bc3 seminars: "species interaction networks, global change and restoration" dr. daniel...

Species interac,on networks, global change and restora,on

Daniel Montoya ([email protected])

Bilbao, April 2015

STRUCTURE

STABILITY FUNCTION

STRUCTURE

STABILITY FUNCTION

TIME

Talk outline

1.   Understanding complex communi,es -‐ Structure-‐Stability -‐ Structure-‐Func,oning -‐ Structure-‐Dynamics

2. Responses to perturba,ons & recovery 3. Future direc,ons

Complexity-‐Stability. Background

•  1950s-‐1960s: More complex, more stable Elton, Hutchinson

•  1970s: More complex, less stable Robert May

•  1980s-‐2010s: More complex, more stable? Pimm, Lawton, Bascompte, Olesen, and many more…

•  Today’s challenge: Mul,ple stability + Quan,ta,ve interac,ons

Model. Space & Mul,ple interac,ons

Lurgi, M. et al (Theore,cal Ecology, In press)

/// Mutualism : Antagonism ra,os

Spa,ally-‐explicit model

Individual-‐based



species are becoming weaker (those with less abundant prey).This could in turn cause a shift in the distribution of thestrengths of interactions towards lower values, a distinctivefeature of more stable communities (McCann 2000; Neutelet al. 2002). Interestingly, the distribution of interactionstrengths at the community level was largely affected byMAI ratios, withweaker interactions becomingmore commonin communities with higher MAI ratios. Therefore, a higherfraction of mutualistic species promotes community stabilityby shifting the distribution of interaction strengths towardslower values.

The likely mechanism behind the observed changes in in-teraction strength patterning is a differential spatial aggrega-tion of species per trophic level. Both global (Moran’s I) andlocal (Geary’s C) aggregation metrics were positively influ-enced byMAI ratios at the whole community level, with sometrophic groups displaying a stronger relationship than others.The populations of basal species (plants) were more aggregat-ed at higher MAI ratios. This higher spatial aggregation ofprimary producers is likely due to the fact that mutualisticconsumers take up fewer resources from their interaction part-ners. Populations of mutualistic plants can thus remain more

aggregated due to decreased mortality and hence increasedlocal reproduction. Additionally, given that there are less her-bivore species as MAI ratio increases, non-mutualistic plantsremain more clustered. Regardless of the mechanisms behindthe aggregation of basal species (e.g. decreased mortality, in-creased local reproduction, herbivory release), the effects ofthis aggregation percolates up through the food chains, possi-bly by inducing herbivores (and mutualistic animals) to re-main near aggregated food sources, and hence predator spe-cies become more clustered as MAI ratio increases. In sum-mary, spatial aggregation offers a potential explanation to whyinteractions in the community are becoming weaker in gener-al, as suggested by the decrease in Gq. Consumers will bemore likely to interact with the same prey species if they areaggregated around them, in detriment of their other potentialinteractions as defined in the niche model.

Our results seem to contradict those of Mougi and Kondoh2012, who found that higher levels of mutualisms have adestabilising effect on the communities with a mixture of an-tagonistic and mutualistic interactions. Even though space hasan important influence on the stability of ecological commu-nities (whether natural or artificial), we should not overlook

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Mutualistic plants ***

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Mor

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Non• mutualistic plants ***

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Mor

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Herbivores **

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Top predators ***

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Mor

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Primary predators ***

Fig. 7 Moran’s I spatialaggregation index per trophiclevel as a function of MAI ratio.Points show index values for eachreplicate. Line and shadow oneach plot represent the fit of alinear model to the data and thestandard error of the mean,respectively. ** and ***correspond to p values <0.01 and0.001 for linear models fit to eachdata set, respectively

Theor Ecol

More spa,al aggrega,on with increasing mutualism



with MAI ratio (Fig. 6, p<0.001 for all pairwise comparisonsbetween distributions). This result suggests that mutualisticinteractions make communities more stable by lowering theaverage strength of ecological relationships between species.

MAI ratios did not affect temporal stability (i.e. populationvariability through time), spatial stability (as measured by thechange in the centroid of the species’ spatial range) or the areaand density of species populations. In contrast, higher MAIratios resulted in significantly higher and lower Moran’s I andGeary’s C indexes, respectively (correlation tests using linearmodels yielded F(1273)=29.06, p<0.01 for Moran’s I andF(1273)=24.35, p<0.01 for Geary’s C against MAI ratios),revealing more spatially aggregated populations with increas-ingMAI ratios (Fig. S3). Increases in spatial aggregation weredifferent across trophic levels both at global (Moran’s I) andlocal (Geary’s C) scales. For example, whereas predators andplants got significantly more aggregated as MAI ratio in-creased, the aggregation of mutualistic animals and herbivoreswas either not affected or only weakly affected by changingMAI ratios, respectively (Fig. 7 and S4). We argue that morespatially aggregated populations can be associated with higherreproductive potential stability, as the likelihood of finding areproductive partner in the neighbourhood is higher. From thisperspective, communities in general, and plant and predatorspecies in particular, were thus more stable in terms of speciesreproductive potential as the MAI ratio increased (Fig. 7, S3and S4).

Discussion

The consideration of different interaction types simultaneous-ly within the same ecological network has consistent and pre-dictable effects on community organisation and stabilityacross a gradient of antagonistic vs. mutualistic interactions.We have shown that increasing levels of mutualisms result in

more stable communities. More importantly, increasing theproportion of mutualistic vs. antagonistic interactions (i.e.MAI ratios) influences different dimensions of ecological sta-bility in different ways, although never negatively. Stabilitywas either not influenced by increasing mutualism—in thecases of population stability and species’ spatial distribu-tions—or was positively influenced by them—spatial aggre-gation, distribution of interaction strengths. The question aris-ing is: why were some components of stability affected byMAI ratios and others were not?

