Book chapter for Molecular Approaches in Natural Resource Conservation (J.A 1

DeWoody, J.W. Bickham, C. Michler, K. Nichols, O.E. Rhodes, and K. Woeste, eds.). 2

Scheduled for release in 2009 from Cambridge University Press. 3

4

5

A Community and Ecosystem Genetics Approach to Conservation 6

Biology and Management 7

8

Thomas G. Whitham1,2

, Catherine A. Gehring1,2

, Luke M. Evans1,2

, Carri J. LeRoy3, 9

Randy K. Bangert1,4

, Jennifer A. Schweitzer5, Gerard J. Allan

1,2, Robert C. Barbour

6, 10

Dylan G. Fischer3, Bradley M. Potts

6 and Joseph K. Bailey

5 11

12

13

1Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011 14

USA 15

2Merriam-Powell Center for Environmental Research, Northern Arizona University, 16

Flagstaff, Arizona 86011 USA 17

3Evergreen State College, Olympia, WA 98505 USA 18

4Biological Sciences, Stop 8007, Idaho State University, Pocatello, ID 63209 USA 19

5Department of Ecology and Evolution, University of Tennessee, Knoxville TN 37996 20

USA

21

6School of Plant Science and Cooperative Research Centre for Forestry, University of 22

Tasmania, Private Bag 55, Hobart, Tasmania, 7001 Australia 23

24

25

*Correspondence to: TGW 26

*e-mail: Thomas.Whitham@nau.edu 27

28

2

Abstract 29

Community and ecosystem genetics is the study of the genetic interactions that occur 30

among species and their abiotic environment in complex communities. We argue that by 31

progressing from a population genetics approach focused on individual species to a 32

community genetics approach, conservation genetics and management will be less 33

susceptible to interpretation and management errors. These errors typically arise when 34

management decisions are based upon single species rather than whole communities. 35

Because species evolve in response to interactions among diverse species and their 36

environment, by studying evolution within a community context, conservation genetics 37

can move to a new level of understanding with associated changes in management. For 38

example, from a population genetics perspective, preserving genetic diversity in rare and 39

endangered species is important to prevent inbreeding depression and extinction. 40

However, from a community genetics perspective, preservation of genetic diversity in 41

abundant „foundation species‟ may be more important because as „community drivers‟, 42

the loss of genetic diversity in these foundation species impacts a much larger community 43

of organisms and ecosystem processes. Our studies show that different genotypes of 44

nararowleaf cottonwood (Populus angustifolia) support different communities of canopy 45

arthropods, soil microorganisms, trophic interactions with vertebrates, and ecosystem 46

processes such as leaf litter decomposition and nutrient cycling. Our studies also show 47

that these community and ecosystem phenotypes can be quantified as heritable plant 48

traits. Based on these and other studies from multiple systems, we develop five 49

conservation and management implications. 1. It is important to conserve genetic 50

diversity in foundation species to maintain biodiversity and ecosystem function. 2. 51

3

Managing from a species-area curve perspective may be very different than managing 52

from a genetic diversity-area curve perspective. Do we conserve biodiversity based upon 53

area, genetic diversity of the foundation species or both? 3. We contrast the concept of a 54

minimum viable population (MVP) focused on an individual species with that of a 55

minimum viable interacting population (MVIP), which includes the interactions 56

necessary to support a dependent community or organisms. From a community genetics 57

perspective, the current concept of MVP under-represents the population size necessary 58

to conserve the dependent community, especially when applied to foundation species that 59

affect many other species. 4. Some genetically engineered organisms are likely to have 60

unintended community and ecosystem phenotypes. As part of the approval process, these 61

community and ecosystem phenotypes need to be evaluated. 5. Climate change creates a 62

conundrum with the management practice of restoring only with local genotypes. As 63

climate changes, locally adapted genotypes may no longer be locally adapted. Thus, 64

management based upon the use of genotypes from other regions that are a closer match 65

for what the local environment has become or is predicted to become may result in the 66

preservation of a foundation species and more of its dependent community than a strategy 67

of using only local stock in restoration. Here we discuss these and other implications of 68

incorporating a community genetics approach to conservation biology and management. 69

70

Keywords: climate change, community and ecosystem genetics, conservation biology, 71

foundation species, genetic diversity-area curve, genetic similarity rule, genetically 72

engineered organisms, management, minimum viable interacting population, restoration 73

74

4

Introduction 75

The emerging field of community and ecosystem genetics has so far focused on 76

how the genetic variation in one species can influence the composition of associated 77

communities and ecosystem processes such as decomposition (see definitions Table 1; 78

reviews by Whitham et al. 2003, 2006; Johnson & Stinchcombe 2007; Hughes et al. 79

2008). A key component of this approach has been an emphasis on understanding how 80

the genetics of foundation plant species influence a much larger community. It is 81

reasoned that because foundation species structure their ecosystems by creating locally 82

stable conditions and provide specific resources for diverse organisms (Dayton 1972; 83

Ellison et al. 2005), the genetics of these species as „community drivers‟ are most 84

important to understand and most likely to have cascading ecological and evolutionary 85

effects throughout an ecosystem (Whitham et al. 2006). For example, when a foundation 86

species‟ genotype influences the relative fitness of other species it constitutes an indirect 87

genetic interaction (Shuster et al. 2006), and when these interactions change species 88

composition and abundance among individual tree genotypes, they result in individual 89

genotypes having distinct community and ecosystem phenotypes. Thus, in addition to an 90

individual genotype having the „traditional‟ phenotype that population geneticists 91

typically consider as the expression of a trait in the individual and at the population level, 92

community geneticists must also consider higher-level phenotypes at the community and 93

ecosystem level. This distinctness at levels higher than the population can be quantified 94

as community heritability (i.e., the tendency for related individuals to support similar 95

communities of organisms and ecosystem processes; Whitham et al. 2003, 2006; Shuster 96

et al. 2006). 97

5

Numerous studies in diverse systems demonstrate that community and ecosystem 98

phenotypes exist (e.g., cottonwoods – Dickson & Whitham 1996; Whitham et al. 2003; 99

Schweitzer et al. 2004, 2005a,b, 2008; Bailey et et al. al. 2004, 2006; Fischer 2004, 100

2006a,b, 2007; Wimp et al. 2005, 2007; Shuster et al. 2006; LeRoy et al. 2006, 2007; 101

aspen – Madritch et al. 2006, 2007; willows – Fritz & Price 1988; Hochwender & Fritz 102

2004; metrosideros – Treseder & Vitousek 2001; oaks – Madritch & Hunter 2002, 2004; 103

Tovar-Sanchez & Oyama 2006; aspen - Madritch et al. 2006, 2007; Donaldson & 104

Lindroth 2007; seagrass – Hughes & Stachowicz 2004; goldenrod – Maddox & Root 105

1987; Crutsinger et al. 2006; Crawford et al. 2008; eucalypts – Dungey et al. 2000; 106

Barbour et al. 2009; evening primrose – Johnson & Agrawal 2005; Johnson et al. 2005, 107

2006; Johnson 2008; common reed – Araki et al. 2005; birch – Silfver et al. 2007; alder – 108

Lecerf & Chauvet 2008; grape - English-Loeb & Norton 2006; Baccharis – Rudgers & 109

Whitney 2006). Although most of the species examined in these studies are considered 110

to be foundation species in their respective ecosystems, the evening primrose is a notable 111

exception. Thus, it would appear that a community genetics approach need not be limited 112

to just foundation species. 113

Having demonstrated that different plant genotypes support different 114

communities, new emphasis must be placed on understanding the implications of these 115

findings. For example, relatively few studies have quantified how the genetic variation in 116

foundation species might also affect the evolution of dependent community members 117

(e.g., review by Mopper 1996 and more recent studies on oaks - Mopper et al. 2000; 118

goldenrod - Craig et al. 2007; cottonwood - Evans et al. 2008), how the genetic variation 119

in a foundation species in one community might affect another community (e.g., 120

6

terrestrial affects aquatic – Driebe & Whitham 2000; LeRoy et al. 2006, 2007; Lecerf & 121

Chauvet 2008), and how a community might feed back to affect the survival and 122

performance of the genotype expressing the trait (Fischer et al. 2006; Schweitzer et al. 123

