- Email: [email protected]
Advancing realism in biodiversity research
Update Research Focus
Advancing realism in biodiversity research Shahid Naeem Department of Ecology, Evolution, and Environmental Biology, Columbia University, 10th Floor, Schermerhorn Extension, MC5557, 1200 Amsterdam Avenue, New York, NY 10027, USA
Numerous experimental studies suggest that biodiversity loss is detrimental to ecosystem functions such as production and nutrient cycling. These experiments, however, have been criticized as unrealistic because they use combinations of species that do not resemble what is observed in nature. Bracken et al. take a novel approach, using species combinations observed in the field and computer simulations to explore many other combinations. Their approach represents a significant advance in making biodiversity research more realistic.
Biology, geochemistry and the significance of biodiversity Consider a habitat in which all living organisms have been removed. Should a variety of plant, animal and microbial species enter this habitat, it would be transformed. Its geochemistry would become biogeochemistry, meaning that the collective biological processes of the species that entered would be influencing rates of nutrient cycling and energy flow. Phrased another way, biodiversity would influence ecosystem functioning. Over the last decade, the study of the relationship between biodiversity and ecosystem functioning (BEF) has made considerable progress toward developing a predictive understanding of how changes in biodiversity influence biogeochemical processes. This progress owes itself to the steady improvement in the ways researchers have made BEF research more realistic, that is, more closely reflecting the natural systems they investigate. The recent study by Bracken et al. [1] on seaweed biodiversity and tide-pool ecosystem function, described in detail here, represents an important advance in BEF research. The scientific legitimacy of BEF research Like any science, the legitimacy of BEF research rests upon establishing a robust body of findings based on rigorous and realistic experiments. Published BEF studies number in the hundreds, ranging from some of the largest field experiments in ecology (e.g. the BEF experiment in Jena, Germany [2], consisting of an enormous number (490) of plots, some of which are as large as 400 m2) to international projects (e.g. 480 plots spanning 7 European countries [3]). Although the study of BEF was initially controversial, current consensus [4] and meta-analyses [5,6] have established BEF research as a robust body of findings that support significant influences of biodiversity on ecosystem functioning. The question of how realistic these studies have been, however, is another matter. Corresponding author: Naeem, S. ([email protected]).
414
Toward a more realistic approach in BEF research BEF experiments traditionally manipulate biodiversity by generating an array of species combinations drawn randomly from a pool of species, but critics frequently question how realistic this approach is [7]. For example, traditional grassland BEF experiments draw plant species at random from a species pool that contains both grasses and forbs, but some draws yield species combinations that have only forbs and no grasses [8]. Many ecologists, understandably, find such experimental grassland plots that lack grasses unrealistic. Bracken et al. bring realism to focus in an elegant study of the role of seaweed species richness (biodiversity) influencing tide-pool water-column ammonium uptake (ecosystem function). They used 0.4–l tide-pool microcosms stocked with seaweed species found in tide pools at the Bodega Marine Reserve on the northern California coast, USA. Bracken et al. break from the traditional random manipulations in BEF research to manipulate seaweed species richness according to patterns observed in the wild. That is, they used only seaweed compositions that they found in a survey of 50 naturally occurring tide pools. A similar ‘realistic’ approach [9] pre-dates this study, but Bracken et al. went further. They backed up their study by using an in silico experiment, computer simulations of ecosystem function in which biodiversity is manipulated. In silico studies [10–12] generate thousands of combinations of species that permit researchers to explore a much larger array of species combinations than is tractable in field research. Species selection and the biodiversity gradient Bracken et al.’s study draws attention to the two key issues that affect realism in all BEF experiments: species selection and the biodiversity gradient. Species selections range from using whatever is convenient to limiting species to only those found in the ecosystem under investigation. More complicated is the biodiversity gradient. Ideally, the high-diversity endpoint (HDEP) of the biodiversity gradient would contain all species, and the low-diversity endpoint (LDEP) would consist of single species. Removal experiments create the biodiversity gradient by removing species from replicate plots [13] and use control plots (i.e. plots in which no species have been removed) as their HDEP. More common are assembly experiments, which begin by removing all relevant biodiversity and then adding back different numbers of species to create a biodiversity gradient, adding only one species for the LDEP and all species for the HDEP [13]. BEF researchers must also decide which of many possible species combinations they should use in