Stability of our model communities in terms of the variabil-ity in the population dynamics of their constituent species wasnot affected by the MAI ratio. This could be a consequence ofthe stabilising effect of space on complex communities, as hasbeen previously demonstrated (e.g. (Solé and Bascompte2006)), regardless of the type of interaction considered.Several mechanisms that could yield these stability patternsdue to spatial arrangements within communities, such asmetapopulation dynamics and refugee effects, are in place inour model. Metapopulation dynamics, via the exchange ofindividuals among local populations, could be an importantfactor determining the fate of species, preventing them fromgoing extinct (Hanski 1998). Metapopulation structure in ourmodel communities emerges as a property of the system fromorganisation of individuals at the local scale. Also, the refugeeeffect created by highly aggregated populations (see Fig. 7),which prevents predators from attacking individuals at thecore of these populations, could drive stability at the popula-tion level. Collectively, these factors could have profound im-pacts on the ability of predators to capture prey as mutualismsincrease. It is possible however that the opposite pattern couldarise, whereas a more aggregated prey distribution would al-low predator individuals to find the ‘next’ prey to attack morereadily. This would result in higher attack rates. The emer-gence of this pattern would make communities displaying itless able to persist through time since the predator would forcetheir prey into an extinction vortex. This suggests that a goodbalance between prey aggregation and attack rate must befound to enhance persistence. The key to this balance couldlie on the strength of ecological interactions.

Our results showed that increasing MAI ratios results inmodel communities with a lower quantitative generality(Gq). Because quantitative generality measures the generalityof consumers, this indicates that predators, even when keepingall of their prey species as MAI increases, are becoming morespecialised (i.e. they are more likely to interact with some oftheir prey species than with others). Since our model does notenforce any kind of prey preference or selection, this is exclu-sively a consequence of an increased abundance of those ‘pre-ferred’ prey species. A higher proportion of mutualistic inter-actions promotes the dominance of certain prey species thatare becoming relatively more abundant. As a result and inparallel to this pattern, some of the interactions of generalist

0

100

200

300

400

0 .0025 .005 .0075 .01Interaction strength

Fre

quen

cy

MAI ratio

0.1.2.3.4.5.6.7.8.9 1

Fig. 6 Frequency distributions of interaction strengths in the overallecological network across different values of MAI ratio

Theor Ecol



•  Importance of considering mul,ple interac,on types, and stability metrics

•  Complex feedbacks: topology, space, individual behavior?

with MAI ratio (Fig. 6, p<0.001 for all pairwise comparisonsbetween distributions). This result suggests that mutualisticinteractions make communities more stable by lowering theaverage strength of ecological relationships between species.

MAI ratios did not affect temporal stability (i.e. populationvariability through time), spatial stability (as measured by thechange in the centroid of the species’ spatial range) or the areaand density of species populations. In contrast, higher MAIratios resulted in significantly higher and lower Moran’s I andGeary’s C indexes, respectively (correlation tests using linearmodels yielded F(1273)=29.06, p<0.01 for Moran’s I andF(1273)=24.35, p<0.01 for Geary’s C against MAI ratios),revealing more spatially aggregated populations with increas-ingMAI ratios (Fig. S3). Increases in spatial aggregation weredifferent across trophic levels both at global (Moran’s I) andlocal (Geary’s C) scales. For example, whereas predators andplants got significantly more aggregated as MAI ratio in-creased, the aggregation of mutualistic animals and herbivoreswas either not affected or only weakly affected by changingMAI ratios, respectively (Fig. 7 and S4). We argue that morespatially aggregated populations can be associated with higherreproductive potential stability, as the likelihood of finding areproductive partner in the neighbourhood is higher. From thisperspective, communities in general, and plant and predatorspecies in particular, were thus more stable in terms of speciesreproductive potential as the MAI ratio increased (Fig. 7, S3and S4).

Discussion

The consideration of different interaction types simultaneous-ly within the same ecological network has consistent and pre-dictable effects on community organisation and stabilityacross a gradient of antagonistic vs. mutualistic interactions.We have shown that increasing levels of mutualisms result in

more stable communities. More importantly, increasing theproportion of mutualistic vs. antagonistic interactions (i.e.MAI ratios) influences different dimensions of ecological sta-bility in different ways, although never negatively. Stabilitywas either not influenced by increasing mutualism—in thecases of population stability and species’ spatial distribu-tions—or was positively influenced by them—spatial aggre-gation, distribution of interaction strengths. The question aris-ing is: why were some components of stability affected byMAI ratios and others were not?

Stability of our model communities in terms of the variabil-ity in the population dynamics of their constituent species wasnot affected by the MAI ratio. This could be a consequence ofthe stabilising effect of space on complex communities, as hasbeen previously demonstrated (e.g. (Solé and Bascompte2006)), regardless of the type of interaction considered.Several mechanisms that could yield these stability patternsdue to spatial arrangements within communities, such asmetapopulation dynamics and refugee effects, are in place inour model. Metapopulation dynamics, via the exchange ofindividuals among local populations, could be an importantfactor determining the fate of species, preventing them fromgoing extinct (Hanski 1998). Metapopulation structure in ourmodel communities emerges as a property of the system fromorganisation of individuals at the local scale. Also, the refugeeeffect created by highly aggregated populations (see Fig. 7),which prevents predators from attacking individuals at thecore of these populations, could drive stability at the popula-tion level. Collectively, these factors could have profound im-pacts on the ability of predators to capture prey as mutualismsincrease. It is possible however that the opposite pattern couldarise, whereas a more aggregated prey distribution would al-low predator individuals to find the ‘next’ prey to attack morereadily. This would result in higher attack rates. The emer-gence of this pattern would make communities displaying itless able to persist through time since the predator would forcetheir prey into an extinction vortex. This suggests that a goodbalance between prey aggregation and attack rate must befound to enhance persistence. The key to this balance couldlie on the strength of ecological interactions.