2008). These remain important frontiers of research for a community genetics approach 124

to reach its full conceptual potential. However, based upon our current knowledge, it 125

seems clear that this approach can offer productive insights into conservation biology and 126

management. The remainder of this chapter considers the conservation implications of 127

the existence of heritable community and ecosystem phenotypes. 128

129

Conservation in a community context 130

Two main reasons support a community genetics approach for conservation and 131

management. First, because all species have evolved in a community context of many 132

interacting species and their abiotic environments, it is unrealistic to study any one 133

species in isolation as if it had evolved in a vacuum (e.g., Thompson 2005; Wade 2007). 134

Thus, the community context of species evolution is far more realistic as it considers 135

more of the variables that are likely to have influenced the evolution of any one species. 136

Second, and perhaps most importantly from a manager‟s perspective, a 137

community approach is less likely to result in management errors. For example, as a 138

two-way interaction, mistletoes are clearly parasites of flowering plants. However, few 139

interactions are just two-way interactions. In their studies of one-seed juniper (Juniperus 140

monosperma) and its associated mistletoe (Phoradendron juniperinum), van Ommeren 141

and Whitham (2002) found that the berries produced by both the juniper and mistletoe 142

were primarily dispersed by birds such as Townsend‟s solitaires (Myadestes townsendi). 143

7

If the seeds of mistletoe attract more birds that end up dispersing more juniper berries, 144

then as a three-way interaction, mistletoe might be considered as a mutualist rather than a 145

parasite. In support of this hypothesis, they found that juniper stands with mistletoe 146

attracted up to 3 times more birds than stands with little or no mistletoe. Most 147

importantly, the number of juniper seedlings was more than 2-fold greater in stands with 148

high mistletoe density compared with stands that had little or no mistletoe. Based upon a 149

two-way interaction, a manager would be justified in removing mistletoe to promote tree 150

performance. However, based upon a three-way interaction, the opposite strategy might 151

be warranted in which mistletoe could benefit juniper recruitment by promoting avian 152

seed dispersal. Thus, just by adding one additional species to the analysis, the 153

fundamental relationships of juniper and mistletoe can „switch or reverse‟ from negative 154

to positive, and management decisions would also shift accordingly. Because the species 155

composing a community interact, the sign of the ecological interaction and the 156

appropriate management decision is dependent upon the number of interacting players 157

and their combined interactions. 158

Although the above study demonstrates how a community versus a population 159

analysis can result in a qualitative change in interpretation that affects management 160

decisions, it is important to know whether such reversals in interpretation are common 161

enough to warrant concern. Bailey and Whitham (2007) reviewed 85 studies and 417 162

tests that varied in the number of factors examined in scientific studies, which included 163

numbers of species, local to geographic scale, and number of years of the study (Figure 164

1). When the study included only one factor, there was no chance of detecting either a 165

significant interaction or a reversal in outcome as it is beyond the ability of the statistical 166

8

approach. However, when 2 factors were include in the study, there was a 55% 167

probability of detecting a significant interaction term (solid bars) and this probability 168

increased to nearly 95% when 4 factors were included in the analysis. Note that such an 169

interaction term could be just a „quantitative‟ change in which the sign of an interaction 170

would still remain the same (i.e., negative remains negative, and positive remains 171

positive). More importantly, they found that as the number of factors increased, the 172

number of reversals also increased such that when the analysis included 4 factors, there 173

was a nearly 80% probability that the sign of the interaction of one or more factors 174

reversed from negative to positive or positive to negative. These „qualitative‟ changes in 175

interpretation are the most vexing, as they have a high probability of resulting in 176

fundamental management errors. This study concluded that to avoid such errors, 177

conservation biologists and managers need to adopt a long-term, community approach 178

that covers a large geographic area. 179

Because species interact with one another (i.e., it is a multivariate world), only by 180

including the major interacting species can one really understand the sign of the 181

interactions. This is why a community approach is desirable and is less likely to result in 182

management errors that are both costly and produce unintended effects. 183

184

Heritable community and ecosystem phenotypes 185

To justify a community genetics approach it is essential to demonstrate that 186

community and ecosystem phenotypes exist and that they are heritable. Although 187

numerous studies in diverse systems have shown that different genotypes support 188

different community members and ecosystem processes (see references in introduction), 189

9

only a few studies have quantified the heritability of community and ecosystem 190

phenotypes (e.g., arthropod diversity on evening primrose – Johnson & Agrawal 2005; 191

Johnson et al. 2006; arthropod community composition, soil microbial community 192

composition, and trophic interactions on narrowleaf cottonwood – Shuster et al. 2006; 193

Schweitzer et al. 2008; Bailey et al. 2006, respectively). For example, Figure 2 shows 194

that the canopy arthropod community on narrowleaf cottonwood (Populus angustifolia) 195

was under strong genetic control such that regardless of where replicate clones of the 196

same tree genotype were planted within a common garden, they tended to accumulate and 197

support the same community of arthropods (Shuster et al. 2006). Because this study 198

utilized replicate clones of individual tree genotypes, we calculated broad-sense 199

community heritability (H2

c), which estimates that genetic factors account for about 60% 200

of the variation in the canopy arthropod community (H2

c = 0.60). 201

In another common garden study of the soil microbial community, Schweitzer et 202

al. (2008) assessed the microbial community composition in bulk soil beneath tree 203

canopies using phospholipid fatty acid (PLFA) biomarkers (Figure 2). PLFAs are unique 204

to major taxonomic groups (e.g., gram positive and gram negative bacteria, and fungi) 205

and 30 different groups were identified. Their analysis found a strong tree genetic 206

component to microbial community composition (H2

c = 0.70). Schweitzer et al. (2008) 207

also found soil microbial biomass nitrogen (N) exhibited high broad-sense heritability 208

(H2 = 0.61), indicating a genetic basis for soil N pools stored in microorganims. This 209

latter finding is important because soil microorganisms mediate many ecosystem 210

processes, including litter decomposition, rates of nutrient mineralization, and nutrient 211

pools. Since these nutrient pools are genotype dependent, the formation of different 212

10

microbial communities beneath different tree genotypes may feed back to affect plant 213

fitness. If so, this would establish a crucial link between the microbial community and 214

plant fitness that could pave the way for ecosystem science to be placed within an 215

evolutionary framework (Bever et al. 1997; Hooper et al. 2000; Whitham et al. 2003; 216

Bartelt-Ryser et al. 2005). 217

The potential for similar feedbacks on plant performance also exist aboveground 218

in the interactions of insect herbivores and their avian predators (Figure 2). For example, 219

the presence or absence of the leaf-galling aphid, Pemphigus betae, is determined by 220

susceptible or resistant tree genotypes. This interaction predictably affects other trophic 221

levels and alters the composition of a diverse community of fungi, insects, spiders, and 222

avian predators (Dickson & Whitham 1996; Bailey et al. 2006). In combination, these 223

and other studies described above suggest that indirect genetic interactions among 224

relatively few foundation species (e.g., tree, herbivore, predator, mutualist, and/or 225

pathogen) and their environment may structure the composition and abundance of a much 226

larger community of organisms, which could then feed back to affect the performance of 227

individual tree genotypes. 228

An important goal for understanding the ecology, evolution, conservation and 229

management of any ecosystem is to define how many foundation species (plants, animals, 230

microbes) define a given ecosystem and their extended effects on associated species. 231

Understanding the genetics (genotypic or genetic variation therein) of foundation species 232

in a community context is likely to be more informative than the genetics of most other 233

species (e.g., rare species) and should result in fewer management errors than single 234

factor or population approaches. So far, our findings suggest that the study of relatively 235

11

few foundation species in a community context can tell us much about a larger system 236

involving many species. If correct, then the conservation and management of whole 237

ecosystems may be more tractable than previously thought as all species are not equally 238

important in „driving‟ the system. This does not imply that these species are not 239

important or deserving of preservation. It simply argues that the interactions of relatively 240

few species structure an ecosystem and that many species respond to rather than create 241

the conditions required by other species for their survival. If concentrating on the 242

community genetics of foundation species proves generally correct, it would greatly 243

simplify the dilemma of having to understand the complex interactions of all species to 244

make appropriate management decisions, which is a daunting if not impossible task. 245

246

Conservation consequences of heritable phenotypes 247

If different genotypes of a foundation tree species support different community 248

members and ecosystem processes as the above heritability studies demonstrate, several 249

key predictions can be made, which are of conservation and management value. First, if 250

different tree genotypes support different communities, we would expect that the 251

genotypes that are genetically most similar will support the most similar communities. 252

