Climate change Flashcards
Historical CO2 and temperature variation
There is no correlation between CO2 and global temperature in deep time.
But more recently there has been
a strong association between CO2 and temperature
Globally averaged GHG concentrations, CO2 now over 400ppm
CH4 at 1700 ppb and N2O at 320 ppb
Global anthropogenic CO2 emissions correspond to the increase
In atmospheric GHG concentrations
Increase from 1970 to 2010
Of 22Gt of anthropogenic GHG 65% through fossil fuels
Land and ocean temperatures 1850-2017
Steep increase from 1970 industrialisation, land is almost over 1.5°C and sea is at 0.75°C
2010-2020 +1°C and +0.45m sea-level rise
2100 +4°C, +0.73m sea-level rise
Heavier precipitation
IPCC 2018 Special report on global warming of 1.5°C
Threatened ecosystems, extreme weather events, large scale singular events, coral die-off, arctic region, coastal flooding, river flooding
Case study: ‘Hot-dogs’ African Wild Dog -
Woodroofe et al. 2017 J. Anim Ecol 86: 1329-38
Trade-off between temperature and activity
High temperatures: • reduced activity • longer inter-birth intervals • poorer pup recruitment Primarily arise from direct impacts on body temp
Case study:
Pied flycatchers & trophic mismatch
Both et al. 2006 Nature 441, 81-83
Both et al. 2010 Proc Roy Soc B 277, 1259-1266
Food abundance and time of breeding used to be matched however food is becoming abundant earlier meaning more caterpillars and and fewer flycatchers
Which species most vulnerable?
Kellermann et al 2012 PNAS
Those in the tropics
Latitudinal range shifts
Chen et al 2011 Science 333, 1024-1026
Europe, America, Chile
Mean rate 16.9 km per decade, but much variation:
i) faster in areas with more rapid warming
ii) broadly speaking range shifts track climate change, but far from always
iii) Species traits don’t
predict lag
(Angert et al 2011 Ecol Letters 14, 677-689)
Range shifts: altitude
Observed elevational range shifts more limited than latitudinal shifts with large lag to expected shift
Species traits poor predictors of altitudinal shifts mainly because they move to a different face of mountain instead of moving upwards
Predicting range shifts – bioclimatic envelope models
Principle is to record the relationship between a species current distribution and the current climate
Then feed predictions of future climate into this relationship to predict future distributions
climatic atlas of european breeding birds - Breeding distributions in 50km x 50km cells in 1985-1988
Predictions for 2100
3 climate models, ‘middle of the road’ emissions scenario
Case study: dartford warbler Simulated presence late 21st century
Northward shift and decrease in overall range size
General findings
NE shift in range boundary: typically ‘several hundred km’
Average future range 80% of current range: 31 species <10%
Average overlap of current & future ranges 38- 53%:
mean for endemics 14-34%; 10 species 0%
Global predictions
Bioclimatic envelope models for 1,103 endemic animal and plant species in numerous regions
Highlights important general points re emission scenarios and dispersal
Thomas et al. 2004 Nature 427, 145-148
Extinction risk much higher with max emissions and with no dispersal (9-13% vs 38-52%)
Global predictions
Extinction risk based on species-area relationship, assume z = 0.25 (based on data)
S = cAz
i) Changes in summed area of all species
ii) Mean % loss of area of each species = % species extinctions
‘faithful to species-area relationship as halving habitat area leads on average to loss of half of the range of each species’
iii) Similar to 2nd method but done separately for each species
Problems with bioclimatic envelopes
Spatial scale – based on average conditions in a large grid cell.
Giménez-Benavides et al 2007 Annals of Botany 99, 723-734
Spatial scale appropriate for conservation planning?
Ignores local adaptation - assumes that all individuals have an identical response to climate
BCE Local individuals can have a ‘home advantage’ i.e. evidence of local adaptation
Assumes that climate regulates range limits – ignores biotic interactions
Jankowski et al 2010 Ecology 91, 1877-1884
BCE Ignores evolutionary rescue – hard to predict but may be important
Carlson et al 2014 TREE 29, 521-530
Should bio-climatic envelope models be ignored?
Willis et al 2009 Conserv. Letts. 2, 46-52
Marbled white & small skipper
Introduced to two sites north of range but predicted to be suitable
Populations increased steadily following the introductions
Assessing vulnerability (climate change, extinction risk, population size, viability) to climate change
Sensitivity: inter-specific variation in effects of climate change in population growth rate (depends on climatic tolerance, geographic range size, population size, trophic level)
Adaptability: Ability to cope with climate change in situ to change behaviour or evolve, ex situ to migrate (depends on behavioural and phenotypic plasticity, reliance on specific habitat, specificity of species interactions, genetic diversity, populations size and fecundity, and dispersal ability)
Exposure: nature and magnitude of climate change experienced (depends on changes in average or extreme temperature, precipitation, snow fall, snow melt timing, drought, floods, fire intensity and frequency
Conservation implications
Dawson et al 2011 Science 332, 53-58
x axis - exposure to climate change and barriers to dispersal
y axis - sensitivity and adaptive capacity
High vulnerability = high exposure, high barriers to dispersal, high sensitivity and low adaptive capacity
Preparedness = low exposure, high sensitivity/low adaptability
Low-intensity intervention = High exposure, low sensitivity and high adaptive capacity
