Showing posts with label phylogeny. Show all posts
Showing posts with label phylogeny. Show all posts

Monday, February 27, 2017

Archiving the genomes of all species

There is so much bad news about global biodiversity, that it is nice to hear about new undertakings and approaches. One of these is the 'Earth BioGenome Project' which proposes to sequence the genomes of the entirety of life on earth. Given that sequencing services have never been more affordable and more available to scientists, without question, though ambitious this is a feasible undertaking. Still, with perhaps 9 million eukaryotes on the planet, a rough prediction suggests it could take 10 years and several billion dollars to achieve.

The cost suggests a certain agony of choice - what is the best use of that amount of money (in the dream world where money can be freely moved between projects)? Direct application to conservation and management activities, or a catalog of diversity which may be the only way to save some of these species? 
Leonard Eisenberg's tree of life (https://www.evogeneao.com).

Monday, July 18, 2016

The Forest, the Trees, and the Phylo-diversity Jungle

with Florent Mazel

As has been a recurrent topic on the blog recently (here, and here and elsewhere), it is difficult to know when it is appropriate and worthwhile to write responses to published papers. Further, a number of journals don't provide clear opportunities for responses even when they are warranted. And maybe, even when published, most responses won't make a difference anyways. 

Marc Cadotte and I and our coauthors experienced this first hand when we felt a paper of ours had been misconstrued. We wanted to provide a useful, positive response, but whether the time investment was worthwhile was unclear. The journal then informed us they didn't publish responses. We tried instead to write a 'News and Views' piece for the journal, which it ultimately declined to publish. And really, a response piece is at cross-purposes from the usual role of N&V (positive editorials). In the end, rather than spend more time on this, we made the manuscript available as a preprint, found here

The initial response was to a publication in Ecography from Miller et al. (2016) [citations below]. Their paper that does a nice job of asking how well 32 phylo-diversity metrics and nine null models discriminate between community assembly mechanisms. The authors first simulated communities under three main assembly rules, competitive exclusion, habitat filtering, and neutral assembly. They then tested which combination of metrics and null models yielded the best statistical performance. Surprisingly, only a fraction of phylo-diversity metrics and null models exhibited both high statistical power coupled with low Type I error rate. Miller et al. conclude that, for this reason, some metrics and null models proposed in the literature should be avoided when asking if filtering and competition play an important role in structuring communities. This is a useful extension for the eco-phylogenetic literature. However, the authors also argue that their results show that a framework for phylodiversity metrics introduced in a paper by myself and coauthors (Tucker et al. 2016) was subjective and should not be used. 

What was disappointing is that there is a general issue (how can we best understand phylogenetic metrics for ecology?) that could benefit from further discussion in the literature.

Metrics can be analysed and understood in two ways: (1) by grouping them based on their underlying properties (e.g. by comparing mathematical formulations); and (2) by assessing context-dependent behaviour (e.g. by comparing metric performance in relation to particular questions). The first approach requires theoretical and cross-disciplinary studies to summarize the main dimensions along which phylo-diversity metrics vary, while the second provides a field-specific perspective to quantify the ability of a particular metric to test a particular hypothesis. These two approaches have different aims, and their results are not necessarily expected to be identical.

One reason there are so many metrics is that they have been pooled across community ecology, macroecology and conservation biology. The questions typically asked by conservationists and macroecologists, for example, differ from those of community ecologists. Different metrics frequently perform better or worse for different types of problems. The second approach to metrics provides a solution to this problem through explicitly simulating the processes of interest for a given research question (e.g. vicariance or diversification processes in macroecological research), and selecting the most appropriate metric for the task. The R package presented by Miller et al., as well as others (e.g. Pearse et al. 2015) all help facilitate this approach. And it can be very useful to a field when this is done thoroughly.

But this approach has some limitations as well - it is inefficient and sensitive to choices made in the simulation process. It also doesn't provide a framework or context in which to understand results. The general approach fills this need: the Tucker et al. paper took this approach and classified 70 phylo-diversity metrics along three broad mathematical dimensions: richness, divergence and regularity--the sum, mean and variance of phylogenetic distances among species of assemblages, respectively. This framework is analogous to a system for classifying functional diversity metrics (e.g. Villéger et al. 2008), allowing theoretical linkages between phylogenetic and functional approaches in ecology. We also carried out extensive simulations to corroborate the metric behaviour classification system across different assembly scenarios.