At both local and regional levels, Bangert et al. (2006a, b) found that genetically similar 253

cottonwoods supported similar arthropod communities, whereas genetically dissimilar 254

plants supported more different communities (i.e., a genetic similarity rule hypothesis). 255

For example, using a Mantel test to compare the molecular marker and arthropod 256

community matrices of individual trees, trees that were the most genetically similar, were 257

also the most similar in their arthropod communities (Figure 3). This pattern holds for 258

12

trees growing in a common garden, for trees growing in the wild in a single river (Figure 259

3; Bangert et al. 2006a), and for trees growing throughout the American West in multiple 260

river systems covering 720,000 km2 (Bangert et al. 2006b). Studies with blue gum 261

(Eucalyptus globulus) in Australia showed a similar pattern in which the most genetically 262

similar trees and geographic races also supported more similar canopy communities 263

(Barbour et al. 2009). In both studies the correlation in divergence in molecular and 264

community variation was mediated through correlated divergence in quantitative traits 265

such as leaf morphology and chemistry. Although it is well known that many specialist 266

insects may show corresponding divergence that is correlated with the divergence of their 267

host plant species (e.g., Abbot & Withgott 2004), it appears that the same pattern holds 268

for less specialized community members. From a conservation perspective, the findings 269

of Figures 2 and 3 argue that genetic diversity in a foundation tree species matters for the 270

dependent community of microbes, arthropods, and vertebrates. 271

Second, if different tree genotypes support different communities, then greater 272

genetic diversity in a foundation species should be directly correlated with greater 273

community diversity. Based on this prediction, Wimp et al. (2004) used AFLP molecular 274

markers to quantify genetic variation in 11 different stands of cottonwoods growing along 275

the Weber River in Utah, U.S.A. The arthropod community was then censused for the 276

same trees and they found that stand genetic diversity accounted for about 60% of the 277

variation in the diversity of an arthropod community composed of 207 species (Figure 278

4). Similar findings were obtained with oaks (Quercus spp.; Tovar-Sanchez & Oyama 279

2006). Experimental studies that manipulated genetic diversity with evening primrose 280

(Oenothera biennis) and goldenrod (Solidago altisima) also showed that genetically 281

13

diverse patches supported significantly greater species richness than monocultures 282

(Johnson et al. 2006; Crutsinger et al. 2006, respectively). 283

Third, if individual plant genotypes support different communities, they also may 284

drive genetic differentiation and speciation in dependent community members. Several 285

studies have found that populations of herbivores are adapted to individual host 286

genotypes (Karban 1989; Mopper 1996; Mopper et al. 1995, 2000; Evans et al. 2008). 287

Because speciation may follow from locally adapted, differentiated populations of 288

arthropods (Via 2001; Dres & Mallet 2002), conserving genotypic diversity in foundation 289

species can conserve locally adapted populations of arthropods. This may extend to 290

multiple trophic levels. For example, multiple herbivores (Stireman et al. 2005) and their 291

parasitoids (Stireman et al. 2006) are genetically differentiated on two closely related 292

sympatric species of Solidago indicating that host plants can affect species‟ evolution at 293

multiple tropic levels. A major conclusion from such studies is that genetic diversity is 294

far more than just an endangered species issue, it is also very important for common 295

foundation species in which different plant genotypes support different community 296

members. 297

Fourth, when different genotypes of a foundation species support different 298

communities, management practices based on species-area relationships may need to 299

shift to genetic diversity-area relationships. In other words, is the species-area curve 300

really driven by area, or is it driven by the genetic diversity of the foundation species, or 301

some combination of both? To answer this question, Bangert et al. (2008) quantified the 302

arthropod species accumulation curve for broadleaf cottonwoods (Figure 5a). This curve 303

shows that as the arthropod communities of more trees are sampled (i.e., area), arthropod 304

14

species richness rises predictably and begins to plateau. This is a common pattern and is 305

a primary justification for the conservation practice of preserving large areas to capture 306

the species pool. However, when molecular marker diversity for the same trees was 307

analyzed to produce a molecular marker richness accumulation curve, the same pattern 308

emerged. This curve shows that as the molecular markers of more trees are sampled (i.e., 309

area), marker richness also rises predictably and begins to plateau. These two curves are 310

remarkably similar. Furthermore, arthropod species richness is highly correlated with 311

molecular marker richness (r2 = 0.92; Figure 5b). This strong correlation raises the 312

issue, is the species-area curve driving biodiversity or is it the genetic diversity of the 313

foundation species that drives biodiversity, or some combination of the two? Although 314

area and genetic diversity of foundation species are undoubtedly autocorrelated, from a 315

manager‟s perspective it is important to know whether a landscape should be managed 316

from an area perspective or from the genetic diversity of a foundation species‟ 317

perspective. Furthermore, if future conservation efforts try to establish living repositories 318

for individual species, it is important to define the genetic diversity of these repositories 319

that will be required to support their dependent communities. 320

321

A community genetics perspective on minimum viable populations 322

Historically, an important concept in conservation biology is the minimum viable 323

population (MVP), which is the size of a population that, with a given probability, will 324

ensure the existence of the population for a stated period of time (Shaffer 1981; Gilpin 325

1987). This concept is especially important for rare and endangered species whose 326

populations are often small, highly inbred and lacking in genetic diversity. While this 327

15

concept attempts to understand what population sizes are required to save a single 328

species, it falls short of understanding the population size that is necessary to save 329

dependent community members. This concern is especially relevant for species that 330

require a single host species or a subset of the host‟s genotypes for their survival. 331

An example of how the distribution, population growth rate and evolution of one 332

species can be dependent upon genotypic differences of another species is that of the gall-333

forming mite, Aceria parapopuli, on cottonwoods. McIntyre and Whitham (2003) found 334

that 99% of the mite‟s population was concentrated on naturally occurring F1 hybrids 335

between narrowleaf and Fremont cottonwood (P. angustifolia, P. fremontii, respectively). 336

Because suitable F1 hybrid hosts are rare in the wild, the „„actual‟‟ host population for 337

these mites is a small subset of the larger cottonwood population. Furthermore, Evans et 338

al. (2008) showed that the mites are even further differentiated such that the mites on the 339

hybrids and parental hosts are different cryptic species. Transfer experiments showed 340

that even among F1 hybrids the mites can be adapted to individual tree genotypes. Thus, 341

even though cottonwoods are very common trees in riparian forests, a dependent species 342

can still be limited by the number of suitable host genotypes. 343

Even generalist species can be genetically differentiated and highly specialized at 344

the local level (Thompson 1994). The eastern tiger swallowtail, Papilio glaucus, has a 345

large list of host species, but at a local scale, it can be very host specific (Scriber 1986). 346

Other examples are provided by Feder et al. (1988), Wood & Keese (1990), Roinen et al. 347

(1993), and Thompson (1994). Microorganims can exhibit even greater specificity. 348

Gene-specific interactions between Rhizobium leguminosarum bv. viciae and native 349

Afghani pea plants (Pisum sativum) regulate symbiosis. The loss of a single gene will 350

16

disrupt the symbiotic interaction and prevent nitrogen fixation (Vijn et al. 1993). 351

Specificity in gene-for-gene interactions in molecular control points between plants and 352

microorganisms in the rhizosphere is thought to be widespread in many foundation 353

species (Hirsch et al. 2003; Phillips et al. 2003). 354

Such specificity suggests that minimum viable population (MVP) sizes (Shaffer 355

1981) in one species may be too small to support important interactions with other 356

species (see Thompson 1994). Other researchers have recognized the weaknesses 357

associated with the conservation of individual species rather than communities or 358

ecosystems (e.g., Rohlf 1991; Simberloff 1998; Lindenmayer et al. 2007). A community 359

genetics approach enhances our toolbox for understanding why the genetics of foundation 360

species are so important, provides genetic-based mechanisms to support these criticisms, 361

and has the potential to merge different points of view as it spans genes-to-ecosystems. 362