Our results showed that increasing MAI ratios results inmodel communities with a lower quantitative generality(Gq). Because quantitative generality measures the generalityof consumers, this indicates that predators, even when keepingall of their prey species as MAI increases, are becoming morespecialised (i.e. they are more likely to interact with some oftheir prey species than with others). Since our model does notenforce any kind of prey preference or selection, this is exclu-sively a consequence of an increased abundance of those ‘pre-ferred’ prey species. A higher proportion of mutualistic inter-actions promotes the dominance of certain prey species thatare becoming relatively more abundant. As a result and inparallel to this pattern, some of the interactions of generalist

0

100

200

300

400

0 .0025 .005 .0075 .01Interaction strength

Fre

quen

cy

MAI ratio

0.1.2.3.4.5.6.7.8.9 1

Fig. 6 Frequency distributions of interaction strengths in the overallecological network across different values of MAI ratio

Theor Ecol

Talk outline



Biodiversity-‐Ecosystem Func,oning -‐  Few species (mostly grassland

plants) -‐  No trophic levels -‐  Controlled experiments -‐  From single to mulIple funcIons -‐  From species to funcIonal groups

Background

Food web research -‐  MulIple species, trophic levels,

indirect effects -‐  Structure-‐Stability -‐  Surrogate measures of funcIon

Cedar Creek experiment Rooney & McCann (TREE 2012) Norwood food web (Pocock et al. Science 2012)

2. Is there any relaIonship between food web structure and ecosystem funcIoning?

1. How funcIonal diversity is distributed in space?

PORTISHEAD

SAND BAY

STEART

POET'S CORNER, CLEVEDON

Bristol Channel

10 Km

Salt marsh ecosystems

PORTISHEAD

SAND BAY POET'S CORNER, CLEVEDON

STEART

115 islands (27-‐31 per archipelago) Size: 0.2 – 52.4 m2

Distance to mainland: 1.3 – 250 m

Diatoms

Crabs

Gastropods Collembola

Crane flies

Pollinator insects

Amphipods

Money spiders

Carabid beetles

Green algae

Brown algae

Terrestrial plants

ISLAND SALT MARSH FOOD-‐WEB

EXAMPLE ARCHIPELAGO (POET'S CORNER, CLEVEDON)

EXAMPLE ARCHIPELAGO (POET'S CORNER, CLEVEDON)

x 4

115 islands ‘networks of networks’

Montoya et al. (Nat. Commun. 2nd review)


Some islands are funcIonally more ‘important’ than others


SpaIal Generalized EsImaIon EquaIons: Func%onal Diversity ~ Diversity + Network Proper%es + Island Size + Distance

2. RelaIonship food web structure-‐funcIonal diversity?


FuncIonal groups form modules

Modular architectures

increase funcIonal diversity

SpaIal Generalized EsImaIon EquaIons: Func%onal Diversity ~ Diversity + Network Proper%es + Island Size + Distance




Olesen et al. (PNAS 2007), Montoya et al. (Nat. Commun. 2nd review)

FuncIonal groups form modules

Modular architectures

increase funcIonal diversity



Heterogeneous distribuIon à Some islands are funcIonally more ‘important’ than others

FuncIonal diversity increases with modularity à Link between food web structure and funcIoning

CONCLUSIONS



Heterogeneous distribuIon à Some islands are funcIonally more ‘important’ than others

FuncIonal diversity increases with modularity à Link between food web structure and funcIoning

CONCLUSIONS

Structure and func,on are

related in food webs

Talk outline



Jouzel et al. (Science 2013)

Climate Change in the Quaternary

Blois et al. (Science 2013)

Climate Change in the Quaternary

How long-‐term community dynamics and structure at regional scales have changed over the last ≈ 1 million years? Are these changes associated to Quatenary climate changes?

The dataset

-‐  Last 850,000 years (Pleistocene + Holocene periods)

-‐  6 Ime periods: EP, MP, LIM, LGM, H, C -‐  Number of glacial cycles between periods as a proxy for climate changes

(MIS/OIS boundaries) -‐  Large cummulaIve food webs (71 fossil sites IB)

-‐  Large body-‐size mammals (>20Kg)

-‐  Trophic links?

6 regional large mammal communiIes

ExIncIon, immigraIon & turnover rates Random Body size Phylogeny

Food web properIes: SR, L, C, Vul, Gen, P:P, Robustness, Nestedness

ExIncIons not related with body size ImmigraIons: support PCH ExIncIon ≤ ImmigraIon à éSpecies richness (1/20-‐36ky) (1/12-‐36ky)

ExIncIons: affect large-‐bodied and specialist species ImmigraIon rate drops. No new predators. éExIncIon >> ImmigraIon à êSpecies richness (1/0.83ky)

1. How long-‐term community dynamics and structure at regional scales have changed over the last ≈1 my?

Pleistocene Holocene Pleistocene

Holocene

Pleistocene

Holocene

ExIncIons not related with body size ImmigraIons: support PCH ExIncIon ≤ ImmigraIon à éSpecies richness (1/20-‐36ky) (1/12-‐36ky) Network properIes relaIvely constant Stability relaIvely constant

ExIncIons affect large-‐bodied and specialist species ImmigraIon rate drops. No new predators. éExIncIon >> ImmigraIon à êSpecies richness (1/0.83ky) éConnectance êGen, Vul, Pred:Prey Stability??

