community assembly and dis-assembly under global...

Community assembly anddis-assembly under global change

Elizabeth M. WolkovichUniversity of California, San Diego

November 2011

Warming by 2099

Changes relative to last 20th centuryA2 scenario, IPCC, WG1 summary, 2007

What will be species’ responses?

Changes relative to late 20th centuryA2 scenario, IPCC, WG1 summary, 2007

– Extinctions– Spatial shifts– Temporal shifts

Impacts of global change oncommunity assembly

– Diverse methods tounderstand directeffects

– Assembly theory topredict indirecteffects


– Direct: Changes in planttiming with warming Methods comparison Beyond earlier spring

– Indirect: Role of timing inplant invasions

– Direct & indirect:Mechanisms of invasioneffects on food webs

Phenology

Phenology

– When you plant– Harvest dates– Ties to wild

species: Performance Ranges Extinction

1990

2006

USDA hardiness maps (Arbor Day)

Phenology most commonly used as anindicator of global climate change

Phenology most commonly used as anindicator of global climate change

– Our ability toexplain andpredict variationacross species,habitats and timeis still poor.

What are the direct phenologicaleffects of climate change on plant

species?

Global synthesis of warmingeffects on phenology

– Diverse communities– Long-term datasets– Multiple approaches

Comparison of methods

Experiments Observations– Project forward to future conditions

– Isolate effects

– Best data for how plants respondto climate change

Do experiments and observationsfind the same plant responses

to warming?

Two databases

Spans 1,560 plant species, >2,000 species x site, over 200 years

Plant sensitivities to temperature

– Calculated sensitivities Change in days per °C

– Hierarchical mixed-effects models Accounts for non-

independence amongsites and species

Experiments underpredictresponses to climate change

1,560 species

flowering: F1,33=9.36, p=0.004

leafing: F1,20=3.58, p=0.07

Experiments underpredictresponses to climate change

1,560 species matching species

flowering: F1,16=3.67, p=0.07leafing: F1,10=8.75, p=0.014

Mismatch was not due to:

lifespan: F1,1891=6.11, p=0.014lifespan x study-type: F1,1891=0.11, p=0.74

– Speciescharacteristics No difference

between herbs &woody species

Annuals equally moresensitive

Mismatch was not due to:– Species characteristics

No difference between herbs &woody species

Annuals equally more sensitive– Species sampling– Habitat– Timescales: Genotypic shifts

30 versus 3 years– Correlations with other variables– Aspects of experimental design– Degree of warming

Mismatch may be due to:– Artifacts of

experiments Reduced irradiation Reduced soil moisture

– Improving design: Avoid artifacts, or

measure them Add light and moisture

treatments Report high-quality

temperature data

Mismatch may be due to:– Artifacts of

experiments Reduced irradiation Reduced soil moisture

– Climate changeeffects not replicatedby experiments

– Improving design: Avoid artifacts, or measure

them Add light and moisture

treatments Report high-quality

temperature data

Beyond earlier spring:Multi-seasonal effects of climate change

– Most temperatespecies haveadvanced withwarming (70-80%)

– Most speciesrespond to springwarming

– Some temperatespecies requirewinter chilling(vernalization)

Data from Chinnor, UK (Fitter & Fitter 2002)

Vernalization

– Do not respondto springwarming untilchilling iscomplete

– Lab andmodeling studiessuggest thiscould delayflowering withwarming


How does winter and springwarming affect phenology?

– Used 47-yr dataset: Calculate sensitivities to

temperature acrossseasons

Model-fitting approach toinclude spring versusspring + winter responses

Compared modelparameters with species’long-term responses towarming

Species’ responses to spring andwinter warming

– Of 384 species: 275 had significant cues to

spring-warming only 70 had both spring-warming

and vernalization cues⇒ Divergent responders


Does including vernalizationimprove predictions of long-term

trends to climate change?

Spring-only modelover-predicts advance

Model with vernalizationaccurately predicts mean response

Species with diverse cues mayrespond to future warming

– Species withvernalizationrespond strongly totemperature

– But show no currenttrends due to off-setting response

– May delay in futureas chillingrequirements are notmet

What are the direct phenologicaleffects of climate change on plant

species?

What can we predict about directresponses to climate change?

– Multi-seasonal effects: Most species advance with warming 10-20% temperate species currently

showing no response have divergent climatecues, may shift in future

– Mean response is 5-7 days/ºC– Annuals are more sensitive– Sensitivities are similar across habitats– Experiments should be used

cautiously to project responses

How does phenological assemblyin an era of changing climatecontribute to plant invasions?

Time in community ecology theory

Storage effect model uses inter-annualvariability to promote coexistence

Abundanceor relativefrequency

Inter- vs. intra-annual variability

– Invasion biology &phenology Vacant niche Priority effects Plasticity

Extending theory to intra-annual scale

Vacant niche

– Predicts: Exotic species tend toleaf/bloom when native speciesnot in leaf/bloom

Abundanceor relativefrequency

Vacant niche

– Predicts: Exotic species tend toleaf/bloom when native speciesnot in leaf/bloom

Amur honeysuckle(Lonicera maacki) staysgreen late in season

Priority effects

– Predicts: Exotic speciesleaf/bloom earlier than nativespecies

Priority effects

– Predicts: Exotic speciesleaf/bloom earlier than nativespecies

Red brome (Bromusmadritensis ssp. rubens)greens up earlier

Plasticity & climate change

– Predicts: Leafing/blooming of exoticspecies varies across years morethan native species, co-varies withclimate.

