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

Biota (生物相)

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

Based on research 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.

kingdom > region > province > district

(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

B. Geographical distribution (地理分布)

1. Arctogaea (北界): Arctogaea
2. Notogaea (南界)
   • Austro-Columbia (中南米)
   • Austral-Asia
   • New Zealand

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

Mammals in in New Guinea - characterized by Australian mammals, e.g., marsupials (wallabies, bandicots and marsupial mice) and monotremes (spiny anteaters)
Mammals in Sunda - many large mammals inhabiting in southeastern Asia

Revised based on Dickerson RE. 1928. Distribution of life in the Philippines. Bureau of printing

Weber Line (ウェーバー線) (Weber 1888): rejected the significance of Wallace Line by the distribution of freshwater fish

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

Pacific Ocean

Insect fauna: characterized by fauna in Oriental Region + islandic factors
Decreasing animal diversities from west to east with increasing the distance from mother lands

Japan (日本)

named by Milne, John (1850-1913, England)

1857-1859: Palliser Expedition - western Canada
1860 Yangtze River in China (military duty)
1861, 1863-1884 Hakodate

animals in Hokkaido are related to northern Asian species

Body size on animals

Bergmann's rule (ベルクマンの法則)

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
Ex. 1969 Brown & Lee: woodrats (Neotoma)

linear and negative relationship between temperature and body weight

Allen's rule (アレンの法則)

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

Characteristics of islandic biota

Endemic plants

well-established on lava

Exotic plants

Origin: Mexico - Brazil (biologically-invasive species to Hawaii)

Species distribution models (SDMs)

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

DK-GARP

BIOCLIM

BRUTO

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

DOMAIN

GBM

GLM

MARS

MARSINT

MaxEnt model

1. MaxEnt typically outperforms other methods based on predictive accuracy
2. the software is particularly easy to use

• 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

• 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

• 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

• 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