1. How long-‐term community dynamics and structure at regional scales have changed over the last ≈1 my?

2. Are these changes associated to Quatenary climate changes?

Changes in food web structure between Pleistocene & Holocene mammal communiIes associated with the loss of specialist predators and increases in

connectance following a non-‐random reducIon in community size

•  Dynamic and structural changes in the Holocene associated to anatomically modern humans: H. sapiens as a central node in the network

Conclusions

•  Despite species turnover, Pleistocene communiIes reorganized with the arrival of phylogeneIcally similar species without major changes in food web structure and stability

•  Pleistocene Vs Holocene: Holocene communiIes are remnants of larger Pleistocene megafaunas

Conclusions

Impacts on funcIoning?

Conclusions

Impacts on funcIoning?

Contemporary climate change?

Talk outline



EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE:

15. A. Woolfe et al., PLoS Biol. 3, e7 (2005).16. A. Siepel et al., Genome Res. 15, 1034 (2005).17. L. Z. Holland et al., Genome Res. 18, 1100 (2008).18. Materials and methods are available as supporting

material on Science Online.19. K. S. Pollard, M. J. Hubisz, K. R. Rosenbloom, A. Siepel,

Genome Res. 20, 110 (2010).20. International HapMap Consortium, Nature 449, 851

(2007).21. G. Robertson et al., Nat. Methods 4, 651 (2007).22. G. E. Crawford et al., Genome Res. 16, 123 (2006).23. A. P. Boyle et al., Cell 132, 311 (2008).

24. A. Valouev et al., Nat. Methods 5, 829 (2008).25. M. Ashburner et al., Nat. Genet. 25, 25 (2000).26. C. Y. McLean et al., Nat. Biotechnol. 28, 495

(2010).27. C. J. Bult, Nucleic Acids Res. 36, D724 (2008).28. P. Wu et al., Int. J. Dev. Biol. 48, 249 (2004).Acknowledgments: This work was supported by the Howard

Hughes Medical Institute (C.B.L., S.R.S., D.M.K., D.H.),the NSF (CAREER-0644282 to M.K., DBI-0644111 toA.S.), the NIH (R01-HG004037 to M.K., P50- HG02568to D.M.K., U54-HG003067 to K.L-T., 1U01-HG004695to C.B.L., 5P41-HG002371to B.J.R.), the Sloan

Foundation (M.K.), and the European Science Foundation(EURYI to K.L-T.).

Supporting Online Materialwww.sciencemag.org/cgi/content/full/333/6045/[page]/DC1Materials and MethodsFigs. S1 to S9Tables S1 to S12References (29–49)

10 January 2011; accepted 24 June 201110.1126/science.1202702

Rapid Range Shifts of SpeciesAssociated with High Levelsof Climate WarmingI-Ching Chen,1,2 Jane K. Hill,1 Ralf Ohlemüller,3 David B. Roy,4 Chris D. Thomas1*

The distributions of many terrestrial organisms are currently shifting in latitude or elevation in responseto changing climate. Using a meta-analysis, we estimated that the distributions of species haverecently shifted to higher elevations at a median rate of 11.0 meters per decade, and to higher latitudesat a median rate of 16.9 kilometers per decade. These rates are approximately two and three timesfaster than previously reported. The distances moved by species are greatest in studies showing thehighest levels of warming, with average latitudinal shifts being generally sufficient to track temperaturechanges. However, individual species vary greatly in their rates of change, suggesting that therange shift of each species depends on multiple internal species traits and external drivers of change.Rapid average shifts derive from a wide diversity of responses by individual species.

Threats to global biodiversity from climatechange (1-8) make it important to identifythe rates at which species have already

responded to recent warming. There is strong evi-dence that species have changed the timing oftheir life cycles during the year and that this islinked to annual and longer-term variations intemperature (9–12). Many species have alsoshifted their geographic distributions towardhigher latitudes and elevations (13–17), but thisevidence has previously fallen short of demon-strating a direct link between temperature changeand range shifts; that is, greater range shifts havenot been demonstrated for regions with the high-est levels of warming.

We undertook a meta-analysis of availablestudies of latitudinal (Europe, North America,and Chile) and elevational (Europe, North Amer-ica, Malaysia, and Marion Island) range shifts fora range of taxonomic groups (18) (table S1). Weconsidered N = 23 taxonomic group ! geographicregion combinations for latitude, incorporating764 individual species responses, and N = 31

taxonomic group ! region combinations for ele-vation, representing 1367 species responses. Forthe purpose of analysis, the mean shift across allspecies of a given taxonomic group, in a givenregion, was taken to represent a single value (forexample, plants in Switzerland or birds in NewYork State; table S1) (18).

The latitudinal analysis revealed that spe-cies have moved away from the Equator at a

median rate of 16.9 km decade!1 (mean = 17.6km decade!1, SE = 2.9, N = 22 species group !region combinations, one-sample t test versuszero shift, t = 6.10, P < 0.0001). Weighting eachstudy by the "(number of species) in the group !region combination gave a mean rate of 16.6 kmdecade!1. For elevation, there was a median shiftto higher elevations of 11.0 m uphill decade!1

(mean = 12.2 m decade!1, SE = 1.8, N = 30 spe-cies groups ! regions, one-sample t test versuszero shift, t = 7.04, P < 0.0001). Weighting ele-vation studies by "(number of species) gave amean rate of uphill movement of 11.1 m decade!1.