Because of these issues, Whitham et al. (2003) proposed the minimum viable 363

interacting population size (MVIP) that represents the size of a population needed to 364

maintain the genetic diversity at levels required by other dependent and interacting 365

species in a community context. For example, minimum viable population (MVP) sizes 366

for conserving a foundation tree species are probably much smaller than the population 367

size required to conserve other dependent community members that require a subset of 368

the tree‟s genotypes for their survival (MVIP). Thus, MVP represents the lower end of 369

the population size that is required to conserve the foundation species, and MVIP 370

represents the upper end of the population size of the foundation species that is required 371

to conserve the dependent community and their interactions. Figure 6 provides an initial 372

guide as to where the differences between MVP for a foundation species and MVIP for a 373

17

foundation species and its dependent community would be expected to be highest and 374

lowest. High community and ecosystem heritability (H2

c) suggests that many community 375

members and the ecosystem processes they drive are dependent on the individual 376

genotypes of a foundation species, whereas low or non-significant H2

c suggests that the 377

distributions of most community members are not genotype dependent. Thus, as 378

community heritability increases for any given foundation species, we would expect that 379

the difference between MVP and MVIP would also increase. 380

MVP and MVIP represent end points on the population to community continuum. 381

They provide a conceptual mechanism for why conservation efforts may flounder if they 382

do not consider the consequences of genetic variation in foundation species on the 383

dependent community. Numerous studies show that temperate and boreal trees exhibit 384

moderate to strong clines in ecologically important traits suggesting that populations are 385

genetically differentiated and locally adapted (Aitken et al. 2008). Associated with this 386

genetic structuring, common garden studies of Barbour et al. (2009) showed that different 387

geographic races of the same eucalypt species supported different communities. 388

Although current conservation strategies target rare species, it may be even more 389

important to conserve the genetic variation in foundation species with broad distributions, 390

because individual genotypes and races have different community and ecosystem 391

phenotypes. 392

393

Community phenotypes of genetically engineered organisms 394

Although the incorporation of novel genes into an organism through recombinant 395

DNA technology has enormous potential benefits, including increasing crop yields and 396

18

reducing reliance on chemical pesticides (Avise 2003), the genes introduced into GEOs 397

(genetically engineered organisms) may have unintended community and ecosystem 398

phenotypes that are important to evaluate. Several issues need to be considered. First, 399

GEOs now have been introduced over large areas in both industrialized and developing 400

economies. For example, the global area planted with transgenic crops exceeded 110 401

million hectares in 2007, a 12% increase over the previous year (Lawrence 2008). 402

Because many of these crops are grown as monocultures with little genetic variation, they 403

are, in effect, both a foundation species and a foundation genotype. 404

Second, there is increasing evidence that transgenic organisms can move beyond 405

their intended destinations where they have the potential to persist and to hybridize with 406

native species. Maximal gene flow distances of 21 km were observed in herbicide 407

(glyphosate) resistant creeping bent grass (Agrostis stolonifera L.), a wind-pollinated out-408

crossing perennial turf grass planted in test fields in Oregon (Watrud et al. 2004). Three 409

years after the test fields were taken out of production, 62% of plants tested were 410

glyphosate-resistant (Zapiola et al. 2008). There is evidence that transgenic canola 411

(Brassica napus) and sunflower (Helianthus annuus) hybridize with wild relatives, 412

though the fitness of these hybrids is highly variable and frequently not greater than wild 413

genotypes (reviewed in Chapman & Burke 2006). Recent research also suggests that the 414

risk of environmental impact from horizontal gene transfer of bacterial genes from crops 415

to surrounding soil microorganisms is much higher than initially supposed (Heinemann & 416

Traavik 2004). In their review, Marvier and Van Acker (2005) concluded that movement 417

of transgenes beyond their intended destinations is a “virtual certainty”. 418

19

A third reason that transgenics may have unintended ecological consequences for 419

communities and ecosystems is that transgenes are designed to have significant fitness 420

consequences (e.g., reducing herbivory, disease or competition) thereby increasing the 421

likelihood that transgenics will interact significantly with members of the communities in 422

which they are introduced. Furthermore, crops with stacked traits (two or more 423

transgenes) are now more widely planted than those containing only insect resistance 424

traits (Lawrence 2008). 425

Transgenics have been shown to have non-target community and ecosystem 426

phenotypes. For example, Bt corn byproducts can enter headwater streams where they 427

are stored, consumed and transported downstream to other bodies of water. Feeding 428

trials in the laboratory indicate that Bt corn residues negatively affect non-target stream 429

insects (Rosi-Marshall et al. 2007). However, recent meta-analyses highlight the need 430

for comparison of the effects of transgenes to those of traditional agricultural practices. 431

An analysis of 42 experiments showed that Bt cotton and corn altered the abundance of 432

some taxa of non-target arthropods, but the effects of the transgenes were much smaller 433

than those of traditional pesticides (Marvier et al. 2007). Likewise, consistent effects of 434

Bt corn, cotton and potatoes on particular functional guilds of non-target arthropods were 435

not observed (Wolfenbarger et al. 2008). A recent review of Bt plant effects on the soil 436

biota also found few significant impacts (Icoz & Stotzky 2008). Note that most studies of 437

non-target effects of transgenic organisms are relatively short-term and thus may ignore 438

effects that accumulate slowly or are triggered by a change in environmental conditions. 439

A community genetics analysis provides a method for quantifying the community 440

and ecosystem phenotypes of transgenes and their heritability. Importantly, in many 441

20

ecological studies, the direct effects of a trait on an organism‟s phenotype (e.g., increased 442

resistance to pest attack) may be less important than the indirect effects on the 443

community phenotype (e.g., increased resistance to pest attack alters competitive 444

interactions with other species to alter community structure). The movement of 445

transgenes into wild populations, via hybridization and introgression, could alter 446

interspecific interactions potentially resulting in changes in the distribution of the wild 447

population and its dependent community. Few studies of GEOs examine the 448

consequences of transgenes on interactions among organisms, particularly indirect 449

interactions. In addition, subtle, initial changes in interactions between transgenic and 450

wild populations may not be detected in short-term studies that are frequently limited in 451

spatial scale as well. The community and ecosystem consequences of transgenes are also 452

important to define because of the growing view that transgenes will escape from their 453

intended destination and that they cannot be retracted once they have escaped (Marvier & 454

Van Acker 2005). Once released, some transgenes may have irreversible conservation 455

implications for communities and ecosystems. These possible negative consequences of 456

GEOs should be considered along with their potential benefits such as promoting 457

agricultural efficiency and reducing pesticide use. 458

459

Community genetics and climate change 460

Climate change is a „wild card‟ in conservation and restoration. Because of 461

concerns about restoring only with locally adapted genotypes, the policies of most 462

agencies require the use of local stock in restoration. While this is a valid view in an 463

unchanging world, in a dynamic world facing major vegetation shifts in response to 464

21

climate change, the „local‟ environment may well shift so fast or so much that local 465

genotypes will no longer be locally adapted (see also Aitken et al. 2008). In the 466

American Southwest, the effects of climate change are pronounced and affecting the 467

distributions of pinyon pine (Pinus edulis), cottonwood (Populus fremontii), ponderosa 468

pine (Pinus ponderosa), aspen (Populus tremuloides), and manzanita (Arctostaphylos 469

pungens), which are foundation species of their respective communities from low 470

elevation deserts to mountain forests (Breshears et al. 2005; Mueller et al. 2005; Gitlin et 471

al. 2006). For example, climate change models of Rehfeldt et al. (2006) predict that the 472

climate envelopes for both pinyon pine (Pinus edulis) and saguaro cactus (Carnegiea 473

gigantea) will shift out of Arizona by 2090 where they are currently foundation species 474

that define their respective communities (Figure 7). Such dramatic shifts in the 475

distributions of these species are likely to represent both ecological and evolutionary 476

events, and major conservation challenges. If these shifts come about, Saguaro National 477

Monument will no longer contain saguaros, and for saguaros to physically reach their 478

new climate envelope in eastern Nevada, human assisted migration will likely be required 479

(McLachlan et al. 2007; Aitken et al. 2008). 480

One factor that can potentially alleviate such dramatic shifts in distribution is the 481

potential for selection to favor more drought adapted genotypes that allow species to 482

persist in areas where they might otherwise go locally extinct. For example, Franks et al. 483

(2007) found rapid evolution in flowering time of field mustard (Brassica rapa) in 484

response to altered growing seasons over just seven years. Post-drought descendants of 485

this annual bloomed earlier than pre-drought ancestors, advancing first flowering by as 486

much as 9 days. The intermediate flowering time of ancestor x descendant hybrids 487

