Top
ヘッダー

(Upload on March 5 2018) [ 日本語 | English ]

Flora (フローラ)






Mount Usu / Sarobetsu post-mined peatland
From left: Crater basin in 1986 and 2006. Cottongrass / Daylily

Flora (pl. floras or florae) (植物相)
The plant species found in one or more regions, or eras (Dunster & Dunster 1996)
Fauna (動物相)
The animal species found in one or more regions, or eras

How to use (Examples)

  • Flora of Japan
  • Flora of Hokkaido
  • Flora of Sapporo
  • Flora of Mount Usu
  • Flora of lawn in the campus of Hokkaido University
Flora lists in this site

Mount Usu, Mount Koma, Chise-Frep in Shihoro, Plant species in Washington

索引

Biota (生物相)

= fauna + flora

Ex. land biota, benthic biota, African biota
biosphere (生物圏)


Case: Characteristics of flora on Mount Koma

Based on researches in 2000

Seventy-five seed plant species (露崎他 2001)
Common species on mountainous, volcanic areas
+
(Specimen collected from the mountain)
Coastal plant species

Ex. Chimaphila umbellata (L.) W. Barton: collected from the southwestern slope at ca 500 m in elevation at at August 3, 2000. This species generally establish in forests near sea coast.

Alpine and/or subalpine plant species

Ex. Campanula lasiocarpa Cham.: collected from the southwesten slope at August 3, 2000. This species is an alpine plant.

Carnivorous plant species

Ex. Drosera rotundifolia L.: collected from the southwestern slope at ca 850 m in elevation at August 13, 2000. This species is a typical carnivorous plant.

These specimens have been stored in the Hokkaido University Herbarium.

What we can say from the flora on Mount Koma

  1. We have to consider the effects of sea coast flora on vegetation.
  2. Tree line is depressed, perhaps due to volcanic activities.
  3. Soil nutrients have been low, and thus carnivorous plants may establish.

[Geography, Island biogeography, GIS]

Biogeography (生物地理学)


(Fattorini 2015)

Chorotype
  1. global chorotype: groups of species with overlapping ranges (overall distributions)
  2. regional chorotype: a similar distribution within a certain area

Animal

A. Continental distribution type
a. Continental chain
b. Distribution pattern of taxa over species Wallace
Fig. The terrestrial zoogeographic realms and regions of the world. Zoogeographic realms and regions are the product of analytical clustering of phylogenetic turnover of assemblages of species, including 21,037 species of amphibians, nonpelagic birds, and nonmarine mammals. Dashed lines delineate the 20 zoogeographic regions identified in this study. Thick lines group these regions into 11 broad-scale realms, which are named. Color differences depict the amount of phylogenetic turnover among realms. Dotted regions have no species records, and Antarctica was not included in the analyses. (Holt et al. 2013)
B. Geographical distribution (地理分布)
most popular one is Wallace
Huxley (1868): 4 regions
  1. Arctogaea (北界): Arctogaea
  2. Notogaea (南界)
    • Austro-Columbia (中南米)
    • Austral-Asia
    • New Zealand
Wallace (1876): 24 subregions in 6 regions
  1. Palaearctic Region (旧北区).: European subregion, Mediterranean, Siberian, Manchurian
  2. Ethiopian Region (エチオピア区).: East-African, West African, South-African, Malagasy (Madagascar)
  3. Oriental Region (東洋区): Hindostan (中央インド), Ceylon, Indo-Chinese (Himalayan), Indo-Malayan
  4. Austrasian Region (オーストラリア区): Austro-Malayan, Australian, Polynesian, New Zealand
  5. Neotropical Region (新熱帯区): Brazilian, Chihan (S. Temperate American), Mexican, Antillean
  6. Nearctic Region (新北区): Californian, Rocky Mountain, eastern Alleganian, Canadian

→ Japan is located on a boundary between Oriental and Palaearctic Region

Wallace
Fig. The original zoogeographical realms.