A previous meta-analysis (14) of distribu-tion changes analyzed individual species, ratherthan the averages of taxonomic groups ! regionsthat we used, and also included data on latitu-dinal and elevational shifts in the same analysis(18). It concluded that ranges had shifted towardhigher latitudes at 6.1 km decade!1 and to high-er elevations at 6.1 m decade!1 (14), whereasthe rates of range shift that we found were sig-nificantly greater [N = 22 species groups ! regions,one-sample t test versus 6.1 km decade!1, t =3.99, P = 0.0007 for latitude; N = 30 groups !regions, one-sample t test versus 6.1 m decade!1,t = 3.49, P = 0.002 for elevation (18)]. Ourestimated mean rates are approximately threeand two times higher than those in (14), for

1Department of Biology, University of York, Wentworth Way,York YO10 5DD, UK. 2Biodiversity Research Center, AcademiaSinica, 128 Academia Road, Section 2, Nankang Taipei 115,Taiwan. 3School of Biological and Biomedical Sciences, andInstitute of Hazard, Risk and Resilience, Durham University,South Road, Durham DH1 3LE, UK. 4Centre for Ecology &Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire,OX10 8BB, UK.

*To whom correspondence should be addressed. E-mail:[email protected]

Fig. 1. Relationship between observed and expected range shifts in response to climate change, for (A)latitude and (B) elevation. Points represent the mean responses (TSE) of species in a particular tax-onomic group, in a given region. Positive values indicate shifts toward the pole and to higher ele-vations. Diagonals represent 1:1 lines, where expected and observed responses are equal. Open circles,birds; open triangles, mammals; solid circles, arthropods; solid inverted triangles, plants; solid square,herptiles; solid diamond, fish; solid triangle, mollusks.

19 AUGUST 2011 VOL 333 SCIENCE www.sciencemag.org1024

REPORTS

Chen et al. (Science 2011)

Climate change. Distribu,on shihs

European Environment Agency

EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE:

15. A. Woolfe et al., PLoS Biol. 3, e7 (2005).16. A. Siepel et al., Genome Res. 15, 1034 (2005).17. L. Z. Holland et al., Genome Res. 18, 1100 (2008).18. Materials and methods are available as supporting

material on Science Online.19. K. S. Pollard, M. J. Hubisz, K. R. Rosenbloom, A. Siepel,

Genome Res. 20, 110 (2010).20. International HapMap Consortium, Nature 449, 851

(2007).21. G. Robertson et al., Nat. Methods 4, 651 (2007).22. G. E. Crawford et al., Genome Res. 16, 123 (2006).23. A. P. Boyle et al., Cell 132, 311 (2008).

24. A. Valouev et al., Nat. Methods 5, 829 (2008).25. M. Ashburner et al., Nat. Genet. 25, 25 (2000).26. C. Y. McLean et al., Nat. Biotechnol. 28, 495

(2010).27. C. J. Bult, Nucleic Acids Res. 36, D724 (2008).28. P. Wu et al., Int. J. Dev. Biol. 48, 249 (2004).Acknowledgments: This work was supported by the Howard

Hughes Medical Institute (C.B.L., S.R.S., D.M.K., D.H.),the NSF (CAREER-0644282 to M.K., DBI-0644111 toA.S.), the NIH (R01-HG004037 to M.K., P50- HG02568to D.M.K., U54-HG003067 to K.L-T., 1U01-HG004695to C.B.L., 5P41-HG002371to B.J.R.), the Sloan

Foundation (M.K.), and the European Science Foundation(EURYI to K.L-T.).

Supporting Online Materialwww.sciencemag.org/cgi/content/full/333/6045/[page]/DC1Materials and MethodsFigs. S1 to S9Tables S1 to S12References (29–49)

10 January 2011; accepted 24 June 201110.1126/science.1202702

Rapid Range Shifts of SpeciesAssociated with High Levelsof Climate WarmingI-Ching Chen,1,2 Jane K. Hill,1 Ralf Ohlemüller,3 David B. Roy,4 Chris D. Thomas1*

The distributions of many terrestrial organisms are currently shifting in latitude or elevation in responseto changing climate. Using a meta-analysis, we estimated that the distributions of species haverecently shifted to higher elevations at a median rate of 11.0 meters per decade, and to higher latitudesat a median rate of 16.9 kilometers per decade. These rates are approximately two and three timesfaster than previously reported. The distances moved by species are greatest in studies showing thehighest levels of warming, with average latitudinal shifts being generally sufficient to track temperaturechanges. However, individual species vary greatly in their rates of change, suggesting that therange shift of each species depends on multiple internal species traits and external drivers of change.Rapid average shifts derive from a wide diversity of responses by individual species.

Threats to global biodiversity from climatechange (1-8) make it important to identifythe rates at which species have already

responded to recent warming. There is strong evi-dence that species have changed the timing oftheir life cycles during the year and that this islinked to annual and longer-term variations intemperature (9–12). Many species have alsoshifted their geographic distributions towardhigher latitudes and elevations (13–17), but thisevidence has previously fallen short of demon-strating a direct link between temperature changeand range shifts; that is, greater range shifts havenot been demonstrated for regions with the high-est levels of warming.

We undertook a meta-analysis of availablestudies of latitudinal (Europe, North America,and Chile) and elevational (Europe, North Amer-ica, Malaysia, and Marion Island) range shifts fora range of taxonomic groups (18) (table S1). Weconsidered N = 23 taxonomic group ! geographicregion combinations for latitude, incorporating764 individual species responses, and N = 31

taxonomic group ! region combinations for ele-vation, representing 1367 species responses. Forthe purpose of analysis, the mean shift across allspecies of a given taxonomic group, in a givenregion, was taken to represent a single value (forexample, plants in Switzerland or birds in NewYork State; table S1) (18).