22

supports an additive genetic basis for this divergence. Even vertebrate species appear 488

capable of rapid evolutionary responses to climate change. Over a 30 year period, Grant 489

and Grant (2002) observed rapid evolution in the shape and size of beaks of finches in 490

association with climate change driven changes in seed availability. 491

So, what is the community genetics connection to climate change? If climate 492

change acts as an agent of selection to alter the frequency distribution of genotypes of 493

foundation species, then their dependent communities will also be affected. For example, 494

Whitham et al. (2003) showed that approximately 1000 species of organisms from 495

microbes to vertebrates were differentially associated with insect resistant and susceptible 496

pinyon pine (P. edulis). Recent studies by Sthultz et al. (2009) showed that insect 497

resistant trees were three times more likely to die than susceptible trees during a record 498

drought and that the change in the distribution of resistant and susceptible phenotypes on 499

the landscape had changed from resistant-dominated stands to near parity. Thus, even if 500

a foundation species survived in a given habitat, the change in its genetic structure is 501

likely to affect dependent community members whose distributions are also dependent 502

upon the genotypes of the foundation plant species (see earlier sections). 503

Rehfeldt et al. (2006) used climate envelopes to predict the future distributions of 504

many tree species of the western USA, including P. edulis. They concluded that P. edulis 505

would be extirpated from Arizona within 80 years. This prediction, however, did not 506

include potentially drought adapted genotypes of P. edulis or reflect the genetic variation 507

among populations that might exist over its large geographic distribution. In contrast, a 508

few other models have incorporated intraspecific genetic variation and demonstrated its 509

potential influence on climate change projections. For example, based on provenance 510

23

trials involving 140 populations tested at 62 sites throughout most of the range of 511

lodgepole pine, P. contorta, O‟Neill et al. (2008) included genetic differences in the 512

performance of these populations into their models. Because populations tend to be 513

locally adapted, these models predicted that different populations would vary greatly in 514

their responses to climate change and that many populations would be maladapted to 515

their new local environment. This provenance trial approach also provides an 516

experimental method for identifying which populations and genotypes might best survive 517

predicted climate changes. Thus, for species where such provenance trials already exist, 518

the use of this powerful experimental approach could greatly enhance the precision of 519

climate change models. 520

From these examples, it would appear that climate change creates a conundrum 521

for conservation biologists and managers. If our management policies require restoration 522

with local genotypes, in a climate change scenario are we then planting the very 523

genotypes that are doomed to fail? Few studies with foundation species are in place to 524

provide clear information for managers to make appropriate decisions (but see O‟Neill et 525

al. 2008). We need a national network of common gardens for foundation species in 526

which genotypes from the whole range of a species‟ distribution are planted reciprocally 527

within their current range and their predicted new ranges. Only with such empirical 528

information can we identify the specific genotypes and source populations that are best 529

suited to current and predicted future local conditions. 530

531

Management conclusions 532

24

A community and ecosystem genetics approach is based upon the fundamental finding 533

that individual genotypes or populations of foundation species have heritable community 534

and ecosystem phenotypes (Figure 2). Because this finding has emerged from diverse 535

systems on different continents, our review explores five management implications. 1. 536

Management practices should be based upon conserving genetic diversity in foundation 537

species. Because different genotypes support different communities (Figures 2 & 3), 538

greater biodiversity will be achieved by preserving the greatest genetic diversity in the 539

foundation species (e.g., Figure 4). 2. It is important for managers to determine if they 540

should be managing for area, the genetic diversity of the foundation species or some 541

combination of both. This is due to the fact that the species-area curve (i.e., the current 542

basis of the management practice of setting aside large geographical areas to capture the 543

maximum number of species in preserves) is also highly correlated with the genetic 544

diversity of foundation species-area curve (Figure 5). 3. Managers should incorporate 545

the concept of the minimum viable interacting population (MVIP) to conserve 546

biodiversity. Because the distribution of many species is dependent upon a subset of the 547

genotypes of a foundation species, the population size of the foundation species required 548

to support all of its dependent community members is much larger than that needed to 549

save the foundation species itself (Figure 6). 4. We need management protocols that 550

specifically quantify the unintended ecosystem effects of genetically engineered 551

organisms (i.e., their community and ecosystem phenotypes) as part of the evaluation 552

process for commercial release. 5. Managing for climate change by establishing 553

provenance trials of foundation species throughout their current and predicted range is 554

needed to identify the genotypes and source populations that are most likely to survive, 555

25

reproduce and support their dependent communities in a new climate. These are a few of 556

the management issues that arise when we apply a community and ecosystem genetics 557

approach rather than just a population genetics approach. 558

As an initial step to achieve these goals, it would be especially helpful and cost 559

effective if managers took the additional step of tagging and recording the donors used 560

for propagation (both for seeds and cuttings) and then tagged the individuals used in 561

restoration plantings. It is especially important to do this for each of the known or 562

suspected foundation species as these are the primary „drivers‟ that define and structure 563

the rest of the community. Although such „book keeping‟ would represent a small 564

fraction of the overall cost of a restoration project, the value added could be great and 565

result in cost savings in future restoration projects. If multiple source populations and 566

genotypes were used, managers could identify which genotypes and source populations 567

were best adapted to a specific planting site. Even poorer performers may have high 568

future value because those that perform best under current climatic conditions may not be 569

the best with future climatic conditions (e.g., Sthultz et al. 2009). Performance data 570

combined with climate change models could be used to identify which source populations 571

and genotypes might best suit a predicted future climate. Because many plants used in 572

restoration are long-lived, once restoration sites are established, they can also be used for 573

the life of the site to quantify the diverse communities of organisms they support as a 574

function of the individual genotypes and source populations used in the restoration 575

project. Since species evolve in a community context, then we must ultimately conserve 576

and manage species in a community context. 577

578

26

Acknowledgements 579

We thank an NSF Frontiers of Integrative Biological Research grant for bringing together 580

diverse scientists to help integrate and develop the emerging field of community and 581

ecosystem genetics. NSF IGERT and Science Foundation Arizona grants supported 582

student research; The Australian Research Council (grants DP0451533 and DP0773686) 583

supported parallel studies in Australia, and NSF grant DEB-0816675 supported climate 584

change research in the U.S.A. The Ogden Nature Center, the Utah Department of Natural 585

Resources and Reclamation provided lands for common gardens, restoration and public 586

outreach. 587

588

References Cited 589

Abbot P, Withgott JH (2004) Phylogenetic and molecular evidence for allochronic 590

speciation in gall-forming aphids (Pemphigus). Evolution, 58, 539-553. 591

Aitken SN, Yeaman S, Holliday JA et al. (2008) Adaptation, migration or extirpation: 592

climate change outcomes for tree populations. Evolutionary Applications, 1, 95-593

111. 594

Araki R, Mori M, Mori M et al. (2005) Genetic differences in nitrate uptake in two 595

clones of the common reed, Phragmites australis. Breeding Science, 55, 297-302. 596

Avise JC (2003) The best and the worst of times for evolutionary biology. BioScience, 597

53, 247-255. 598

Bailey JK, Schweitzer JA, Rehill BJ et al. (2004) Beavers as molecular geneticists: A 599

genetic basis to the foraging of an ecosystem engineer. Ecology, 85, 603-608. 600

27

Bailey JK, Whitham TG (2007) Biodiversity is related to indirect interactions among 601

species of large effect. In: Ecological Communities: Plant Mediation in Indirect 602

Interaction Webs (eds Ohgushi T, Craig TP, Price PW), pp. 306-328. Cambridge 603

University Press, UK. 604

Bailey JK, Schweitzer JA, Úbeda F et al. (2009) From genes to ecosystems: A synthesis 605

of the effects of plant genetic factors across levels of organization. Philosophical 606

Transactions of the Royal Society B, (in press). 607

Bailey JK, Wooley SC, Lindroth RL et al. (2006) Importance of species interactions to 608

community heritability: A genetic basis to trophic-level interactions. Ecology 609