The minor point to me is that, although Miller et al. concluded this tripartite framework performed poorly, their results appear to provide independent support for the tripartite classification system. (And this is despite some methodological differences, including using a clustering algorithm instead of an ordination approach for metric grouping). The vast majority of metrics used by Miller et al. on their simulated communities group according to this richness-divergence-regularity classification system (see our Fig 2 vs. Miller et al.'s Fig 1B). And metrics like HAED and EED, which stem from a mathematical combination of richness and regularity dimensions, are expected to sometimes cluster with richness (as observed by Miller et al. but noted as evidence against our framework), and sometimes with regularity. There is specific discussion on this type of behaviour in Tucker et al., 2016.
Tucker et al. Fig. 2. "Principal components analysis for Spearman’s correlations between the a-diversity metrics shown in Table 1. Results represent measures taken from 800 simulated landscapes, based on 100 simulated phylogenetic trees and eight landscape types defined in Table 2 (see Appendix S2) for detailed methods. (A) All metrics excluding abundance-weighted metrics and those classified as parametric indices. (B) As in A, but with abundance-weighted metrics included (underlined). (C) As in B, but with parametric indices (black), and indices that incorporate multiple dimensions (underlined) included (e.g. all a-diversity metrics). X and Y axes are scaled to reflect explained variance (PC1 = 41.8%; PC2 = 20.5% for the PCA performed with all metrics, shown in (C))." 

Miller et al. Fig 1B.  "Dendrogram of intercorrelations among the phylogenetic community structure metrics, including species richness itself (labeled richness). Group 1 metrics focus on mean relatedness; Group 2 on nearest-relative measures of community relatedness; and Group 3 on total community diversity and are particularly closely correlated with species richness. Four metrics, PAE, EED , IAC, and EAED show variable behavior. They do not consistently cluster together or with each other, and we refer to their placement as unresolved. The branches of the dendrogram are colored according to the metric classifications proposed by Tucker et al. (2016): green are “regularity” metrics, pink are “richness” metrics, and yellow are “divergence” metrics."
The major point is that dismissing general approaches can lead to more confusion about phylogenetic metrics, leading users to create even more metrics (please don't!), to conclude that particular metrics should be discarded, or to adopt hard-to-interpret metrics because some study found they were highly correlated with a response. Context is necessary.

I think both approaches have utility, and importantly, both approaches benefit each other. On one hand, detailed analyses of metric performance offer a valuable test of the broader classification system, using alternative simulations and codes. On the other hand, broad syntheses offer a conceptual framework within which results of more focussed analyses may be interpreted.

For example, comparing Miller et al.'s results with the tripartite framework provides some additional interesting insight. They found that metrics closely aligned with only a single dimension are not the best indicators of community assembly. In their results, sometimes the metrics with the best statistical performances are Rao’s quadratic entropy and IntraMPD. Because of the general framework, we know that these classified as are 'hybrid' metrics that include both richness and divergence in phylogenetic diversity. Taking it one step further, because the general framework connects with functional ecology metrics, we can compare their findings about Rao's QE/IntraMPD to results using corresponding dimensions in the functional trait literature. Interestingly, functional ecologists have found that community assembly processes can alter multiple dimensions of diversity (e.g. both richness and divergence)(Botta-Dukát and Czúcz 2016), which may provide insight to why a hybrid metric is useful for understanding community assembly.

In summary, there is both a forest and individual trees, and both of these are valid approaches. I hope that we can continue complement broad-scale syntheses with question- and hypothesis-specific studies, and that as a result the field can be clarified.

References:
Botta-Dukát, Z. and Czúcz, B. 2016. Testing the ability of functional diversity indices to detect trait convergence and divergence using individual-based simulation. - Methods Ecol. Evol. 7: 114–126. 

Bryant, J. A. et al. 2008. Microbes on mountainsides: contrasting elevational patterns of bacterial and plant diversity. - Proc. Natl. Acad. Sci. U. S. A. 105: 11505–11. 

Graham, C. H. and Fine, P. V. A. 2008. Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. - Ecol. Lett. 11: 1265–1277. 

Hardy, O. 2008. Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. - J. Ecol. 96: 914–926. 

Isaac, N. J. B. et al. 2007. Mammals on the EDGE: conservation priorities based on threat and phylogeny. - PLoS One 2: e296. 

Kraft, N. J. B. et al. 2007. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. - Am. Nat. 170: 271–283. 

Pavoine, S. and Bonsall, M. B. 2011. Measuring biodiversity to explain community assembly: a unified approach. - Biol. Rev. 86: 792–812. 

