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LIFE ZONES CLASSIFICATION SYSTEM OF HOLDRIDGE

The concept of life zones was developed by Clinton Hart Merriam in 1889. A life zone is characterized as an enviroment where the plant and animal life share unique and similar characteristics. The concept of life zones in Costa Rica has been applied to land development and conservation, as it has been in many places. The life zones were supposed to help determine land-use options and identify ecosystems. Merriam broke life zones down into these categories:

bulletLower Sonoran (low, hot desert)
bulletUpper Sonoran (desert steppe)
bulletTransition (open woodlands)
bulletCanadian (fir forest)
bulletHudsonian (spruce forest)
bulletArctic-Alpine (alpine meadows or tundra)

 This system was eventually criticised for being too imprecise and Leslie Holdridge revised the concept of life zones to create what is known as Holdridge's Life Zones. Holdridge's sysem (1978) is a more complex classification system which is predominately based on three factors: 

bulletMedian Annual Biotemperature; 
bulletAnnual Percipitation; and 
bulletAnnual Evapotranspiration*.

The biotemperature has essentially been mimicked from climatic zones of the earth, corrected by elevation climate classes on the location. Each of these factors are direct or indirect climatic factors, which must be obtained from climatic data. 

Evapotranspiration (ET) is the sum of evaporation from the surface of the earth and plant transpiration to atmosphere.

Holdridge’s system (1978) consist of a triangle of honeycomb shaped cells, each with varying climatic and precipitation characteristics. 



Mean annual
biotemperature (
oC)

Potential evaporation ratio

Humidity Province

Average total annual precipitation (cm)

Polar

<1.5

0.125-1.5

6.25-75

 

 

 

 

 

 

Subpolar (Alpine)

Tundra

 

 

 

 

   Dry

1.5-3

1-2

Subhumid

6.25-12.5

 

   Moist

1.5-3

0.5-1

Humid

12.5-25

 

   Wet

1.5-3

0.25-0.50

Perhumid

25-50

 

   Rain

1.5-3

0.125-0.25

Superhumid

50-100

 

 

 

 

 

 

Boreal (Subalpine)

Desert

3-6

2-4

Semiarid

6.25-12.5

Boreal

Dry  Scrub

3-6

1-2

Subhumid

12.5-25

Boreal

Forest

 

 

 

 

 

Moist puna

3-6

0.50-1

Humid

25-50

 

Wet paramo

3-6

0.25-0.50

Perhumid

50-100

 

Rain paramo

3-6

0.125-0.25

Superhumid

100-200

 

 

 

 

 

 

Cool Temperate

Desert

6-12

4-8

Arid

6.25-12.5

Cool Temperate

Desert Scrub

6-12

2-4

Semiarid

12.5-50

Cool Temperate

Tundra Dry

6-12

1-2

Subhumid

25-50

Cool Temperate

Forest

 

 

 

 

 

Moist

6-12

0.5-1

Humid

50-100

 

Wet

6-12

0.25-0.5

Perhumid

100-200

 

Rain

6-12

0.125-0.25

Superhumid

200-400

 

 

 

 

 

 

Subtropical

Desert

12-24

8-16

Periarid

6.25-12

Subtropical

Desert  Scrub

12-24

4-8

Arid

12-25

Subtropical

Thorn Woodland

12-24

2-4

Semiarid

25-50

Subtropical

Forest

 

 

 

 

 

Dry

12-24

1-2

Subhumid

50-100

 

Moist

12-24

0.5-1

Humid

100-200

 

Wet

12-24

0.25-0.5

Perhumid

200-400

 

Rain

12-24

0.125-0.25

Superhumid

400-800

 

 

 

 

 

 

Tropical

Desert

>24

16-32

Superarid

6.25-12

Desert Scrub

>24

8-16

Periarid

12-25

Tropical

Thorn Woodland

>24

4-8

Arid

25-50

Tropical

Forest

 

 

 

 

 

Very Dry

>24

2-4

Semiarid

50-100

 

Dry

>24

1-2

Subhumid

100-200

 

Moist

>24

0.5-1

Humid

200-400

 

Wet

>24

0.25-0.5

Perhumid

400-800

 

Rain

>24

0.125-0.25

Superhumid

>800

Direct and indirect climatic modifiers as the prime modifiers has severe limitations for mapping purposes. First of all, Vreugdenhil et al (2002) have argued that available climate maps are extremely coarse and speculative, as in most developing countries, climatic data are based on sparsely distributed, poorly maintained weather stations. Areas with natural ecosystems often have limited access, and weather stations are usually absent. 

Secondly, Data recording is often unreliable, taken irregularly and sometimes even fraudulent, particularly in remote areas.  Therefore climatic maps are of very limited use for distinguishing between different vegetation and ecosystem classes. 

The weather station in Ciudad Mutis, Chocó, Colombia is the first one down from the border with Panamá along the coast.   Measurements of solar hours are restricted by shade from the trees. The wind gage is too low and hidden among the trees.
In many developing countries, the national and local climatic maps are based on erroneous data sets like generated by this meteorological station

Worldwide climate zones are even less useful, as they are so coarse that they usually provide no more than 1 - 3 classes for medium sized countries and 1 for small countries.   

While a bit more detailed in mountain regions, in climatically homogeneous regions, like vast areas in Siberia and in the Amazone, the Holdridge Live Zone classification generates hardly any classes at all: One cell of the Holdridge System (tropical moist forest) encompasses a vast swath of Amazonian forest. Within this swath of territory are some 30,000 species of trees distributed among scores of distinct communities. Clearly the Holdridge System fails to represent this diversity even worse than any of the other classification systems.

Another problem with the Holdredge system is that it can't be drawn from readily available data. It requires rather complex modeling from rather large data sets. While this is possible nowadays, it was not in the nineteen seventies and eighties. It is not surprising that the existing Holdridge maps have very few classes and are very small. Given the complexity of the requiered modeling, the absence of computer power and the unreliability and scarce distribution of weather station in those days, those maps must have been hand drawn sketches, primarily based on existing course elevation and poor quality precipitation maps. In other words, those maps are sketchy reflections of the system at best, and most likely mere fantasies.

The Holdridge life zone system at best could look something like this but we even doubt the validity of this map that we found on the internet. 

The now broadly available GIS and remotely sensed imagery, allow multi-criteria classification and much higher levels of precision. For the design of protected areas systems, biounits distinguished by this classification system on existing maps are too unreliable and coarse to be used at all.

 

This page  is part of our web-book on Biodiversity Conservation. For organized reading go to our on-line Table of Content, or download our book in pdf format.

 

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