The latitudinal analysis revealed that spe-cies have moved away from the Equator at a

median rate of 16.9 km decade!1 (mean = 17.6km decade!1, SE = 2.9, N = 22 species group !region combinations, one-sample t test versuszero shift, t = 6.10, P < 0.0001). Weighting eachstudy by the "(number of species) in the group !region combination gave a mean rate of 16.6 kmdecade!1. For elevation, there was a median shiftto higher elevations of 11.0 m uphill decade!1

(mean = 12.2 m decade!1, SE = 1.8, N = 30 spe-cies groups ! regions, one-sample t test versuszero shift, t = 7.04, P < 0.0001). Weighting ele-vation studies by "(number of species) gave amean rate of uphill movement of 11.1 m decade!1.

A previous meta-analysis (14) of distribu-tion changes analyzed individual species, ratherthan the averages of taxonomic groups ! regionsthat we used, and also included data on latitu-dinal and elevational shifts in the same analysis(18). It concluded that ranges had shifted towardhigher latitudes at 6.1 km decade!1 and to high-er elevations at 6.1 m decade!1 (14), whereasthe rates of range shift that we found were sig-nificantly greater [N = 22 species groups ! regions,one-sample t test versus 6.1 km decade!1, t =3.99, P = 0.0007 for latitude; N = 30 groups !regions, one-sample t test versus 6.1 m decade!1,t = 3.49, P = 0.002 for elevation (18)]. Ourestimated mean rates are approximately threeand two times higher than those in (14), for

1Department of Biology, University of York, Wentworth Way,York YO10 5DD, UK. 2Biodiversity Research Center, AcademiaSinica, 128 Academia Road, Section 2, Nankang Taipei 115,Taiwan. 3School of Biological and Biomedical Sciences, andInstitute of Hazard, Risk and Resilience, Durham University,South Road, Durham DH1 3LE, UK. 4Centre for Ecology &Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire,OX10 8BB, UK.

*To whom correspondence should be addressed. E-mail:[email protected]

Fig. 1. Relationship between observed and expected range shifts in response to climate change, for (A)latitude and (B) elevation. Points represent the mean responses (TSE) of species in a particular tax-onomic group, in a given region. Positive values indicate shifts toward the pole and to higher ele-vations. Diagonals represent 1:1 lines, where expected and observed responses are equal. Open circles,birds; open triangles, mammals; solid circles, arthropods; solid inverted triangles, plants; solid square,herptiles; solid diamond, fish; solid triangle, mollusks.

19 AUGUST 2011 VOL 333 SCIENCE www.sciencemag.org1024

REPORTS

Chen et al. (Science 2011)


European Environment Agency

Species respond individually to climate change

Influence on ecological networks?


Enemy escape and species expansion 417

© 2008 The AuthorsJournal compilation © 2008 The Royal Entomological Society, Ecological Entomology, 33, 413–421

were found between populations using different host plants ( Fig. 2b , Mann – Witney, Z = – 0.22, P = 0.827).

Temporal variability in parasitism rate: new vs. long-established populations

Figure 3 shows observed parasitism rate of A. agestis caterpil-lars in four generations over 3 years, at Aston Rowant and Kirkby-on-Bain. Observed parasitism rate did not differ between generation within populations (Aston Rowant: ! 2 = 3.06, P = 0.383 and Kirkby-on-Bain: ! 2 = 2.66, P = 0.447). However, observed para-sitism rate was always higher in the long-established Aston Rowant population than in Kirkby-on-Bain, where colonisation was recent (mean ± SE: 75.0 ± 6.9 Aston Rowant and 33.6 ± 5.4 Kirkby-on-Bain; Mann – Whitney, Z = – 2.31, P < 0.05).

Parasitism rate of Aricia agestis vs. a long established host (Polyommatus icarus)

Sixty-two P. icarus caterpillars from Kirkby-on-Bain and 23 caterpillars from Aston Rowant were collected. Observed parasitism rate differed between the two butterfly species in the newly colonised area at Kirkby-on-Bain ( Fig. 4 , Mann – Whitney, Z = – 2.31, P < 0.05); the small sample size did not allow us to test for differences between species in the long-established pop-ulation at Aston Rowant (only 23 caterpillars of P. icarus were collected). Observed parasitism rate for P. icarus in the newly colonised area was also as high as that observed for A. agestis in the long-established population (mean ± SE: 75.0 ± 6.9 A. agestis in Aston Rowant and 68.0 ± 8.0 P. icarus in Kirkby-on-Bain; P = 0.486).

Figure 5 shows the complex of parasitoid species attacking the larval stage of A. agestis and P. icarus in Aston Rowant (long established) and Kirkby-on-Bain (newly colonised)

populations. Hyposoter notatus was the species responsible for most parasitism of A. agestis in Aston Rowant, but it contributed little to parasitism at Kirkby-on-Bain, where C. astrarches was the main parasitoid. In contrast, P. icarus experienced a high rate of parasitoid attack by H. notatus at Kirkby-on-Bain.

Discussion

Invading insects, and those that are expanding as a result of climate change, may be attacked by fewer parasitoid species and suffer reduced levels of parasitism in newly colonised areas ( Cornell & Hawkins, 1993; Schönrogge et al. , 1995, 1998 ). The results of the present study showed that similar numbers of parasitoid species were attacking A. agestis in newly (five species) and long-established (six species) popula-tions. This is probably because A. agestis parasitoids are not single-host specialists and most of the parasitoid species already occurred far to the north of the distribution of the but-terfly, using alternative hosts such as P. icarus or A. artaxerxes ( Shaw, 1996, 2007, 2008 ). Host recruitment by relatively specialised insect parasitoids seems common when exotic invaders move to an area that contains native congeners ( Keane & Crawley, 2002 ).