Letters, 9, 78-85. 610

Bangert RK, Lonsdorf EV, Wimp GM et al. (2008) Genetic structure of a foundation 611

species: scaling community phenotypes from the individual to the region. 612

Heredity, 100, 121-131. 613

Bangert RK, Turek RJ, Martinsen GD et al. (2005) Benefits of conservation of plant 614

genetic diversity on arthropod diversity. Conservation Biology, 19, 379–390. 615

Bangert RK, Turek RJ, Rehill B et al. (2006) A genetic similarity rule determines 616

arthropod community structure. Molecular Ecology, 15, 1379–1392. 617

Barbour RC, O'Reilly-Wapstra JM, De Little DW et al. (2009) A geographic mosaic of 618

genetic variation within a foundation tree species and its community-level 619

consequences. Ecology (in press). 620

Bartelt-Ryser J, Joshi J, Schmid B et al. (2005) Soil feedbacks of plant diversity on soil 621

microbial communities and subsequent plant growth. Perspectives in Plant 622

Ecology, Evolution and Systematics, 7, 27–49. 623

28

Bever JD, Westover KM, Antonovics J (1997) Incorporating the soil community into 624

plant population dynamics: the utility of the feedback approach. Journal of 625

Ecology, 85, 561–573. 626

Breshears DB, Cobb NS, Rich PM et al. (2005) Regional vegetation die-off in response 627

to global-change-type drought. Proc. Natl. Acad. Sci. USA, 102, 15144–15148. 628

Chapman MA, Burke JM (2006) Letting the gene out of the bottle: the population 629

genetics of genetically modified crops. New Phytologist, 170, 429-443. 630

Craig TP, Itami JK, Craig JV (2007) Host plant genotype influences survival of hybrids 631

between Eurosta solidaginis host races. Evolution, 61, 2607-2613. 632

Crawford KM, Crutsinger GM, Sanders NJ (2007) Host-plant genotypic diversity 633

mediates the distribution of an ecosystem engineer. Ecology, 88, 2114-2120. 634

Crutsinger GM, Collins MD, Fordyce JA et al. (2006) Plant genotypic diversity predicts 635

community structure and governs an ecosystem process. Science, 313, 966–968. 636

Dayton PK (1972) Toward an understanding of community resilience and the potential 637

effects of enrichments to the benthos at McMurdo Sound, Antarctica. In: 638

Proceedings of the Colloquium on Conservation Problems (ed Parker BC), pp. 639

81-96. Allen Press, Lawrence, Kansas. 640

Dickson LL, Whitham TG (1996) Genetically-based plant resistance traits affect 641

arthropods, fungi, and birds. Oecologia, 106, 400–406. 642

Donaldson JR, Lindroth RL (2007) Genetics, environment, and their interaction 643

determine efficacy of chemical defense in trembling aspen. Ecology, 88, 729-739. 644

Dres M, Mallet J (2002) Host races in plant-feeding insects and their importance in 645

sympatric speciation. Phil. Trans. R. Soc. Lon. B. Biol. Sci., 357, 471-492. 646

29

Driebe EM, Whitham TG (2000) Cottonwood hybridization affects tannin and nitrogen 647

content of leaf litter and alters decomposition. Oecologia, 123, 99-107. 648

Dungey HS, Potts BM, Whitham TG et al. (2000) Plant genetics affects arthropod 649

community richness and composition: evidence from a synthetic eucalypt hybrid 650

population. Evolution, 54, 1938–1946. 651

Ellison AM, Bank MS, Clinton BD et al. (2005) Loss of a foundation species: 652

consequences for the structure and dynamics of forested ecosystems. Frontiers in 653

Ecology and the Environment, 3, 479–486. 654

English-Loeb G, Norton A (2006) Lack of trade-off between direct and indirect defence 655

against grape powdery mildew in riverbank grape. Ecological Entomology, 31, 656

415-422. 657

Evans LM, Allan GJ, Shuster SM et al. (2008) Tree hybridization and genotypic variation 658

drive cryptic speciation of a specialist mite herbivore. Evolution, 62, 3027-3040. 659

Feder JL, Chilcote CA, Bush GL (1988) Genetic differentiation between sympatric host 660

races of the apple maggot fly Rhagoletis pomonella. Natur, 336, 61-64. 661

Fischer DG, Hart SC, LeRoy CJ et al. (2007) Decoupled fine root production and total 662

belowground carbon allocation in Populus forests. New Phytologist, 176, 415-663

425. 664

Fischer DG, Hart SC, Rehill BJ et al. (2006) Do high-tannin leaves require more roots? 665

Oecologia, 149, 668-675. 666

Fischer DG, Hart SC, Whitham TG et al. (2004) Ecosystem implications of genetic 667

variation in water-use of a dominant riparian tree. Oecologia, 139, 188-197. 668

30

Fischer DG, Hart SC, Whitham TG et al. (2006) Hidden belowground responses to 669

defense: Do highly defended leaves require more roots? Oecologia, 149, 668-675. 670

Fritz RS, Price PW (1988) Genetic variation among plants and insect community 671

structure: Willows and sawflies. Ecology, 69, 845-856. 672

Gilpin ME (1987) Minimum viable populations: A restoration ecology perspective. In: 673

Restoration Ecology a Synthetic Approach to Ecological Research (eds Pages 674

301-305 in Jorden iii WR, Gilpin ME, Aber JD), pp. 301-305. Cambridge 675

University Press, Cambridge, England. 676

Gitlin AR, Sthultz CM, Bowker MA et al. (2006) Mortality gradients within and among 677

dominant plant populations as barometers of ecosystem change during extreme 678

drought. Conservation Biology, 20, 1477-1486. 679

Grant PR, Grant BR (2002) Unpredictable evolution in a 30-year study of Darwin‟s 680

finches. Science, 296, 707–711. 681

Heinemann JA, Traavik T (2004) Problems in monitoring horizontal gene transfer in field 682

trials of transgenic plants. Nature Biotechnology, 23, 488. 683

Hirsch AM, Bauer WD, Bird DM et al. (2003) Molecular signals and receptors: 684

controlling rhizosphere interactions between plants and other organisms Ecology, 685

84, 858–868 686

Hochwender CG, Fritz RS (2004) Plant genetic differences influence herbivore 687

community structure: evidence from a hybrid willow system. Oecologia, 138, 688

547–557. 689

31

Hooper DU, Bignell DE, Brown VK et al. (2000) Interaction between aboveground and 690

belowground biodiversity in terrestrial ecosystems: patterns, mechanisms and 691

feedbacks. BioScience 50, 1049–1061. 692

Hughes AR, Inouye BD, Johnson MTJ et al. (2008) Ecological consequences of genetic 693

diversity. Ecology Letters, 11, 609-623. 694

Hughes AR., Stachowicz JJ (2004) Genetic diversity enhances the resistance of a 695

seagrass ecosystem to disturbance. Proc. Natl. Acad. Sci. USA, 101, 8998-9002. 696

Icoz I, Stotzky G (2008) Fate and effects of insect-resistant Bt crops in soil ecosystems. 697

Soil Biology and Biochemistry, 40, 559–586 698

Johnson MTJ (2008) Bottom-up effects of plant genotype on aphids, ants and predators. 699

Ecology, 89, 145-154. 700

Johnson MTJ, Agrawal AA (2005) Plant genotype and the environment interact to shape 701

a diverse arthropod community on evening primrose (Oenothera biennsis). 702

Ecology, 18, 549–551. 703

Johnson MTJ, Lajeunesse MJ, Agrawal AA (2006) Additive and interactive effects of 704

plant genotypic diversity on arthropod communities and plant fitness. Ecology 705

Letters, 9, 24–34. 706

Johnson MTJ, Stinchcombe JR (2007) An emerging synthesis between community 707

ecology and evolutionary biology. Trends in Ecology and Evolution, 22, 250-257. 708

Johnson MTJ, Stinchcombe JR (2007) An emerging synthesis between community 709

ecology and evolutionary biology. Trends. Ecol. Evol., 22, 250-257. 710

Karban R (1989) Fine-scale adaptation of herbivorous thrips to individual host plants. 711

Nature, 340, 60-61. 712

32

Keim P, Paige KN, Whitham TG et al. (1989) Genetic analysis of an interspecific hybrid 713

swarm of Populus: occurrence of unidirectional introgression. Genetics, 123, 714

557–565. 715

Lawrence S (2008) Brazil surpasses US in new transgenic crop plantings. Nature 716

Biotechnology, 26, 260. 717

Lecerf A, Chauvet E (2008) Intraspecific variability in leaf traits strongly affects alder 718

leaf decomposition in a stream. Basic and Applied Ecology, 9, 598-605. 719

LeRoy CJ, Whitham TG, Keim P et al. (2006) Plant genes link forests and streams. 720

