Interpreting community data from charts and tables
Ecology • Adaptations, interdependence and competition
Flashcards
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Key concepts
What you'll likely be quizzed about
Population and community - definitions
A population is the total number of organisms of the same species in a defined geographical area. A community is a group of two or more populations of different species living in the same area at the same time. These definitions place charts and tables in the correct ecological scale for interpretation .
Biotic and abiotic factors - limiting factors
Biotic factors are the living parts of the environment such as predators, prey and disease; abiotic factors are non‑living features such as light, temperature and pH. Changes in a limiting abiotic factor cause a change in population processes, for example reduced light → reduced photosynthesis → lower plant growth → lower herbivore abundance. Identification of limiting factors in data supports causal explanations of trends in community graphs .
Types of data and graph selection
Quantitative data include counts and measurements and appear as continuous or discrete values; qualitative data appear as categories. Line graphs show how one continuous variable changes with another and suit time series or continuous measurements. Bar graphs compare categories and histograms display frequency across continuous ranges. Scatter graphs explore correlations between two quantitative variables. Correct graph choice ensures cause → effect interpretation and prevents misleading conclusions .
Presenting data in tables - conventions
The independent variable appears in the first column and is organised in increasing order; dependent variables occupy subsequent columns. Column headers must include clear titles and units; means and repeats appear in the rightmost columns. Consistent units and decimal places prevent misreading and support accurate calculations of averages and percentage changes .
Sampling and bias in community surveys
Random sampling gives each potential sample plot an equal chance of selection and reduces sampling bias. Small sample sizes or non‑random selection produce biased abundance estimates, which cause incorrect conclusions about community structure. Use of random number methods and sufficient replicates creates more reliable estimates of species abundance and distribution .
Identifying trends, anomalies and causal links
Trends show systematic increases or decreases and support causal explanations when linked to known biotic or abiotic drivers. Anomalous results are single points that depart from a trend and may indicate measurement error, contamination or rare events. Calculation of means, plotting lines of best fit and comparing multiple conditions in tables or graphs allow objective interpretation and evaluation of evidence before causal statements are made .
Key notes
Important points to keep in mind