Update
Trends in Ecology and Evolution
Vol.23 No.8
an experiment. The number of combinations for any level of species richness in a BEF experiment is n!/[r!(n r)!], where n is the number of species in the pool and r is the number of species drawn from the pool [14]. The number of species combinations can be very large even for small experiments. For example, for an experiment with an HDEP of 16 species and LDEP of 1, with 16 levels of species richness (i.e. 1, 2, 3, . . .16), there is a total of 65 535 possible species combinations. With minimal replication, say just three replicates for each combination, that would yield 196 605 replicates. Such large numbers of replicates are simply not feasible and require BEF researchers to make decisions about how best to limit the number of replicates to something more tractable (frequently no more than a few hundred replicates). Dealing with the logistics of combinatorial experiments Every BEF researcher must, by necessity, make a series of decisions concerning how best to reduce the enormous numbers of species combinations such combinatorial experiments generate to something manageable while not compromising realism. Bracken et al., for example, made at least ten decisions that determined the number of replicates they would explore. These were to (i) manipulate only taxonomic diversity rather than both taxonomic and functional diversity, (ii) select an HDEP based on nature rather than convenience, (iii) use a subset of 7 from the pool of available species, (iv) use monocultures as the LDEP, (v) assemble rather than remove species, (vi) use both random and realistic combinations of only 1, 3, 6 and 7 species and omit 2, 4 and 5, (vii) not manipulate other trophic levels, (viii) use equal species densities in each replicate even though, in nature, species are quite different in abundance, (ix) use only combinations likely to occur in nature and (x) supplement the study with an in silico assessment of additional combinations and different densities. The tenth decision, to supplement with an in silico study, is something more BEF researchers should consider, but here too, researchers will have to make decisions about what species combinations they will explore. In an in silico study of 126 tropical tree species [10] for example, there were 8.5 1035 species combinations and 5.4 1039 unique pathways of extinction from the HDEP to the LDEP. It would not be particularly informative to explore so many combinations and pathways. Instead, the authors decided to examine seven specific sets of pathways, each set based on hypotheses of how the traits of tree species affect their probability of local extinction. Similarly, Bracken et al. used an in silico study to examine the two sets of pathways most relevant to their study: pathways involving random extinction and pathways that included only species combinations observed in nature. This in silico study substantially reduces our concerns that only 9 seaweed species combinations were examined out of a possible 127. BEF research in the modern world The number of decisions one makes in BEF experiments and where one feels the experiment falls in the realm of abstract versus realistic illustrate the core issues surrounding the
Figure 1. Species selection and the biodiversity gradient in biodiversity and ecosystem functioning (BEF) research. The bases for selecting species for BEF experiments range from selecting species from natural pools that are presumably shaped by biogeographic processes to arbitrary selection. The bases for the biodiversity gradient can range from using all possible combinations of the selected species to specifically removing species based on their functional traits, for example, using all species found in an ecosystem for the high-diversity endpoint and progressively removing species that are more sensitive to disturbance until one reaches the low-diversity endpoint of monocultures. Note that in silico, or computer-simulated, manipulations of biodiversity are considered ‘experiments’ in the sense that the researcher selects which species to include in the species pool, which species compositions are to be examined and what species abundances are to be used based on what is observed in the field.
