(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (2024)

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (1)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

620

Original Research Article https://doi.org/10.20546/ijcmas.2018.701.075

Population Dynamics of Sucking Pests with Relation to Weather

Parameters in Bt Cotton in Buldana District, Maharashtra, India

Prashant W. Nemade, Kiran P. Budhvat* and Pankaj S. Wadaskar

Cotton Research Unit, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola 444104,

Maharashtra, India

*Corresponding author

A B S T R A C T

Introduction

Cotton (Gossypium sp.) is the leading natural

fibre and oil seed crop which plays a key role

in Indian economy with global position of

second in production after China and offering

livelihood security for the Indian farming

community. It also plays a dominant role in

the industrial and agricultural economy of the

nation and has a unique place in social affairs.

Many allied activities like ginning, fabric

production, textile processing, garment

manufacture and their marketing etc. provides

employment about 6 million people. It also

provides 65 percent raw material to textile

industry and contributed 1/3rd

of total foreign

exchange earning of India (Mayee and Rao,

2002).

In India, the area under cotton crop is 121.91

lakh ha with production of 347.05 lakh bales

(170 kg) and productivity of 484 kg lint/ha,

however Maharashtra state comes under

central zone occupies an area of 40.95 lakh ha

with production of 73.75 lakh bales and

productivity of 306 kg lint /ha (Anonymous,

2011). The sucking pests viz. Aphids (Aphis

gossypii), Jassids (Amrasca biguttula

International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 01 (2018) Journal homepage: http://www.ijcmas.com

The population dynamics of sucking pests Bt cotton along with their correlation with

weather factors were studied during 2008-2013 in five talukas of Buldana district,

Maharashtra. According to five year average data the maximum aphid population (43.56

per cent) was recorded in 33rd meteorological week whereas jassid population were

reached maximum of 4.42 jassid in 37th meteorological week. The population of thrips

was more abundant (6.96 thrips/3 leaves/plant) on the crop in 36th meteorological week. In

case of white fly, maximum population of 6.57 whiteflies per 3 leaves per plant was

recorded in 37th meteorological week whereas, minimum of 0.82 whiteflies recorded in

31st meteorological week. Among the weather parameters maximum temperature showed

positive correlation with A. bigutulla bigutulla, T. tabaci and B. tabaci except whitefly

whereas, minimum temperature showed positive correlation with all above mentioned

sucking pests. The rainfall favored the activity of all sucking pests with positive

correlation except whitefly.

K e y w o r d s

Bt cotton,

Correlation,

Relative humidity,

Sucking pests and

temperature

Accepted:

06 December 2017

Available Online: 10 January 2018

Article Info

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (2)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

621

biguttula) Whiteflies (Bemisia tabaci) and

Thrips (Thrips tabaci) are most serious and

destructive pests with regular occurrence.

After introduction of Bt cotton hybrid a

general shift in sucking pest complex towards

thrips, aphids, jassids, white fly and other

sucking pests was observed. These minor

pests attained economic importance after

introducing Bt cotton. The pest problems,

particularly that of up till now minor pests,

had not be monitored regularly and often

remedial majors were undertaken only after

they reached epidemic or causes heavy losses.

A regular surveying of cotton pest, using

preferably information communication

technologies and development of suitable IPM

strategies was the need of the hour, as it would

have lay to better by all agencies involved in

plant protection and issue proper advice to

farmer based on actual pest problem. Amongst

various reason of low productivity of Bt

transgenic cotton, the sucking pests gain much

importance due to havoc created by most of

the sucking pest in the recent years. A broad

range of insecticides available in market have

proved as effective in reducing the pest

population. However, negligence in following

the principles of crop protection,

indiscriminate and extensive use of synthetic

insecticides led to development of insecticidal

resistance, pest resurgence, residue,

destruction of natural enemies etc. Hence, it is

require moving on other molecules with

different mode of action to overcome such

types of consequences (Patel, et. al., 2014).