Pearse, W. D. et al. 2014. Metrics and Models of Community Phylogenetics. - In: Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, pp. 451–464. 

Pearse, W. D. et al. 2015. pez : phylogenetics for the environmental sciences. - Bioinformatics 31: 2888–2890. 

Tucker, C. M. et al. 2016. A guide to phylogenetic metrics for conservation, community ecology and macroecology. - Biol. Rev. Camb. Philos. Soc. doi: 10.1111/brv.12252.

Vellend, M. et al. 2010. Measuring phylogenetic biodiversity. - In: McGill, A. E. M. B. J. (ed), Biological diversity: frontiers in measurement and assessment. Oxford University Press, pp. 193–206. 

Villéger, S. et al. 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. - Ecology 89: 2290–2301. 

Webb, C. O. et al. 2002. Phylogenies and Community Ecology. - Annu. Rev. Ecol. Evol. Syst. 33: 475–505. 

Winter, M. et al. 2013. Phylogenetic diversity and nature conservation: where are we? - Trends Ecol. Evol. 28: 199–204.

Wednesday, May 4, 2016

The future of community phylogenetics

Community phylogenetics has received plenty of criticism over the last ten years (e.g. Mayfield and Levine, 2010; Gerhold et al. 2015). Much of the criticism is tied to concerns about pattern-based inference, the use of proxy variables, and untested assumptions. These issues are hardly unique to community phylogenetics, and I think that few ideas are solely ''good or solely 'bad'. They are useful in moulding our thinking as ecologists and inspiring new directions of thought. Many influential ideas in ecology have bobbled in confidence through time, but remain valuable nonetheless [e.g. interspecific competition, character displacement (Schoener 1982; Strong 1979)]. But still, it can be hard to see exactly how to use phylogenetic distances to inform community-level analyses in a rigorous way. Fortunately, there is research showing exactly this. The key, to me at least, to avoid treating a phylogeny as just another matrix to analyze, but to consider and test the mechanisms that might link the outcome of millions of years of evolution to community-level interactions.

A couple of potential approaches to move forward questions about community phylogenetics are discussed below. The first is to consider the mechanisms behind the pattern-inference analyses and ask whether assumptions hold.

1) Phylogenies and traits - testing assumptions about proxy value
As you know, if you have read the introductory paragraph of many community phylogenetic papers, Charles Darwin was the first to highlight that two closely related species might have different interactions than two distantly related species. People have tested this hypothesis in many ways in various systems, with mixed results. The most important directions forward is to make explicit the assumptions behind such ideas and experimentally test them. I.e. Do phylogenetic distances/divergence between species capture trait and ultimately ecological divergence between species?

From Kelly et al. 2015 Fig 1b.
Because evolutionary divergence should relate to feature divergence (sensu Faith), the most direct question to ask is how functionally important trait differences increase with increasing phylogenetic distances. For example, Kelly et al. (2014) found that “close relatives share more features than distant relatives but beyond a certain threshold increasingly more distant relatives are not more divergent in phenotype”, although in a limited test based only on patristic distances. This suggests that at short distances, phylogenetic distances may be a reasonable proxy for feature divergence, but that the relationship is not useful for making predictions about distant relatives.

Phylogenies and coexistence/competition. Ecological questions about communities may not be interested in traits alone. The key assumption behind many early analyses was that closely related species shared more similar *niches*, and so competed more strongly than distantly related species. Thus the question is one step removed from trait evolution, asking instead how phylogenetic divergence correlates into fitness differences or interaction strength. Not surprisingly, current papers suggest there is a fairly mixed, less predictable relationship between phylogenetic relatedness and competitive outcomes.

Recent findings have varied from “Stabilising niche differences were unrelated to phylogenetic distance, while species’ average fitness showed phylogenetic structure” (California grassland plants, Godoy et al. 2014); to, there is no signal in fitness or niche differences (algae species, Narwani et al. 2013); to, when species are sympatric, both stabilizing and fitness differences increase with phylogenetic distance (mediterranean annual plants; Germain et al. 2016). Given constraints, tradeoffs and convergence of strategies, it is really not surprising that the idea of simply inferring the importance of competition from patterns along a phylogenetic tree is not generally possible (Kraft et al. 2015; blogpost).