efficacy of BEF experiments. Figure 1 illustrates the realm of possible experiments concerning what decisions researchers make about species selection and the biodiversity gradient. Of course, the more system driven (i.e. more ‘realistic’) a study, the less general its prediction. Conversely, the more theory driven a study, the less specific its predictions. Researchers should therefore ask how realistic a BEF experiment needs to be and then take the appropriate steps to achieve that level of realism. Following Bracken et al.’s approach, one would use micro- and mesocosm experiments to test theory and mechanism [15] and use realistic field experiments and in silico simulations to validate micro- and mesocosm findings as well as make specific predictions about the ecosystem under investigation. Such pluralistic, synthetic approaches should be the dominant trend in BEF research as it seeks to provide real-world applications to the widespread problem of biodiversity loss. In a world where humanity’s impact on biodiversity is omnipresent [16] and 40% of land is in farm, pasture, plantation or some other form of management [17], the question of how closely BEF studies reflect what one observes in nature is not merely academic. Realistic manipulations, like those of Bracken et al., are crucial for accurately predicting the ecological significance of biodiversity loss. By contrast, given that managed or restored ecosystems might be assembled in ways that bear little resemblance to the natural systems they replace, empirical studies need not necessarily restrict themselves to realistic combinations. Experiments that improve realism are less 415
Update the arbiters of the legitimacy of BEF research they once used to be than the means by which we can better predict the consequences of local and global changes in biodiversity. Acknowledgements This manuscript benefited from funds from the National Science Foundation, and critical reading by Daniel Bunker, Daniel Flynn, Claire Jouseau, Nicholas Mirotchnik, Elizabeth Nichols, Matt Palmer, Sara Tjossem, Matthew Bracken and two anonymous reviewers.
References 1 Bracken, M.E. et al. (2008) Functional consequences of realistic biodiversity changes in a marine ecosystem. Proc. Natl. Acad. Sci. U. S. A. 105, 924–928 2 Roscher, C. et al. (2005) Overyielding in experimental grassland communities – irrespective of species pool or spatial scale. Ecol. Lett. 8, 419–429 3 Spehn, E.M. et al. (2005) Ecosystem effects of biodiversity manipulations in European grasslands. Ecol. Monogr. 75, 37–63 4 Hooper, D.U. et al. (2005) Effects of biodiversity on ecosystem functioning: a consensus of current knowledge and needs for future research. Ecol. Monogr. 75, 3–35 5 Worm, B. et al. (2006) Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 6 Cardinale, B.J. et al. (2006) Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992
Trends in Ecology and Evolution Vol.23 No.8 7 Raffaelli, D. (2004) How extinction patterns affect ecosystems. Science 306, 1141–1142 8 Tilman, D. et al. (2001) Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 9 Zavaleta, E.S. and Hulvey, K.B. (2004) Realistic species losses disproportionately reduce grassland resistance to biological invaders. Science 306, 1175–1177 10 Bunker, D.E. et al. (2005) Species loss and aboveground carbon storage in a tropical forest. Science 310, 1029–1031 11 Solan, M. et al. (2004) Extinction and ecosystem function in the marine benthos. Science 306, 1177–1180 12 McIntyre, P.B. et al. (2007) Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl. Acad. Sci. U. S. A. 104, 4461–4466 13 Daz, S. et al. (2003) Functional diversity revealed by removal experiments. Trends Ecol. Evol. 18, 140–146 14 Power, M.E. et al. (1996) Challenges in the quest for keystones. Bioscience 46, 609–620 15 Cardinale, B.J. et al. (2007) Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl. Acad. Sci. U. S. A. 104, 18123–18128 16 Kareiva, P. et al. (2007) Domesticated nature: shaping landscapes and ecosystems for human welfare. Science 316, 1866–1869 17 Foley, J.A. et al. (2005) Global consequences of land use. Science 309, 570–574
0169-5347/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.tree.2008.05.003 Available online 24 June 2008
Research Focus
Reciprocal cooperation in avian mobbing: playing nice pays David J. Wheatcroft and Trevor D. Price Committee on Evolutionary Biology, University of Chicago, 1025 East 57th Street, Chicago, IL 60637, USA
Unrelated passerine birds often join together while mobbing, a widespread antipredator behavior during which birds harass a predator. Although previous analyses concluded that mobbing could not have evolved via reciprocity, Krams and colleagues’ field experiments show that birds preferentially join mobs with neighbors that have aided them previously, suggesting that these birds utilize reciprocity-based strategies involving individual recognition and recollection of previous interactions with others. This implies a level of sophistication in bird communities greater than had previously been realized.