Thus the present study was conducted to share

the information on sucking pest scenario with

farmer, state agencies and central agencies for

developing appropriate management

strategies.

Materials and Methods

The field survey was conducted in Buldana

district (Maharashtra) under project entitled

“National Information System for Pest

Management- Bt Cotton” between the years

2008-09 to 2012-13. In the present study, 20

villages from five talukas of Buldhana district

were selected. From these talukas two circles

were preferred and from which two villages

were selected and two Bt cotton growing

farmer were chosen from each village. The

observations were recorded in each week from

these farmers field. Observations on aphids,

jassids, thrips, whiteflies count were recorded

by randomly selecting 20 plants from each

field plot on top, middle and bottom leaves per

plant. Sowing of cotton by project farmers was

done from 15th

May to 1st

week of July at a

different spacing under rain fed situation. For

this purpose, different Bt hybrid varieties were

selected for sowing purpose and regular

agronomic practices were carried out. Data on

weather parameters were obtained from the

Meteorology unit, Regional Research Station,

Buldana under Dr. PDKV, Akola. The

relationship between weather parameters and

sucking pests was established by using simple

correlation coefficient and regression analysis.

Results and Discussion

The cumulative data on aphid was averaged

out of between the years 2008-09 to 2012-13.

Average population of aphid was ranged from

7.91 to 43.56 per cent on presence or absence

basis. The maximum (43.56 % aphids)

population was recorded in 33rd

meteorological week; the aphid population

was observed above ET level during 31st to

37th

standard meteorological week, however it

was lower (7.91 %) in 50th meteorological

week. The peak population of aphid was

increased at 33rd

standard meteorological

week and thereafter the peak population goes

decreasing up to 41st week. Thereafter, the

second peak of population was observed in

42nd

SMW after that the population continues

decreasing up to 50th

SMW after that third

peak of aphid was observed in 51st SMW (Fig.

1). Dhobi and Bharpoda (2013) also reported

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (3)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

622

that population of aphid (Aphis gossypii

Glover) was appear during 37th

standard

meteorological week and reached to a first

peak during 39th

SMW.

The population of jassid was recorded during

the same period of years. The result depicted

in Figure 1 revealed that the population of

jassid was ranged between 0.51 and 4.42. The

peak period of jassid population was observed

during 31st to 41

th standard meteorological

week which shows that the maximum (4.42

jassid per 3 leaves) population was recorded in

five year average in 37th

meteorological week

and it was observed minimum of 0.51

jassids/3 leaves/plant in 2nd meteorological

week.

Reddy et al., (2011) showed that the peak

incidence was observed from the second

fortnight of October to first fortnight of

November in 2009-10 (10.11 to 10.82/leaf)

and in the season of 2010-11, the peak

incidence was noticed in mid-September to

first fortnight of October (6.02 to 5.48/leaf).

Similarly, Bharpoda et al., (2013) concluded

that the peak activity of jassid was recorded

during 38th

to 45th

SMW to the tune of 3.17 to

4.82 per 3 leaves. These results are in line

with the present findings.

Average population of thrips was ranged from

0.21 to 6.96 thrips per 3 leaves per plant. The

peak activity of thrips was recorded during

34th

to 39th

SMW (Fig. 1) during which

maximum population of 6.96 thrips/3 leaves

was recorded in 36th meteorological

week; whereas least in 2nd meteorological

week i.e. 0.21 thrips/3 leaves /plant. The

present findings are supporting the results of

Bharpoda et al., (2013) who stated that the

activity of thrips was concerned in Vadodara

district to the tune of 0.06 (5th

SMW) to 4.30

(32nd

SMW). The population showed number

of fluctuation in its activity. However, higher

activity of thrips was noticed during 32nd

to

44th

SMW. Shivanna et al., (2009) reported

that the maximum incidence of thrips

population was noticed from April to May

with a peak incidence of 26.81 per three

leaves was recorded in April second fortnight.