Despite the similar species richness of parasitoids in the new part of the range, A. agestis caterpillars nonetheless suffered lower overall parasitism rates compared with caterpillars in the long-established part of the range. They also suffered lower parasitism than did caterpillars of P. icarus , a host species that has been long established in the same northern localities that A. agestis has recently colonised, and also long-established populations of A. artaxerxes in more northern sites (67% para-sitism was recorded by Shaw, 1996 ). This suggests that A. ages-tis has partially escaped from its enemies during its northwards expansion in Britain, and this is not a result of latitudinal pat-terns in parasitism rates.

Fig. 2. Observed parasitism (%) of Aricia agestis caterpillars (i.e. sum of parasitism by all parasitoid species) during the fi rst genera-tion in 2004: (a) populations that differ in the position within the butterfl y range (estab-lished vs. new parts of the range), and (b) populations that differ in the host plant used by the butterfl y ( Helianthemum vs. Geranium / Erodium ). Values are mean + SE and num-bers within bars show sample sizes (numbers of caterpillars collected).

(a) (b)

Established New

0

10

20

30

40

50

60

Obs

erve

d pa

rasi

tism 41

66

Helianthemum Geranium/Erodium

0

10

20

30

40

50

60

3473

Type of population

Menendez et al. (Ecol. Entomol. 2011)

Aricia ages,s


Enemy escape and species expansion 417

© 2008 The AuthorsJournal compilation © 2008 The Royal Entomological Society, Ecological Entomology, 33, 413–421

were found between populations using different host plants ( Fig. 2b , Mann – Witney, Z = – 0.22, P = 0.827).

Temporal variability in parasitism rate: new vs. long-established populations

Figure 3 shows observed parasitism rate of A. agestis caterpil-lars in four generations over 3 years, at Aston Rowant and Kirkby-on-Bain. Observed parasitism rate did not differ between generation within populations (Aston Rowant: ! 2 = 3.06, P = 0.383 and Kirkby-on-Bain: ! 2 = 2.66, P = 0.447). However, observed para-sitism rate was always higher in the long-established Aston Rowant population than in Kirkby-on-Bain, where colonisation was recent (mean ± SE: 75.0 ± 6.9 Aston Rowant and 33.6 ± 5.4 Kirkby-on-Bain; Mann – Whitney, Z = – 2.31, P < 0.05).

Parasitism rate of Aricia agestis vs. a long established host (Polyommatus icarus)

Sixty-two P. icarus caterpillars from Kirkby-on-Bain and 23 caterpillars from Aston Rowant were collected. Observed parasitism rate differed between the two butterfly species in the newly colonised area at Kirkby-on-Bain ( Fig. 4 , Mann – Whitney, Z = – 2.31, P < 0.05); the small sample size did not allow us to test for differences between species in the long-established pop-ulation at Aston Rowant (only 23 caterpillars of P. icarus were collected). Observed parasitism rate for P. icarus in the newly colonised area was also as high as that observed for A. agestis in the long-established population (mean ± SE: 75.0 ± 6.9 A. agestis in Aston Rowant and 68.0 ± 8.0 P. icarus in Kirkby-on-Bain; P = 0.486).

Figure 5 shows the complex of parasitoid species attacking the larval stage of A. agestis and P. icarus in Aston Rowant (long established) and Kirkby-on-Bain (newly colonised)

populations. Hyposoter notatus was the species responsible for most parasitism of A. agestis in Aston Rowant, but it contributed little to parasitism at Kirkby-on-Bain, where C. astrarches was the main parasitoid. In contrast, P. icarus experienced a high rate of parasitoid attack by H. notatus at Kirkby-on-Bain.

Discussion

Invading insects, and those that are expanding as a result of climate change, may be attacked by fewer parasitoid species and suffer reduced levels of parasitism in newly colonised areas ( Cornell & Hawkins, 1993; Schönrogge et al. , 1995, 1998 ). The results of the present study showed that similar numbers of parasitoid species were attacking A. agestis in newly (five species) and long-established (six species) popula-tions. This is probably because A. agestis parasitoids are not single-host specialists and most of the parasitoid species already occurred far to the north of the distribution of the but-terfly, using alternative hosts such as P. icarus or A. artaxerxes ( Shaw, 1996, 2007, 2008 ). Host recruitment by relatively specialised insect parasitoids seems common when exotic invaders move to an area that contains native congeners ( Keane & Crawley, 2002 ).

Despite the similar species richness of parasitoids in the new part of the range, A. agestis caterpillars nonetheless suffered lower overall parasitism rates compared with caterpillars in the long-established part of the range. They also suffered lower parasitism than did caterpillars of P. icarus , a host species that has been long established in the same northern localities that A. agestis has recently colonised, and also long-established populations of A. artaxerxes in more northern sites (67% para-sitism was recorded by Shaw, 1996 ). This suggests that A. ages-tis has partially escaped from its enemies during its northwards expansion in Britain, and this is not a result of latitudinal pat-terns in parasitism rates.

Fig. 2. Observed parasitism (%) of Aricia agestis caterpillars (i.e. sum of parasitism by all parasitoid species) during the fi rst genera-tion in 2004: (a) populations that differ in the position within the butterfl y range (estab-lished vs. new parts of the range), and (b) populations that differ in the host plant used by the butterfl y ( Helianthemum vs. Geranium / Erodium ). Values are mean + SE and num-bers within bars show sample sizes (numbers of caterpillars collected).