Mobbing behavior Mobbing is an important antipredator behavior in many communities of small passerines [1,2]. The formation of a large group might be critical to the success of a mob at driving a predator away [3], but participants suffer costs in the form of increased predation risks [4]. Despite such costs, mobbing behavior is widespread [1]. The prevalence of cooperation between unrelated individuals continues to be a major unresolved question in evolutionary biology, because individuals that do not participate—‘defectors’— Corresponding author: Wheatcroft, D.J. ([email protected]).
416
can benefit from the behavior of others while incurring none of the costs [5,6]. To explain cooperation, research has focused on behavioral strategies, such as reciprocity, that limit the ability of defectors to persist [5,6]. Animals utilizing reciprocity assist others that have aided them previously but ignore defectors, thereby promoting cooperation over defection [5,6] (Figure 1). Among mobile animals, reciprocity requires individual recognition so that animals can remember the results of previous interactions during future encounters [6]. This requirement led pioneers in the field to dismiss reciprocity as an explanation for cooperative mobbing, because seasonal communities of breeding birds were considered anonymous aggregations [1]. However, recent field experiments by Krams and colleagues [7] show that pied flycatchers (Ficedula hypoleuca) cooperate to drive predators away from the nests of neighbors, but only help those neighbors that have aided them in the past. Reciprocal mobbing in pied flycatchers Krams and colleagues [7] tested how pairs of breeding pied flycatchers respond when a neighboring pair defects against them (Figure 2). They conducted experiments at 44 triplets of nest boxes (A, B, and C) occupied by pairs of flycatchers (pairs A, B, and C, respectively). During phase one of their experiment, the researchers encaged pair B while simultaneously presenting a stuffed owl at nest A.
Advancing realism in biodiversity research Shahid Naeem Department of Ecology, Evolution, and Environmental Biology, Columbia University, 10th Floor, Schermerhorn Extension, MC5557, 1200 Amsterdam Avenue, New York, NY 10027, USA
Numerous experimental studies suggest that biodiversity loss is detrimental to ecosystem functions such as production and nutrient cycling. These experiments, however, have been criticized as unrealistic because they use combinations of species that do not resemble what is observed in nature. Bracken et al. take a novel approach, using species combinations observed in the field and computer simulations to explore many other combinations. Their approach represents a significant advance in making biodiversity research more realistic.
Biology, geochemistry and the significance of biodiversity Consider a habitat in which all living organisms have been removed. Should a variety of plant, animal and microbial species enter this habitat, it would be transformed. Its geochemistry would become biogeochemistry, meaning that the collective biological processes of the species that entered would be influencing rates of nutrient cycling and energy flow. Phrased another way, biodiversity would influence ecosystem functioning. Over the last decade, the study of the relationship between biodiversity and ecosystem functioning (BEF) has made considerable progress toward developing a predictive understanding of how changes in biodiversity influence biogeochemical processes. This progress owes itself to the steady improvement in the ways researchers have made BEF research more realistic, that is, more closely reflecting the natural systems they investigate. The recent study by Bracken et al. [1] on seaweed biodiversity and tide-pool ecosystem function, described in detail here, represents an important advance in BEF research. The scientific legitimacy of BEF research Like any science, the legitimacy of BEF research rests upon establishing a robust body of findings based on rigorous and realistic experiments. Published BEF studies number in the hundreds, ranging from some of the largest field experiments in ecology (e.g. the BEF experiment in Jena, Germany [2], consisting of an enormous number (490) of plots, some of which are as large as 400 m2) to international projects (e.g. 480 plots spanning 7 European countries [3]). Although the study of BEF was initially controversial, current consensus [4] and meta-analyses [5,6] have established BEF research as a robust body of findings that support significant influences of biodiversity on ecosystem functioning. The question of how realistic these studies have been, however, is another matter. Corresponding author: Naeem, S. ([email protected]).