Plasticity & climate change

– Predicts: Leafing/blooming of exoticspecies varies across years morethan native species, co-varies withclimate.

Exotic species trackclimate closer inConcord, Massachusetts

Febr

uary

May

Day of year

Wolkovich & Cleland, Frontiers in Ecology & the Environment, 2011

Mixed-effects ANOVA (species as random): F2,84=3.74, p=0.03

Exotics show earlier leafburst

– Citizen science– North Carolina– Budburst/first leaf for

all species– Supports priority

effects– Similar findings using

USDA Plants

Coastal sage scrub

– Non-native grassgrows early

– Senescence 1-2months earlierthan most nativespecies

Invasion alters plant timing of system

Grass invasion alters detritus

Food web effects

Native shrubbiomass

Grazingweb

Non-nativelitter

moisturedecompositionnutrient cycling

Detrital web

omnivorouspredators

Food web vs. Ecosystem effects

Native shrubbiomass

Grazingweb

Non-nativelitter

moisturedecompositionnutrient cycling

Detrital web

omnivorouspredators

How do abiotic and biotic effectsof invasions alter arthropod

communities?

3-yr fieldmanipulation of

grass litter

Possible paths: Top-down via directfood web shifts

Leaf-hoppers

Non-nativelitter

Ground spiders

Web spiders

Possible ecosystem and food webpaths: Bottom-up via quantity

Leaf-hoppers

Native shrub growth

Non-nativelitter

Ground spiders

Web spiders

Possible ecosystem and food webpaths: Bottom-up via quality

Leaf-hoppers

Native shrub growth

Non-nativelitter

Ground spiders

Web spiders

Native shrub leaf %N

Possible paths:Litter to shrub arthropods

Leaf-hoppers

Native shrub growth

Non-nativelitter

Ground spiders

Web spiders


Strong, positive bottom-up effect

Leaf-hoppers

Native shrub growth

Non-nativelitter

Ground spiders

Web spiders


-0.100.06

-0.04-0.003

-0.12

0.58*** 0.84*** 0.62***

0.34*

X2 = 7.24, p = 0.30

AIC = 22.8Wolkovich, Ecology, 2010

No support for direct food web,plant quality effects

Leaf-hoppers

Native shrub growth

Non-nativelitter

Ground spiders

Web spiders


-0.100.06

-0.04-0.003

-0.12

0.58*** 0.84*** 0.62***

0.34*

X2 = 7.24, p = 0.30

AIC = 22.8

Wolkovich, Ecology, 2010

Path analysis supportsshrub growth as only major link

Leaf-hoppers

Native shrub growth

Non-nativelitter

Web spiders

0.58*** 0.85*** 0.60***

X2 = 2.01, p = 0.57

AIC = 10.0

∆ AIC > 5

As compared with 5other a priori modelsWolkovich, Ecology, 2010

Ecosystem shifts drivefood web effects

Leaf-hoppers

Native shrub growth

Non-nativelitter

Web spiders

Wolkovich et al., GlobalChange Biology, 2010


Leaf-hoppers

Native shrub growth

Non-nativelitter

Web spiders

Wolkovich et al.,Journal of Vegetation Science, 2009

– Increased soil moisture→ shrub growth


Leaf-hoppers

Native shrub growth

Non-nativelitter

Web spiders

Wolkovich et al.,Global Change Biology, 2010

– Increased soil moisture →shrub growth

– Rapid 20% increases incarbon and nitrogenstorage via changes: soil community decomposition

– Food web impactsoccur via ecosystemshifts

– Detrital changes dueto invasion have largeimpacts on: Native plants Arthropod food webs Ecosystem C & N Phenology

Impacts of invasion on food webs

What will be species’ responses?

Changes relative to late 20th centuryA2 scenario, IPCC, WG1 summary, 2007

Understanding & predictingcommunities with global change

– Diverse methodswith global data What direct effects

we can predict now– Assembly theory to

predict indirecteffects Role of phenology in

plant invasions

Diverse methods– Spatial gradient

studies– Field experiments– Long-term trends &

time-series– Simulation modeling– Meta-analysis– Comparison across

methods– Robust quantitative

designs

– Building up from directeffects of climate toconsequences on: Species interactions Communities Ecosystem processes

– Controls on tropicalphenology

– Evolutionary constraintson phenology

– Generalizing invasiontheory to communityassembly

Current & Future Research

– Bottom-up andtop-down Nutrient Network


– Bottom-up andtop-down Nutrient Network Top-down across

an invasiongradient


– Bottom-up andtop-down Nutrient Network Top-down across

an invasiongradient

– Climate forcing ofwinegrapes


Acknowledgements

– Doug Bolger & KathyCottingham

– Elsa Cleland & StephHampton

– Forecasting Phenologyworking group

– Ben Cook– David Holway, David Lipson,

John Moore

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