The average population of white fly incidence

was observed between the ranged of 0.82 to

6.57 whitefly per 3 leaves per plant during the

five year in respective SMW. The peak

activity of white fly was recorded during 36th

to 44th

SMW (Fig. 2) where the maximum

(6.57 whiteflies/ 3 leaves) population was

recorded in 37th meteorological week

whereas, minimum of 0.82 whiteflies recorded

in 31st meteorological week. These present

findings are in line with the results obtained

by Deb and Bharpoda (2013) who reported

that the peak activity of white fly was

observed during 46th

SMW (2nd

week of

November).

Correlation studies

The five years (2008 to 2013) mean weekly

counts of various sucking pests from 31st std.

week were correlated separately with weather

parameters and the correlation coefficients

analysis is presented in Table 1.

Aphid

The maximum temperature did not show any

significant impact on the A. gossypii as the

correlation was negatively non-significant

with the population of aphids (-0.102) but the

minimum temperature had showed highly

significant positive correlation with the

population of aphid (0.725*) which showed

that the minimum temperature was most

favorable to the incidence aphids in Bt cotton

(Table 1). The rainfall showed highly

significant positive impact on population

buildup of aphid (0.773*) which reflects that

the rainfall was very favourable for the

positive population buildup of aphid.

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (4)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

623

Table.1 Correlation of weather parameters with incidence of sucking pests on Bt cotton

Sucking

pests

Temperature (°C) Rainfall

(mm)

Relative Humidity (%) BSH

(hr) Maximum Minimum Morning Evening

Aphid -0.102 0.725* 0.773* 0.773* 0.871* -0.839*

Jassid 0.358 0.912* 0.677* 0.849* 0.789* -0.471

Thrips 0.124 0.685* 0.784* 0.752* 0.699* -0.419

Whitefly 0.691* 0.584* 0.217 0.460 0.262 0.136

*Significant at p =0.05% r (5%) = 404 and r (1%) = 515

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (5)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

624

Table.2 Multiple regression equation of sucking pests with weather parameters on Bt cotton at

Buldana district (2008-2013)

Sr.

No.

Sucking

pests

Regression equation R2

Value

1. Aphid Y1 = -144.33+5.658X1-4.321X2-0.109X3+0.390X4+1.360X5-4.113X6 0.845

2. Jassid Y1=5.528-0.346X1+0.475X2+0.009X3-0.034X4-0.023X5+0.406X6 0.885

3. Thrips Y1= -5.408-0.120X1+0.122X2+0.074X3+0.068X4 -0.021X5+0.482X6 0.744

4. Whitefly Y1= 0.583-0.433X1+0.805X2+0.028X3-0.004X4-0.111X5+1.035X6 0.773

Relative humidity both in morning (0.773*)

and evening (0.871*) was positively

correlated with aphid population. The impact

of BSH on the population of all sucking pests

revealed that the population was significantly

negative correlated with aphid (-0.839). The

present findings are in line with Bhute (2012)

who stated that simple correlation studies

revealed that rainfall showed significant and

negative correlation with aphids. The present

findings on positive relationship between

relative humidity and population buildup of

aphids corroborate with observations of

Mohapatra (2008) and Selvaraj and

Adiroubne (2012).

Jassid

The maximum temperature did not show any

significant impact on the A. biguttula

biguttula. The correlation with jassid (0.358)

population was positively non-significant and

the minimum temperature (0.912) and rainfall

(0.677*) had showed highly significant

positive relationship with the population of

jassid (Table 1). Relative humidity both in

morning and evening was positively

correlated with jassids. The average of

morning (0.849*) relative humidity had

positive relationship whereas, the relative

humidity at the evening (0.789*) had showed

highly significant positive relationship. The

impact of BSH on the population of jassids

revealed that the population was significantly

negative correlated with aphid jassid (-0.471).

Mohapatra (2008) reported that rainfall had a

non-significant positive effect on population

of A. biguttula biguttula (0.284).

Thrips

The maximum temperature was negatively

non-significant correlated (0.124) population

but minimum temperature was highly

significant positive (0.685*) relationship with

thrips (Table 1). The rainfall showed highly

significant positive impact on population

buildup of thrips (0.784*) during all the years.