2) Phylogenies and the regional species pool
Really more interesting than testing for proxy value is to think about the mechanisms that tie evolution and community dynamics together. A key role for evolution in questions about community ecology is to ask what we can learn about the regional species pool—from which local communities are assembled. What information about the history of the lineages in a regional species pool informs the composition of local composition?

The character of the regional species pool is determined in part by the evolutionary history of the region, and this can in turn greatly constrain the evolutionary history of the community (Bartish et al. 2010). The abundance of past habitat types may alter the species pool, while certain communities may act as 'museums' harbouring particular clades. For example, Bartish et al. 2016 found that the lineages represented in different habitat types in a region differ in the evolutionary history they represent, with communities in dry habitats disproportionately including lineages from dry epochs and similar for wet habitats. Here, considering the phylogeny provides insight into the evolutionary component of an ecological idea like 'environmental filtering'.

Similarly, species pools are formed by both ecological processes (dispersal and constraints on dispersal) and evolutionary ones (extinctions, speciation in situ), and one suggestion is that appropriate null models for communities may need to consider both ecological and evolutionary processes (Pigot and Etienne, 2015).
Invasive species also should be considered in the context of evolution and ecology. Gallien et al. 2016 found that “currently invasive species belong to lineages that were particularly successful at colonizing new regions in the past.”

I think using phylogenies in this way is philosophically in line with ideas like Robert Ricklef's 'regional community' concept. The recognition is that a single time scale may be limiting in terms of understanding ecological communities.

References:
  1. Mayfield, Margaret M., and Jonathan M. Levine. "Opposing effects of competitive exclusion on the phylogenetic structure of communities." Ecology letters 13.9 (2010): 1085-1093.
  2. Gerhold, Pille, et al. "Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better)." Functional Ecology 29.5 (2015): 600-614.
  3. Schoener, Thomas W. "The controversy over interspecific competition: despite spirited criticism, competition continues to occupy a major domain in ecological thought." American Scientist 70.6 (1982): 586-595. 
  4. Strong Jr, Donald R., Lee Ann Szyska, and Daniel S. Simberloff. "Test of community-wide character displacement against null hypotheses." Evolution(1979): 897-913. 
  5. Kelly, Steven, Richard Grenyer, and Robert W. Scotland. "Phylogenetic trees do not reliably predict feature diversity." Diversity and distributions 20.5 (2014): 600-612.
  6. Godoy, Oscar, Nathan JB Kraft, and Jonathan M. Levine. "Phylogenetic relatedness and the determinants of competitive outcomes." Ecology Letters17.7 (2014): 836-844.
  7. Narwani, Anita, et al. "Experimental evidence that evolutionary relatedness does not affect the ecological mechanisms of coexistence in freshwater green algae." Ecology Letters 16.11 (2013): 1373-1381.
  8. Rachel M. Germain, Jason T. Weir, Benjamin Gilbert. Species coexistence: macroevolutionary relationships and the contingency of historical interactions. Proc. R. Soc. B 2016 283 20160047
  9. Nathan J. B. Kraft, Oscar Godoy, and Jonathan M. Levine. Plant functional traits and the multidimensional nature of species coexistence. 2015. PNAS.
  10. Bartish, Igor V., et al. "Species pools along contemporary environmental gradients represent different levels of diversification." Journal of Biogeography 37.12 (2010): 2317-2331.
  11. IV Bartish, WA Ozinga, MI Bartish, GW Wamelink, SM Hennekens. 2016. Different habitats within a region contain evolutionary heritage from different epochs depending on the abiotic environment. Global Ecology and Biogeography
  12. Pigot, Alex L., and Rampal S. Etienne. "A new dynamic null model for phylogenetic community structure." Ecology letters 18.2 (2015): 153-163.
  13. Gallien, L., Saladin, B., Boucher, F. C., Richardson, D. M. and Zimmermann, N. E. (2016), Does the legacy of historical biogeography shape current invasiveness in pines?. New Phytol, 209: 1096–1105.