(a) (b)

Established New

0

10

20

30

40

50

60

Obs

erve

d pa

rasi

tism 41

66

Helianthemum Geranium/Erodium

0

10

20

30

40

50

60

3473

Type of population

Menendez et al. (Ecol. Entomol. 2011)

Aricia ages,s “Does range expansion leave enemies behind? Bukerfly pathogen and

parasitoid communi,es in response to climate change”

Aricia ages,s

Epargyreus clarus

Pararge aegeria

Aricia ages,s

Epargyreus clarus

Pararge aegeria

!

Summer 2014…..

Winter-‐Spring 2015…..

Talk outline



Pocock, Evans & Memmok (Science 2012)

Species interac,ons: Meta-‐networks

Knight et al. (Nature 2005)

Spa,al scales: From species to ecosystem func,ons & services

Indirect interacIons: PollinaIon near ponds

INDIRECT EFFECTS


NEED FOR A NETWORK

PERSPECTIVE



INDIRECT EFFECTS


NEED FOR A NETWORK

PERSPECTIVE


NEED FOR A LANDSCAPE APPROACH

CROSS-‐HABITAT INTERACTION


Landscape restora,on: Implica,ons for stability

Montoya, Rogers & Memmok (Trends Ecol & Evol. 2012)

Montoya, Rogers & Memmok (Trends Ecol & Evol. 2012)

Landscape restora,on: The more the beker?

-‐ Low spaIal overlap of different ecosystem services (Eigenbrod et al., J. Appl. Ecol. 2010)

-‐ MulI-‐habitat use of many species

Landscape Food-‐Webs Projects (NERC Funding)

Jane Memmor Steve Gregory Daniel Montoya Talya Hacker Nancy Davis Simon Pors Anna Scor Jason Tylianakis

Recovery dynamics

What's the pathway of recovery?

Thomas et al. (Science 2009)

Recovery dynamics


Maculinea arion

Bullock et al. (Trends Ecol. & Evol. 2011)


Recovery dynamics

Montoya, Rogers & Memmok (Trends Ecol. & Evol. 2012)

Recovery dynamics


Remember the model?


///

Stability

Structure

TIME

The model: Simula,ng restora,on

Stability

Structure

TIME

REFERENCE


Stability

Structure

TIME

REFERENCE

RECOVERY DYNAMICS


Model results. Abundance & Shannon

20%


20% 50%


20% 50% 80%


20% 50% 80%

éDestruc,on/Restora,on


20% 50% 80%


20% 50% 80%



Model results. Network & Stability

Ini,al recovery: More unstable

Model results. Conclusions

•  Magnitude of perturba,on affects recovery (the higher the longer): •  No change in species richness, but abundance takes longer to recover •  Shannon index takes longer to recover: same species but more even

à Parerns of dominant species observed in reference communiIes are diluted

•  Temporal stability takes longer to recover the larger the destrucIon

•  IniIal stages of restoraIon: •  Many properIes show contrasIng parerns (e.g. increase and

decrease), this reflecIng transient dynamics à Need for long-‐term monitoring

Restora,on of tropical seed dispersal networks


!15 years 25 years 57 years

15 years old of restora,on


Plant-‐Seed dispersal network

!


15 years 25 years 57 years

!!!


Species richness

Linkage density

Connectance Weighted nestedness

H2’ Modularity Robustness Shannon index

15 years 34 1.21 0.14 13.6 0.51 -‐ 0.66 3.7

25 years 63 2.44 0.15 26.9 0.3 -‐ 0.74 5

57 years 33 1.27 0.15 15.4 0.42 0.51 0.66 3.7

Ribeiro, et al (Restora,on Ecology, Invited resubmission)

!

!


Species richness

Linkage density

Connectance Weighted nestedness

H2’ Modularity Robustness Shannon index

15 years 34 1.21 0.14 13.6 0.51 -‐ 0.66 3.7

25 years 63 2.44 0.15 26.9 0.3 -‐ 0.74 5

57 years 33 1.27 0.15 15.4 0.42 0.51 0.66 3.7

Link to theore,cal results

Ribeiro, et al (Restora,on Ecology, Invited resubmission)

Talk outline



‘Networks of networks’

Temporal dynamics

Quan,fying mul,ple func,ons

Species interac,ons

Real-‐world ecosystems

Quan,fying stability

Biodiversity-‐ Ecosystem Func,oning

Food web ecology

Complexity-‐Stability

Restora,on ecology

Jane Memmor (UK) Marian Yallop (UK) Sol Milne, Sian de Bell (UK) Rothamstead Research (North Wyke) Miguel Lurgi (Australia) Jose M. Montoya (France) Lucy Rogers (UK) Sara Varela (Germany) Hedvig K. Nenzen (Canada) Maaike De Jong (UK) Gary Barker (UK) Jo Morten, Emy Topp (UK) Steve Gregory (UK) Talya Hacker (UK) Nancy Davis, Rose Archer (UK) Simon Pors (UK) Anna Scor (UK) Jason Tylianakis (New Zealand) Fernanda Ribeiro da Silva (Brazil)

Thanks

Rafael Furtado (Brazil) Marco Aurelio Pizo (Brazil) Ricardo Ribeiro Rodrigues (Brazil) David Moreno-‐Mateos (Spain) Holly Jones (USA) Karen Holl (USA) Jose M. Rey (Spain) Peter Jones (USA) Paula Meli (ArgenIna) Michelle McCrackin (Sweden) Ed Barbier (USA)

bc3 seminars: "species interaction networks, global change and restoration" dr. daniel...

Education

stability structure

oning structure

es structure

ons lurgi

prey aggregation

abundant prey

increased ferred prey

bivore species