Wallace Line: the boundary between Oriental and Austrasian Regions

Dahl (1925): nine subregions in four regions
  1. Arctogean Realm: Arctic Province, Euro-Mediterranean, East-Asian, Snoran
  2. Etlhiopian R.: West-African, South-African, East African, Madagascarene
  3. Indo-Australian R.: Indian, Malayan, Papuan, Australian, Nove-Zealandian, Polynesian, Hawaiian
  4. Neogean R.: Central American, Antillean, Brazilian, Chilian

Body size on animals

Bergmann's rule (ベルクマンの法則)
Bergmann, Carl (1814-1865, Germany), anatomist and physiologist
1847: body size - temperature

populations and species of larger size are found in colder environments and species of smaller size are found in warmer regions within a broadly distributed taxonomic clade

Allen's rule (アレンの法則)
Allen, Joel Asaph (1838-1921, USA), zoologist
1877: appendage - climate

animals adapted to cold climates have shorter limbs and body appendages than animals adapted to warm climates

Predicted distribution (分布推定)


Species distribution models (SDMs)

≈ bioclimatic models, climate envelopes, ecological niche models (ENMs), habitat models, resource selection functions (RSFs), range maps, etc.
to predici species distribution (Elith & Leathwick 2009)
key assumptions = species are at equilibrium with their environments

problems:
confirming if the assumption is supported
prediction → extrapolation
weak linkages between SDM practice and ecological theory

Basic SDMs
  1. gathering relevant data
  2. assessing its adequacy (the accuracy and comprehensiveness of the species data; the relevance and completeness of the predictors)
  3. deciding how to deal with correlated predictor variables
  4. selecting an appropriate modeling algorithm
  5. fitting the model to the training data
  6. evaluating the model including the realism of fitted response functions, the model's fit to data, characteristics of residuals, and predictive performance on test data
  7. mapping predictions to geographic space
  8. selecting a threshold if continuous predictions need reduction to a binary map
  9. iterating the process to improve the model in light of knowledge gained throughout the process

GARP

= genetic algorithm for rule-set rroduction
Data: using presence/absence data
DK-GARP
rule sets derived with genetic algorithms, using desktop version PA DesktopGarp

BIOCLIM

envelope model (using diva-gis)

BRUTO

regression, a fast implementation of a GAM (mda in R)

DOMAIN

multivariate distance, using diva-gis (Carpenter et al., 1993)

GBM

boosted decision trees, gbm package in R

GLM

generalized linear model, using R

MARS

multivariate adaptive regression splines, using mda in R to handle binomial responses

MARSINT

modified MARS, interactions allowed

MaxEnt model

= Maximum entropy model (Phillips et al. 2006)
→ species distribution and environmental niche modeling
  1. MaxEnt typically outperforms other methods based on predictive accuracy
  2. the software is particularly easy to use
Predict:
  • probability distributions given constraints on their moments
  • relative abundance distributions based on the number of individuals, species and total energy
  • community composition along ecological gradients based on traits
  • species distributions based on environmental covariates
  • associations in food webs
  • much more …
Data
  • presence locations, absence data are not required
  • background locations (not pseudo-absences) should be chosen → influencing the prediction
    sampling bias (Merow et al. 2013)
  • a gridded landscape
  • environmental covariates for each landscape grid cell
  • optional - measure of sampling effort in each cell
  • optional – a landscape on which to project your predictions
Entropy maximization
two parts: a constraint component and an entropy component
1) Constraint component
  • the data define moment constraints on the probability distribution
  • the temperature at the all the presence locations define the mean, variance, etc. of the temperature where the species occur
  • Maxent requires that the predicted distribution fulfills these constraints
2) Entropy component
  • Many distributions could fulfill these constraints
  • Maximizing entropy is a method to choose among the many probability distributions that fit your data
  • Maxent starts by assuming the probability is perfectly uniform in geographic space and moves away from this distribution only to the extent that it is forced to by the constraints
Output
  • the output is a probability distribution that sums to 1
    omission rate / auc → roc (receiver operating character) curve
  • for species distributions this gives the relative probability of observing the species in each cell
  • cells with environmental variables close to the means of the presence locations have high probabilities
フッター