This shows that the rainfall was very

favourable for the positive population buildup

of thrips. The average of morning relative

humidity had positive relationship (0.752*)

whereas, the relative humidity at the evening

had highly significant positive relationship

with thrips (0.699*). The impact of BSH

showed significantly negative correlation with

thrips (-0.419). The present findings are in

controversy with Selvaraj and Adiroubne

(2012) who stated that thrips population build

up showed a significant and positive

correlation with BSH.

Whitefly

The maximum (0.691*) and minimum

(0.584*) temperature exhibited highly

significant positive correlation with

population of whitefly (Table 1). The

population of whitefly showed the non-

significant positive relationship (0.217) with

the rainfall. The average of morning relative

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (6)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

625

humidity had positive relationship (0.460*)

but at the evening it showed non -significant

positive (0.262) correlation with the

population of whitefly. The impact of BSH on

the population of whitefly showed the non-

significant positive correlation (0.136). Shera

et al., (2013) found that the population of B.

tabaci was positively correlated with

maximum and minimum temperature during

all the years; however, it was significant with

minimum temperature. Meena et al., (2013)

concluded that whitefly population exhibited

positive correlation with maximum, minimum

and mean temperature on chilli.

Regression studies

Based on regression analysis by taking

sucking pest population (Y) as a dependent

variable and weather parameters (X) as

independent variables following equations

were fitted for the year 2008-2013 (Table 2).

The regression equation indicated that an

increase in 1ºC of maximum temperature

increases the aphid population by 5.658 per 3

leaves per week and the population decreases

by 4.321 per 3 leaves as the 1ºC of minimum

temperature increases. The increase of 1%

morning and evening relative humidity

increases the aphid population by 0.390 and

1.360 per 3 leaves per week. Increase in 1%

maximum temperature resulted for decreasing

the jassid population by 0.346 per 3 leaves

similarly, 1% increasing of morning and

evening relative humidity the population of

jassid were decreases by 0.034 and 0.023 per

3 leaves, respectively. Whereas, in case of

thrips, the population were decreased by

0.120 and 0.021 per 3 leaves as the 1°C of

maximum temperature and 1% evening

humidity were increased, respectively. Due to

the increase in maximum temperature of 1ºC

whitefly population almost reduces by 0.433

and similarly whitefly population reduction

(0.111 per 3 leaves) was also observed at

increasing of evening humidity by 1%.

The prediction of sucking pests of Bt cotton

were made by developing multiple regression

equations by using regression models. The

results of regression models and the

coefficients of determination (R2) indicated

that the sucking pests viz., aphid, jassid, thrips

and whitefly were predicated to an extent of

84, 88, 74 and 77 per cent, respectively. The

correlation and regression analysis clearly

showed the importance of weather factors in

predicting the sucking pest incidence in Bt

cotton.

References

Anonymous, 2011. Central Institute for

Cotton Reasearch. Nagpur, Annual

Report, 2011-12. PP-78.

Bharpoda, T. M., Thumar, R. K., Patel, H. C.

and Borad, P. K, 2013. Activity of

sucking insect pests in Bt cotton grown

in Vadodara district. Souvenir abstract

presented in National Seminar on

technology for development and

production of rainfed cotton. N.A.U.,

Navsari, Gujarat, pp. 42.

Bhute, N. K., Bhosle B. B., Bhede B. V.

and More D. G., 2012. Population

dynamics of major sucking pests of Bt

cotton. Indian J. of Ent. 74(3): 246-252.

Dhobi, C. B. and Bharpoda, T. M. 2013.

Population dynamics of major sucking

pests and natural enemies in Bt cotton.

Souvenir abstract presented in National

Seminar on technology for development

and production of rainfed cotton.

N.A.U., Navsari, Gujarat, pp. 53-54.

Mayee, C. D. and Rao, M. R. K. 2002.

Current cotton production and

protection scenarios including G.M.