Ecology, 87, 255-261. 721

LeRoy CJ, Whitham TG, Wooley SC et al. (2007) Within-species variation in foliar 722

chemistry influences leaf-litter decomposition in a Utah river. Journal of the 723

North American Benthological Society, 26, 426-438. 724

Lindenmayer DB, Fischer J, Felton A et al. (2007) The complementarity of single-725

species and ecosystem-oriented research in conservation research. Oikos, 116, 726

1220-1226. 727

Maddox GD, Root RB (1987) Resistance to 16 diverse species of herbivorous insects 728

within a population of goldenrod, Solidago altissima: genetic variation and 729

heritability. Oecologia, 72, 8–14. 730

Madritch MD, Donaldson JR, Lindroth RL (2006) Genetic identity of Populus 731

tremuloides litter influences decomposition and nutrient release in a mixed forest 732

stand. Ecosystems, 9, 528-537. 733

33

Madritch MD, Donaldson JR, Lindroth RL (2007) Canopy herbivory can mediate the 734

influence of plant genotype on soil processes through frass deposition. Soil 735

Biology and Biochemistry, 39, 1192-1201. 736

Madritch MD, Hunter MD (2002) Phenotypic diversity influences ecosystem functioning 737

in an oak sandhills community. Ecology, 83, 2084-2090. 738

Madritch MD, Hunter MD (2004) Phenotypic diversity and litter chemistry affect nutrient 739

dynamics during litter decomposition in a two species mix. Oikos, 105, 125-131. 740

Marvier M, McCreedy C, Regetz J et al. (2007) A meta-anaylsis of effects of Bt cotton 741

and maize on non-target invertebrates. Science, 316,1475-1477. 742

Marvier M, VanAcker R (2005) Can crop transgenes be kept on a leash? Frontiers in 743

Ecology and the Environment, 3, 99-106. 744

McIntyre PJ, Whitham TG (2003) Plant genotype affects long-term herbivore population 745

dynamics and extinction: Conservation implications. Ecology, 84, 311-322. 746

McLachlan JS, Hellman JJ, Schwartz MW (2007) A framework for debate of assisted 747

migration in an era of climate change. Conservation Biology, 21, 297-302. 748

Mopper S (1996) Adaptive genetic structure in phytophagous insect populations. Trends 749

Ecol. Evol., 11, 235-238. 750

Mopper S, Beck M, Simberloff D et al. (1995) Local adaptation and agents of selection in 751

a mobile insect. Evolution, 49, 810-815. 752

Mopper S, Stiling P, Landau K et al. (2000) Spatiotemporal variation in leafminer 753

population structure and adaptation to individual oak trees. Ecology, 81, 1577-754

1587. 755

34

Mueller RC, Scudder CM, Porter ME et al. (2005a) Differential mortality in response to 756

severe drought: implications for long-term vegetation shifts. Journal of Ecology, 757

93, 1085-1093. 758

O‟Neill GA, Hamann A, Wang T (2008) Accounting for population variation improves 759

estimates of the impact of climate change on species‟ growth and distribution. 760

Journal of Applied Ecology, 45, 1040–1049. 761

Parmesan C (2006) Ecological and evolutionary responses to recent climate change. 762

Annual Review of Ecology, Evolution, and Systematics, 37, 637-669. 763

Phillips DA, Ferris H, Cook DR et al. (2003) Molecular control points in rhizosphere 764

food webs. Ecology, 84, 816–826. 765

Rehfeldt GE, Crookston NL, Warwell MV et al. (2006) Empirical analyses of plant-766

climate relationships for the western United States. International Journal of Plant 767

Sciences, 167, 1123-1150. 768

Reusch TBH, Ehlers A, Hammerlie A, et al. (2005) Ecosystem recovery after climatic 769

extremes enhanced by genotypic diversity. Proc. Natl. Acad. Sci. USA, 102, 770

2826-2831. 771

Rohlf, D. J. 1991. Six biological reasons why the Endangered Species Act doesn't work 772

-- and what to do about it. Conservation Biology 5:273-282. 773

Roinen H, Vuorinen J, Tahvanainen J et al. (1993) Host preference and allozyme 774

differentiation in shoot galling sawfly, Euura atra. Evolution, 47, 300-308. 775

Rosi-Marshall EJ, Tank JL, Royer TV et al. (2007) Toxins in transgenic crop byproducts 776

may affect headwater stream ecosystems. Proceedings of the National Academy 777

of Science, 104, 16204-16208. 778

35

Rudgers JA, Whitney KD (2006) Interactions between insect herbivores and a plant 779

architectural dimorphism. Journal of Ecology, 94, 1249-1260. 780

Schweitzer JA, Bailey JK, Fischer DG et al. (2008) Plant-soil microorganism 781

interactions: A heritable relationship between plant genotype and associated soil 782

microorganisms. Ecology, 89, 773-781. 783

Schweitzer JA, Bailey JK, Hart SC et al. (2005a) Nonadditive effects of mixing 784

cottonwood genotypes on litter decomposition and nutrient dynamics. Ecology, 785

86, 2834-2840. 786

Schweitzer JA, Bailey JK, Hart SC et al. (2005b) The interaction of plant genotype and 787

herbivory decelerate leaf litter decomposition and alter nutrient dynamics. Oikos, 788

110, 133–145. 789

Schweitzer JA, Bailey JK, Rehill BJ et al. (2004) Genetically based trait in a dominant 790

tree affects ecosystem processes. Ecology Letters, 7, 127-134. 791

Scriber JM (1986) Origins of the regional feeding abilities in the tiger swallowtail 792

butterfly: ecological monophagy and the Papilio glaucus australis subspecies in 793

Florida. Oecologia, 71, 94-103. 794

Seager R, Ting M, Held I et al. (2007) Model projections of an imminent transition to a 795

more arid climate in southwestern North America. Science, 316, 1181-1184. 796

Shaffer ML (1981) Minimum population sizes for species conservation. BioScience, 31, 797

131–134. 798

Shuster SM, Lonsdorf EV, Wimp GM et al. (2006) Community heritability measures the 799

evolutionary consequences of indirect genetic effects on community structure. 800

Evolution, 60, 991-1003. 801

36

Silfver T, Mikola J, Rousi M et al. (2007) Leaf litter decomposition differs among 802

genotypes in a local Betula pendula population. Oecologia, 152, 707-714. 803

Simberloff D (1998) Flagships, umbrellas, and keystones: is single-species management 804

passe in the landscape era? Biological Conservation, 83, 247-257. 805

Sthultz CM, Gehring CA, Whitham TG (2009) Deadly combination of genes and 806

drought: Increased mortality of herbivore-resistant trees in a foundation species. 807

Global Change Biology (in press). 808

Stireman JO III, Nason JD, Heard S (2005) Host-associated genetic differentiation in 809

phytophagous insects: General phenomenon or isolated exceptions? Evidence 810

from a goldenrod-insect community. Evolution, 59, 2573-2587. 811

Stireman JO III, Nason JD, Heard S et al. (2006) Cascading host-associated genetic 812

differentiation in parasitoids of phytophagous insects. Proceedings of the Royal 813

Society of London, Series B, 273, 523-530. 814

Thompson JN (1994) The Coevolutionary Process. University of Chicago Press, 815

Chicago. 816

Thompson JN (2005) The Geographic Mosaic of Coevolution. University of Chicago 817

Press, ChPicago. 818

Tovar-Sanchez E, Oyama K (2006) Effect of hybridization of the Quercus crassifolia x 819

Quercus crassipes complex on the community structure of endophagous insects. 820

Oecologia, 147, 702–713. 821

Treseder KK, Vitousek PM (2001) Potential ecosystem-level effects of genetic variation 822

among populations of Metrosideros polymorpha from a soil fertility gradient in 823

Hawaii. Oecologia, 126, 266-275. 824

37

van Ommeren RJ, Whitham TG (2002) Changes in interactions between juniper and 825

mistletoe mediated by shared avian frugivores: Parasitism to potential mutualism. 826

Oecologia, 130, 281-288. 827

Via S (2001) Sympatric speciation in animals: the ugly duckling grows up. Trends Ecol. 828

Evol., 16, 381-390. 829

Vijn I, das Neves L, van Kammen A et al. (1993) Nod factors and nodulation in plants. 830

