISSN

0022-0477

e-ISSN

—

Svazek periodika

105

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

6-19

Kód UT WoS článku

000390331000002

EID výsledku v databázi Scopus

2-s2.0-85006173289