Wednesday, March 23, 2016

The evolutionary canary in the coal mine*

*note -this post originally appeared on the Applied Ecologist's blog

Like canaries in coal mines, species can provide important information about deteriorating environmental conditions. A whole sub-discipline of environmental biomonitoring has emerged to provide the necessary tools to evaluate biological responses to changes in environmental conditions. While historically biomonitoring focused on contaminant concentrations in sentinel species –such as heavy metals in clams; modern biomonitoring uses information across multiple biological levels of organisation, from tissues, to organism behaviour, to the abundances and distributions of species. Since it is impossible to assess every aspect of an ecosystem’s response to pollution, scientists and practitioners still need to make decisions about which elements of an ecosystem should be monitored.
A coal miner with a canary –the classic sentinel species (url for photo: http://www.academia.dk/Blog/wp-content/uploads/CanaryInACoalMine_2.jpg)

In freshwater systems, diatoms are often the preferred organisms for monitoring since they have high diversity and diatom communities are structured strongly by local environmental conditions. Because of their long use in biomonitoring, freshwater biologists have sensitivity and indicator values for thousands of diatom species. Thus, in principle, you should be able to sample diatom communities in lakes and rivers of interest, and then assess the water quality based on the presence and abundance of different diatom species. While such proxies should always be validated and interpreted carefully (Stephens et al. 2015), there is a long and successful history of using diatoms for environmental monitoring.
Image of diatoms from a scanning electron microscope. (By Kostas Tsobanoglou - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45315566)
The difficulty in practice is to identify diatom species, which requires expert training and can be time consuming. A number of researchers have pursued proxies and surrogates, for example using life form (e.g., diatom shape) or higher taxonomic groupings, instead of identifying species (Wunsam, Cattaneo & Bourassa 2002). In a recent article in the Journal of Applied Ecology, Francois Keck and colleagues (Keck et al. 2016) take this one step further, by using diatom evolutionary relationships as the biomonitoring tool.

Keck et al. employ novel statistical methods to create clusters of species based on their evolutionary relatedness from a phylogenetic tree and species’ sensitivity to pollution and show that these clusters, when delineated by short to moderate phylogenetic distances, do a good job of replicating species-level community pollution sensitivity indices.

This may seem like a onerous task, to assign diatoms to a correct position on a phylogenetic tree, but with the availability and now widespread use of DNA barcoding techniques, it is becoming easier to get genetic data for microscopic assemblages than to identify cells to species. This means that samples can be fit to the phylogenetic clusters without needing to shift through samples. Further, if species are observed, which have not been properly assessed for their sensitivity, they can be assigned an expected sensitivity value based on their relatedness to assessed species.
The phylogenetic tree and species’ sensitivities (Fig. 2 in Keck et al.).
While diatom evolutionary history may not have been strongly influenced by environmental pollutants in the past –because they are relatively recent stressors; it is clear from Keck et al.’s results that closely related species are similarly sensitive to pollution. Other fields of applied management have also begun to incorporate evolutionary history in the design and assessment of applied actions –for example, restoration (Hipp et al. 2015). Evolutionary history can provide important insights and management tools for dealing with the consequences of environmental change.


References

Hipp, A.L., Larkin, D.J., Barak, R.S., Bowles, M.L., Cadotte, M.W., Jacobi, S.K., Lonsdorf, E., Scharenbroch, B.C., Williams, E. & Weiher, E. (2015) Phylogeny in the Service of Ecological Restoration. American Journal of Botany, 102, 647-648.
Keck, F., Bouchez, A., Franc, A. & Rimet, F. (2016) Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with river diatoms (microalgae). Journal of Applied Ecology.
Stephens, P.A., Pettorelli, N., Barlow, J., Whittingham, M.J. & Cadotte, M.W. (2015) Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology, 52, 1-6.
Wunsam, S., Cattaneo, A. & Bourassa, N. (2002) Comparing diatom species, genera and size in biomonitoring: a case study from streams in the Laurentians (Quebec, Canada). Freshwater Biology, 47, 325-340.


Monday, November 23, 2015

Conservation’s toughest decision

Guest post by Shelby Hofstetter, currently enrolled in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough

“We should have thrown in the towel years ago!”- the dinner-table conversation takes a drastic turn from gushing over new panda bear cubs at the Toronto Zoo to a more pessimistic view of the state of global panda conservation efforts. The speaker of these words is recalling a program that aired on the CBC when the pandas were first arriving at the Toronto Zoo. In it, host Amanda Lang acknowledged herself as a “panda hater” and expressed her disapproval of the money wasted on continued panda conservation efforts that are based solely on their appearance (link to video below). As someone who queued in line for the chance to take far too many pictures of the adorable bears, I blanch at some of Lang’s comments that pandas are “big and stupid“ and “want to be extinct”. But as a student of conservation, I recognise the underlying truth that we as a society have a bias for spending our conservation dollars on big, fluffy animals, regardless of their likelihood of survival.