414
Toward a more realistic approach in BEF research BEF experiments traditionally manipulate biodiversity by generating an array of species combinations drawn randomly from a pool of species, but critics frequently question how realistic this approach is [7]. For example, traditional grassland BEF experiments draw plant species at random from a species pool that contains both grasses and forbs, but some draws yield species combinations that have only forbs and no grasses [8]. Many ecologists, understandably, find such experimental grassland plots that lack grasses unrealistic. Bracken et al. bring realism to focus in an elegant study of the role of seaweed species richness (biodiversity) influencing tide-pool water-column ammonium uptake (ecosystem function). They used 0.4–l tide-pool microcosms stocked with seaweed species found in tide pools at the Bodega Marine Reserve on the northern California coast, USA. Bracken et al. break from the traditional random manipulations in BEF research to manipulate seaweed species richness according to patterns observed in the wild. That is, they used only seaweed compositions that they found in a survey of 50 naturally occurring tide pools. A similar ‘realistic’ approach [9] pre-dates this study, but Bracken et al. went further. They backed up their study by using an in silico experiment, computer simulations of ecosystem function in which biodiversity is manipulated. In silico studies [10–12] generate thousands of combinations of species that permit researchers to explore a much larger array of species combinations than is tractable in field research. Species selection and the biodiversity gradient Bracken et al.’s study draws attention to the two key issues that affect realism in all BEF experiments: species selection and the biodiversity gradient. Species selections range from using whatever is convenient to limiting species to only those found in the ecosystem under investigation. More complicated is the biodiversity gradient. Ideally, the high-diversity endpoint (HDEP) of the biodiversity gradient would contain all species, and the low-diversity endpoint (LDEP) would consist of single species. Removal experiments create the biodiversity gradient by removing species from replicate plots [13] and use control plots (i.e. plots in which no species have been removed) as their HDEP. More common are assembly experiments, which begin by removing all relevant biodiversity and then adding back different numbers of species to create a biodiversity gradient, adding only one species for the LDEP and all species for the HDEP [13]. BEF researchers must also decide which of many possible species combinations they should use in
Update
Trends in Ecology and Evolution
Vol.23 No.8
an experiment. The number of combinations for any level of species richness in a BEF experiment is n!/[r!(n r)!], where n is the number of species in the pool and r is the number of species drawn from the pool [14]. The number of species combinations can be very large even for small experiments. For example, for an experiment with an HDEP of 16 species and LDEP of 1, with 16 levels of species richness (i.e. 1, 2, 3, . . .16), there is a total of 65 535 possible species combinations. With minimal replication, say just three replicates for each combination, that would yield 196 605 replicates. Such large numbers of replicates are simply not feasible and require BEF researchers to make decisions about how best to limit the number of replicates to something more tractable (frequently no more than a few hundred replicates). Dealing with the logistics of combinatorial experiments Every BEF researcher must, by necessity, make a series of decisions concerning how best to reduce the enormous numbers of species combinations such combinatorial experiments generate to something manageable while not compromising realism. Bracken et al., for example, made at least ten decisions that determined the number of replicates they would explore. These were to (i) manipulate only taxonomic diversity rather than both taxonomic and functional diversity, (ii) select an HDEP based on nature rather than convenience, (iii) use a subset of 7 from the pool of available species, (iv) use monocultures as the LDEP, (v) assemble rather than remove species, (vi) use both random and realistic combinations of only 1, 3, 6 and 7 species and omit 2, 4 and 5, (vii) not manipulate other trophic levels, (viii) use equal species densities in each replicate even though, in nature, species are quite different