Science, 260, 1764-65. 831

Wade, MJ (2007) The co-evolutionary genetics of ecological communities. Nature 832

Reviews Genetics, 8, 185-195. 833

Wartrud LS, Henry Lee E, Fairbrother A et al. (2004) Evidence for landscape-level, 834

pollen-mediated gene flow from genetically modified creeping bentgrass with 835

CP4 EPSPS as a marker. Proceedings of the National Academy of Science, 101, 836

14533-14538. 837

Whitham TG, Bailey JK, Schweitzer JA et al. (2006) A framework for community and 838

ecosystem genetics: From genes to ecosystems. Nature Reviews Genetics, 7, 510-839

523. 840

Whitham TG, DiFazio SP, Schweitzer JA et al. (2008) Extending genomics to natural 841

communities and ecosystems. Science, 320, 492-495. 842

Whitham TG, Young WP, Martinsen GD et al. (2003) Community and ecosystem 843

genetics: A consequence of the extended phenotype. Ecology, 84, 559-573. 844

Wimp GM, Martinsen GD, Floate KD et al. (2005) Plant genetic determinants of 845

arthropod community structure and diversity. Evolution, 59, 61-69. 846

38

Wimp GM, Wooley S, Bangert RK et al. (2007) Plant genetics predicts intra-annual 847

variation in phytochemistry and arthropod community structure. Molecular 848

Ecology, 16, 5057-5069. 849

Wimp GM, Young WP, Woolbright SA et al. (2004) Conserving plant genetic diversity 850

for dependent animal communities. Ecology Letters, 7, 776-780. 851

Wolfenbarger LL, Naranjo SE, Lundgren JG et al. (2008) Bt crop effects on functional 852

guilds of non-target arthropods: A meta-analysis. PLoS ONE, 3, e2118 853

doi:10.1371/journal.pone.0002118. 854

Wood TK, Keese MC (1990) Host plant-induced assortative mating in Enchenopa tree 855

hoppers. Evolution, 44, 619-28. 856

Zapiola M, Campbell C, Butler M et al. (2008) Escape and establishment of transgenic 857

glyphosate-resistant creeping bentgrass (Agrostis stolonifera) in Oregon, USA: A 858

4-year study. Journal of Applied Ecology, 45, 486-494. 859

860

861

39

Table 1. Definitions of community genetics terms 862

863

Community and ecosystem genetics - The study of the genetic interactions that occur 864

among species and their abiotic environment in complex communities 865

Community and ecosystem phenotypes - The effects of genes at levels higher than the 866

population. These phenotypes result from interspecific indirect genetic effects, 867

which can be summarized as a univariate trait. 868

Community heritability - The tendency for related individuals to support similar 869

communities of organisms and ecosystem processes. 870

Foundation species – Species that structure a community by creating locally stable 871

conditions for other species, and by modulating and stabilizing fundamental 872

ecosystem processes. 873

Minimum viable population (MVP) – The size of a population that, with a given 874

probability, will ensure the existence of the population for a stated period of time. 875

Minimum viable interacting population (MVIP) - The size of a population that is 876

required to maintain genetic diversity at levels required by other dependent and 877

interacting species in a community context. 878

879

880

881

40

Figure Legends 882

883

Figure 1. A survey of the literature shows interaction effects and pattern reversals are 884

common, which argues for a community approach to avoid potentially costly errors in 885

conservation and management decisions. Solid bars show that as the number of factors in 886

a study increases, the observed percentage of significant interaction effects also increases 887

(i.e., both quantitative and qualitative interactions). Open bars show that as the number 888

of factors in a study increases, the observed percentage of reversals also increases (i.e., a 889

qualitative change in the sign of an interaction). Figure from Bailey and Whitham 890

(2007). 891

892

Figure 2. Community and ecosystem phenotypes of individual tree genotypes of 893

Populus angustifolia show broad-sense heritability (H2). Significant heritability of the 894

canopy arthropod community, soil microbial community, trophic interactions between 895

birds and insects, and soil nutrient pools demonstrate how trait variation in a foundation 896

tree can structure communities and ecosystem processes (H2

C for community traits; H2 897

for single ecosystem trait). Because soil microorganisms mediate many ecosystem 898

processes, including litter decomposition and rates of nutrient mineralization, the 899

formation of these communities may feed back to affect plant fitness. Solid lines indicate 900

known interactions; dashed lines indicate possible interactions. Quantitative genetic 901

patterns such as these argue that genomic approaches will enhance our understanding of 902

how interacting community members influence ecosystem processes. Figure from 903

Whitham et al. (2008). 904

41

905

Figure 3. Because genetically similar trees support similar communities of organisms, 906

(i.e., the Genetic Similarity Rule), genetic diversity in a foundation plant species is likely 907

to support greater biodiversity in the associated community. On average, Mantel tests 908

show that arthropod communities become less similar as plant genetic distance increases 909

(i.e., small genetic distance indicates more closely related trees). Genetic distances of the 910

common garden trees (solid circles) were based on RFLP markers. The genetic distances 911

of the naturally growing trees in the wild (open triangles) were based on AFLP marker 912

data and only include the F1 and backcross hybrids, thus removing the leverage of the two 913

pure parental species from the analysis. Figure from Bangert et al. (2006a). 914

915

Figure 4. At the stand level, genetic diversity in a foundation tree species is highly 916

correlated with the diversity of an arthropod community of 207 species. Cottonwood 917

genetic diversity predicted arthropod diversity at a stand level. Each point in the 918

scatterplot represents the Shannon–Weaver index of arthropod diversity (H¢ ) as a 919

function of cottonwood gene diversity (He) in the same stand; a total of 11 stands were 920

analyzed. The diagonal line represents the line of best fit that describes the relationship 921

between increasing arthropod diversity as a result of increasing cottonwood genetic 922

diversity. Figure from Wimp et al. 2004) 923

924

Figure 5. The species-area curve is also a species-genetic diversity curve, which may 925

affect management decisions to preserve communities based on area, genetic diversity of 926

foundation species or both. (a) Scaling in which both arthropods and molecular makers 927

42

scale similarly as a function of the number of broadleaf cottonwoods sampled (sample 928

size or number is also a proxy for area). At their asymptotes, adequate sampling captures 929

most of the arthropod community and genetic diversity, respectively. (b) Consistent with 930

the above similarity in scaling, there is a strong positive relationship between arthropod 931

species richness and marker richness. Because trees were selected at random, no 932

geographic structure is reflected in these patterns. Figure from Bangert et al. (2008). 933

934

Figure 6. This graphical model illustrates the hypothesized relationship between high 935

community heritability (i.e., different genotypes of a foundation species support different 936

communities of organisms and ecosystem processes) and the population size needed to 937

preserve the dependent community. As community heritability increases, the population 938

size of the foundation species must rise to support its dependent community and the 939

ecosystem processes they promote. This community perspective on the minimum viable 940

population (MVP) is termed the minimum viable interacting population (MVIP), which 941

takes into account the genetic-based interactions among species in a community context. 942

943

Figure 7. Climate change is predicted to have rapid and major affects on the distribution 944

of foundation plant species across the American West. Modeled climate profiles for 945

saguaro and pinyon pine are shown for the contemporary climate and for the decades 946

beginning in 2030, 2060, and 2090 (left to right). Figure from Rehfeldt et al. (2006). 947

948

949

43

950 Figure 1 951 952

# of Statistical Factors

1 2 3 4

% o

f S

ign

ific

an

t In

tera

cti

on

Te

rms

0

20

40

60

80

100All Observed Significant Interaction Terms

Observed Interactions Resulting in ReversalsFig. 2

953 954

44

955 956 Figure 2 957

958

959

45

960 Figure 3 961 962

963 964 965 966

967

46

968 Figure 4 969 970

Average cottonwood gene diversity (He)

0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

Shannon-w

eave

r in

dex o

f

arthropod d

ive

rsity (H

')

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

971 972

R2

= 0.591

P = 0.006

47

973 Figure 5 974 975

976 977

978

48

979 Figure 6 980 981

Community Heritability H2C

Low High

Population

size needed

to conserve

dependent

community

MVIP – Minimum Viable Interacting Population

982

49

983 Figure 7 984

985

Projected climate change impacts

on the distribution of saguaro & pinyon pine

Current 2030 2060 2090 986 987