Cotton. Agrolook, April-June, 14-20.

Meena, R. S., Ameta, O. P. and Meena, B. L.

2013. Population dynamics of sucking

pests and their correlation with weather

parameters in chilli, Capsicum annum

L. crop. The Bioscan, 8(1): 177-180.

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (7)

Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626

626

Mohapatra, L. N., 2008. Population dynamics

of sucking pests in hirsutum cotton and

influence of weather parameters on its

incidence in western Orissa. J. Cotton

Res. Dev. 22(2): 192-194.

Patel, R. D., Bharpoda, T. M., Patel, N. B and

Borad, P. K. 2014. Bio-efficacy of

cyantraniliprole 10% OD an Anthranilic

diamide insecticides against sucking

pests of cotton. The Bioscan, 9(1): 89-

92.

Reddy Krishna, G. V., Suryakala, I.,

Sheshaiah, B., Ramesh, V. and Sunitha,

V., 2011. Studies on population

dynamics of leaf hopper, Amrasca

bigutulla bigutulla (ishida) on

transgenic Bt cotton. J. of Recent

Advanc. in Appl. Sci. 26:53-55.

Selvaraj, S. and Adiroubane, D., 2012.

Influence of weather parameters on the

incidence of thrips, Thrips tabaci

Lindemann in cotton. J. of Cotton Res.

and Dev. 26(2): 234-237.

Shera, P. S., Vijay Kumar and Anand Aneja,

2013. Seasonal abundance of sucking

insect Pests on transgenic Bt cotton vis-

à-vis weather parameters in Punjab,

India. Acta Phytopathologica et

Entomologica Hungarica, 48(1): 63–74.

Shivanna, B. K., Nagaraja, D. N.,

Manjunatha, M and Naik, M. I., 2009.

Seasonal incidence of sucking pests on

transgenic Bt cotton and correlation

with weather factors. Karnataka J.

Agric. Sci., 22(3-Spl. Issue): 666-667.

Sushma Deb and Bharpoda T. M. 2013. Pest

succession of major sucking insect pest

in Bt cotton. Souvenir abstract presented

in National Seminar on technology for

development and production of rainfed

cotton. N.A.U., Navsari, Gujarat, pp.

50-51.

How to cite this article:

Prashant W. Nemade, Kiran P. Budhvat and Pankaj S. Wadaskar. 2018. Population Dynamics

of Sucking Pests with Relation to Weather Parameters in Bt Cotton in Buldana District,

Maharashtra, India. Int.J.Curr.Microbiol.App.Sci. 7(01): 620-626.

doi: https://doi.org/10.20546/ijcmas.2018.701.075

(PDF) Population Dynamics of Sucking Pests with Relation to ... W. Nemade, et al...Int.J.Curr.Microbiol.App.Sci (2018) 7(1): 620-626 624 Table.2 Multiple regression equation of sucking pests - DOKUMEN.TIPS (2024)

FAQs

What are the factors affecting the distribution and abundance of insects? ›

Numerous studies have shown that the distribution and diversity of insect species are determined by complex biotic and abiotic factors, which mainly include climatic factors, plant communities, soil characteristics, and elevation gradients [6,7,8,9,10,11,12].

What are the ecological factors affecting insect development? ›

Both a-biotic (temperature, humidity, light) and biotic (host, vegetative biodiversity, crowding and diets) stresses significantly influence the insects and their population dynamics. In response to these factors insect may prolong their metamorphic stages, survival and rate of multiplication.

How does temperature affect insect development? ›

For the same type of insect at the same stage of development (egg, or larvae), a higher temperature will speed up development than a lower temperature.

What is a regular pest? ›

Regular pest: Frequently occurs on crop - Close association e.g. Rice slem borer, Brinjal fruit borer. Occasional pest: Infrequently occurs, no close association e.g. Caseworm on rice, Mango stem borer. Seasonal pest: Occurs during a particular season every year e.g. Red hairy caterpillar.

