Why are organisms
absent some places and abundant in others?
·
Abiotic Forces: plants have certain limits regarding
environmental tolerances. Example
: Temperature, moisture, sunlight, pH,
substratum, salinity, atmosphere, etc.
·
Biotic Forces: Organisms compete for space and
resources, and some eat others. Example : competition, predation, symbiosis,
nest sites, habitat modification.
·
Opportunity (history): organisms are absent where
there is no geographical access.
a.
Sometimes when they are introduced into areas where
they were previously absent, havoc ensues.
b.
Introduce (exotic) species are the second most
important cause of extinction.
·
Habit destruction is the first.
Examples: kudzu, fire ants, killer bees, zebra mussels, Asiatic clams,
Japanese honeysuckle, water hyacinth, Hydrilla, Egeria, Japanese beetles,
honeybee mites, giant slugs, Brazilian pepper, tamarisk, thistles, Australian
pine, privet.
Liebig’s
Law of the Minimum
·
Liebig recognized that plant growth is controlled by
plant nutrients, and whichever nutrient is in most limited supply controls the
plant_s growth.
·
This concept was later expanded to cover other
environmental factors such as water, light, temperature.
·
He noted that plants had varying ranges of tolerance
for a given factor. “The growth or distribution of a plant is dependent on the
one environmental factor most critically in demand.”.
Shelford’s Law of Tolerance
·
Shelford
in 1913 noted a weakness in Liebig_s
general law which came to be known as the Law of Tolerance. And this in turn
was modified by Ronald Good, a plant geographer: “Each and every plant species
is able to exist and reproduce successfully only within a definite range of environmental
conditions.
·
Good rated climatic factors above edaphic factors.
·
Some Examples:
a.
Salinity tolerance in plants (intertidal zonation)
b.
Thermal constraints on activity (sparrow
microclimates, shown in the diagram from Smith_s Ecology and Field Ecology.)
•
The distribution of a species along an environmental
gradient generally approximates a Gausian distribution, with the optimal occurrence
somewhere near the midpoint of the distribution.
goodjob maak :) nice info
BalasHapus