in abundance, (ix) use only combinations likely to occur in nature and (x) supplement the study with an in silico assessment of additional combinations and different densities. The tenth decision, to supplement with an in silico study, is something more BEF researchers should consider, but here too, researchers will have to make decisions about what species combinations they will explore. In an in silico study of 126 tropical tree species [10] for example, there were 8.5 1035 species combinations and 5.4 1039 unique pathways of extinction from the HDEP to the LDEP. It would not be particularly informative to explore so many combinations and pathways. Instead, the authors decided to examine seven specific sets of pathways, each set based on hypotheses of how the traits of tree species affect their probability of local extinction. Similarly, Bracken et al. used an in silico study to examine the two sets of pathways most relevant to their study: pathways involving random extinction and pathways that included only species combinations observed in nature. This in silico study substantially reduces our concerns that only 9 seaweed species combinations were examined out of a possible 127. BEF research in the modern world The number of decisions one makes in BEF experiments and where one feels the experiment falls in the realm of abstract versus realistic illustrate the core issues surrounding the
Figure 1. Species selection and the biodiversity gradient in biodiversity and ecosystem functioning (BEF) research. The bases for selecting species for BEF experiments range from selecting species from natural pools that are presumably shaped by biogeographic processes to arbitrary selection. The bases for the biodiversity gradient can range from using all possible combinations of the selected species to specifically removing species based on their functional traits, for example, using all species found in an ecosystem for the high-diversity endpoint and progressively removing species that are more sensitive to disturbance until one reaches the low-diversity endpoint of monocultures. Note that in silico, or computer-simulated, manipulations of biodiversity are considered ‘experiments’ in the sense that the researcher selects which species to include in the species pool, which species compositions are to be examined and what species abundances are to be used based on what is observed in the field.
efficacy of BEF experiments. Figure 1 illustrates the realm of possible experiments concerning what decisions researchers make about species selection and the biodiversity gradient. Of course, the more system driven (i.e. more ‘realistic’) a study, the less general its prediction. Conversely, the more theory driven a study, the less specific its predictions. Researchers should therefore ask how realistic a BEF experiment needs to be and then take the appropriate steps to achieve that level of realism. Following Bracken et al.’s approach, one would use micro- and mesocosm experiments to test theory and mechanism [15] and use realistic field experiments and in silico simulations to validate micro- and mesocosm findings as well as make specific predictions about the ecosystem under investigation. Such pluralistic, synthetic approaches should be the dominant trend in BEF research as it seeks to provide real-world applications to the widespread problem of biodiversity loss. In a world where humanity’s impact on biodiversity is omnipresent [16] and 40% of land is in farm, pasture, plantation or some other form of management [17], the question of how closely BEF studies reflect what one observes in nature is not merely academic. Realistic manipulations, like those of Bracken et al., are crucial for accurately predicting the ecological significance of biodiversity loss. By contrast, given that managed or restored ecosystems might be assembled in ways that bear little resemblance to the natural systems they replace, empirical studies need not necessarily restrict themselves to realistic combinations. Experiments that improve realism are less 415
Update the arbiters of the legitimacy of BEF research they once used to be than the means by which we can better predict the consequences of local and global changes in biodiversity. Acknowledgements This manuscript benefited from funds from the National Science Foundation, and critical reading by Daniel Bunker, Daniel Flynn, Claire Jouseau, Nicholas Mirotchnik, Elizabeth Nichols, Matt Palmer, Sara Tjossem, Matthew Bracken and two anonymous reviewers.