What are the main factors affecting population distribution? ›

The main factors determining population distribution are : climate, landforms, topography, soil, energy and mineral resources, accessibility like distance from sea coast, natural harbours, navigable rivers or canals, cultural factors, political boundaries, controls on migration and trade, government policies.

What are the two biggest factors that affect the distribution of organisms? ›

Species distributions are dependent on interactions with abiotic and biotic factors in the environment. Abiotic factors like temperature, moisture, and soil nutrients, along with biotic interactions within and between species, can all have strong influences on spatial distributions of plants and animals.

How does climate change affect population dynamics of insect pests? ›

The general consequences of global warming on insect dynamics include: expansion of geographic range, increased survival rates of overwintering populations, increased risk of introduction of invasive insect species, increased incidence of insect-transmitted plant diseases due to range expansion and rapid reproduction ...

What temperature kills most bugs? ›

TEMPERATURE.
  • 120°-140°F: insects die in minutes. ...
  • 110°-115°F: insects will die within hours. ...
  • 95°-100°F: insect development stops. ...
  • 65°-70°F: insect development slows and insects are somewhat repelled. ...
  • 55°-60° F: insect development stops. ...
  • 35°-45°F: insects die in weeks. ...
  • 10°-20°F: insects die in minutes.

What is one method of IPM that is used to control pests? ›

As a first line of pest control, IPM programs work to manage the crop, lawn, or indoor space to prevent pests from becoming a threat. In an agricultural crop, this may mean using cultural methods, such as rotating between different crops, selecting pest-resistant varieties, and planting pest-free rootstock.

What are the four main groups of pests? ›

Pests can be broken into four main categories:
  • Vertebrate Pests – Have a backbone. Examples: Rodents, birds, reptiles, and other mammals.
  • Invertebrate Pests – No backbone. Examples: Insets, spiders, ticks, slugs.
  • Weeds – Any plant growing out of place.
  • Disease – Fungi, bacteria, viruses, and other microorganisms.

What is the most common pest? ›

10 Most Common Household Pests
  • Ants.
  • Cockroaches.
  • Bed bugs.
  • Rats.
  • Mice.
  • Fleas.
  • Spiders.
  • Termites.
Nov 8, 2022

What is bug frass? ›

Frass is a by-product of insect breeding, the leftover substrate, which is composed of spent feedstock, insect feces, and cuticles. Recently, it has been evaluated as a potential fertilizer, in a circular economy perspective.

What are the factors affecting abundance and distribution? ›

Both physical (temperature, rainfall) and biotic (predators, competitors) factors may limit the survival and reproduction of a species, and hence its local density and geographic distribution.

What are the factors affecting the growth of insects? ›

The main abiotic factors influencing insects are temperature, moisture, light and air and water currents.

What are the factors responsible for insect dominance abundance? ›

Reasons for insect abundance include structural perfection like their exoskeleton, developmental characteristics like different life stages, high specificity of food sources, high fecundity, reproductive methods, and protective adaptations. Their success is also due to adaptability to diverse habitats and climates.

What 4 factors determine the distribution and abundance of a population? ›

Environmental factors significantly influence the distribution and abundance of species within an ecosystem. Limiting factors such as food supply, water availability, light, space, and nutrients dictate how populations can grow, where they can live, and in what numbers.

References

Top Articles
Latest Posts
Recommended Articles
Article information

Author: Catherine Tremblay

Last Updated:

Views: 6239

Rating: 4.7 / 5 (67 voted)

Reviews: 90% of readers found this page helpful

Author information

Name: Catherine Tremblay

Birthday: 1999-09-23

Address: Suite 461 73643 Sherril Loaf, Dickinsonland, AZ 47941-2379

Phone: +2678139151039

Job: International Administration Supervisor

Hobby: Dowsing, Snowboarding, Rowing, Beekeeping, Calligraphy, Shooting, Air sports

Introduction: My name is Catherine Tremblay, I am a precious, perfect, tasty, enthusiastic, inexpensive, vast, kind person who loves writing and wants to share my knowledge and understanding with you.