References 1 Bracken, M.E. et al. (2008) Functional consequences of realistic biodiversity changes in a marine ecosystem. Proc. Natl. Acad. Sci. U. S. A. 105, 924–928 2 Roscher, C. et al. (2005) Overyielding in experimental grassland communities – irrespective of species pool or spatial scale. Ecol. Lett. 8, 419–429 3 Spehn, E.M. et al. (2005) Ecosystem effects of biodiversity manipulations in European grasslands. Ecol. Monogr. 75, 37–63 4 Hooper, D.U. et al. (2005) Effects of biodiversity on ecosystem functioning: a consensus of current knowledge and needs for future research. Ecol. Monogr. 75, 3–35 5 Worm, B. et al. (2006) Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 6 Cardinale, B.J. et al. (2006) Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992
Trends in Ecology and Evolution Vol.23 No.8 7 Raffaelli, D. (2004) How extinction patterns affect ecosystems. Science 306, 1141–1142 8 Tilman, D. et al. (2001) Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 9 Zavaleta, E.S. and Hulvey, K.B. (2004) Realistic species losses disproportionately reduce grassland resistance to biological invaders. Science 306, 1175–1177 10 Bunker, D.E. et al. (2005) Species loss and aboveground carbon storage in a tropical forest. Science 310, 1029–1031 11 Solan, M. et al. (2004) Extinction and ecosystem function in the marine benthos. Science 306, 1177–1180 12 McIntyre, P.B. et al. (2007) Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl. Acad. Sci. U. S. A. 104, 4461–4466 13 Daz, S. et al. (2003) Functional diversity revealed by removal experiments. Trends Ecol. Evol. 18, 140–146 14 Power, M.E. et al. (1996) Challenges in the quest for keystones. Bioscience 46, 609–620 15 Cardinale, B.J. et al. (2007) Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl. Acad. Sci. U. S. A. 104, 18123–18128 16 Kareiva, P. et al. (2007) Domesticated nature: shaping landscapes and ecosystems for human welfare. Science 316, 1866–1869 17 Foley, J.A. et al. (2005) Global consequences of land use. Science 309, 570–574
0169-5347/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.tree.2008.05.003 Available online 24 June 2008
Research Focus
Reciprocal cooperation in avian mobbing: playing nice pays David J. Wheatcroft and Trevor D. Price Committee on Evolutionary Biology, University of Chicago, 1025 East 57th Street, Chicago, IL 60637, USA
Unrelated passerine birds often join together while mobbing, a widespread antipredator behavior during which birds harass a predator. Although previous analyses concluded that mobbing could not have evolved via reciprocity, Krams and colleagues’ field experiments show that birds preferentially join mobs with neighbors that have aided them previously, suggesting that these birds utilize reciprocity-based strategies involving individual recognition and recollection of previous interactions with others. This implies a level of sophistication in bird communities greater than had previously been realized.
Mobbing behavior Mobbing is an important antipredator behavior in many communities of small passerines [1,2]. The formation of a large group might be critical to the success of a mob at driving a predator away [3], but participants suffer costs in the form of increased predation risks [4]. Despite such costs, mobbing behavior is widespread [1]. The prevalence of cooperation between unrelated individuals continues to be a major unresolved question in evolutionary biology, because individuals that do not participate—‘defectors’— Corresponding author: Wheatcroft, D.J. ([email protected]).
416
can benefit from the behavior of others while incurring none of the costs [5,6]. To explain cooperation, research has focused on behavioral strategies, such as reciprocity, that limit the ability of defectors to persist [5,6]. Animals utilizing reciprocity assist others that have aided them previously but ignore defectors, thereby promoting cooperation over defection [5,6] (Figure 1). Among mobile animals, reciprocity requires individual recognition so that animals can remember the results of previous interactions during future encounters [6]. This requirement led pioneers in the field to dismiss reciprocity as an explanation for cooperative mobbing, because seasonal communities of breeding birds were considered anonymous aggregations [1]. However, recent field experiments by Krams and colleagues [7] show that pied flycatchers (Ficedula hypoleuca) cooperate to drive predators away from the nests of neighbors, but only help those neighbors that have aided them in the past. Reciprocal mobbing in pied flycatchers Krams and colleagues [7] tested how pairs of breeding pied flycatchers respond when a neighboring pair defects against them (Figure 2). They conducted experiments at 44 triplets of nest boxes (A, B, and C) occupied by pairs of flycatchers (pairs A, B, and C, respectively). During phase one of their experiment, the researchers encaged pair B while simultaneously presenting a stuffed owl at nest A.