(Photo taken by Shelby Hofstetter at the Toronto Zoo)

But what are the alternatives? With the realisation that funds for biodiversity conservation are finite, there has been a long history of debate over the best methods for choosing worthy species. The umbrella species concept seems to be the logical response to this conundrum – the classic 2 for 1 sale where conservation efforts for one species have the added bonus of protecting various other species that share the same ecosystem. This is the reason why some claim that the “big, fluffy” species are often highlighted in conservation projects, because the large, continuous tracts of land that are a necessity for their protection become a safe haven for many more.

The reality of the umbrella species concept may not be as simple however- there is some debate over how well it actually works. In some cases the large habitats required for the umbrella species do not overlap with biodiversity hotspots for other types of organisms like invertebrates, plants, amphibians or reptiles[1]. And unfortunately, even in cases where these pieces of habitat would provide protection for additional species, safeguarding the large amount of land necessary is often unrealistic[2].

Figure 1. Based on phylogenetic diversity, species A would be a higher conservation priority than species B or C as it has fewer close relatives that would be similar genetically[4]


Another response to this conservation riddle is aptly named the “Noah’s Ark Problem”, and is a framework for choosing species for conservation based on cost and likelihood of survival, but also on phylogenetic diversity[3]. This objective focus on phylogenetic diversity, or the amount of genetic history that a species contains, has gained momentum in recent years and is aimed at saving species that encapsulate high amounts of Earth’s evolutionary life history. The hope is that phylogenetic diversity is correlated with genetic diversity in general, which could also give these species a better chance of adapting to a changing planet[4].

Another notion that is becoming more prevalent is the consideration of ecosystem services, or the benefits that humans derive from a species or ecosystem, when planning for conservation projects. This concept is not necessarily centered around a specific species, but is more focused on the ecosystem as a whole. The emphasis on ecosystem services may help increase the perceived relevance of conservation projects, as the benefit to society is being highlighted. The uptake of this idea within global conservation efforts has been slow however, with less than 10% of conservation assessments including ecosystem services as part of their rationale for conservation[5]. There also seems to be a push for determining the corresponding economic and monetary value of the services that ecosystems provide to society. This is a science that, in a world focused on dollars and cents, may become very important to determining which species or areas are worthy of conservation efforts.

The jury is still out on how to best make conservation’s toughest decision-   determining which struggling species on this planet should be the lucky winners of our conservation resources. In the meantime the importance of this issue is becoming very clear, as many suggest that Earth is currently experiencing a sixth mass extinction. Smart and timely decision-making is vital for which species limited conservation efforts should be focused on. I wouldn’t go so far as to call myself a “panda hater”, or suggest that we “throw in the towel” on conservation efforts for big fluffy species that may not be likely to recover, but I do agree that these decisions should go beyond visual appearances.
Additional Links:
link to Amanda Lang video: https://www.youtube.com/watch?v=0bm-kEnK3yk


References:
1. Marris, E. (2013, December 24). Charismatic mammals can help guide conservation. Nature | News.
2. Fleishman, E., Blair, R., & Murphy, D. (2001). Empirical Validation Of A Method For Umbrella Species Selection. Ecological Applications, 11(5), 1489-1501.
3. Weitzman, M. (1998). The Noah's Ark Problem. Econometrica, 66(6), 1279-1298.
4. Owen, N. (2014). Life on the edge. Significance, 26-29.
5. Egoh, B., Rouget, M., Reyers, B., Knight, A., Cowling, R., Jaarsveld, A., & Welz, A. (2007). Integrating ecosystem services into conservation assessments: A review. Ecological Economics, 63(4), 714-721.

Thursday, March 26, 2015

Ecology in evolutionary times


Ecological and evolutionary perspectives on community assembly. 2015. Gary G. Mittelbach, Douglas W. Schemske. Trends in Ecology and Evolution.

Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better). 2015. Pille Gerhold, James F. Cahill Jr, Marten Winter, Igor V. Bartish and Andreas Prinzing. Functional Ecology

Community assembly has always provided some of the most challenging puzzles for ecologists. Communities are complex, vaguely delimited, involve multi-species interactions, and assemble with seemingly immense variation. Thousands of papers have been dedicated to understanding community assembly, and many have proposed different approaches understanding communities. These range from the ever popular abiotic/biotic filtering concept, functional traits, coexistence theory, island biogeography, metacommunity theory, neutral theory, and phylogenetic patterns. It is probably fair to say that no one existing approach is adequate to completely describe or predict community assembly.

One response to this problem is the growing demand to expand the lens of “community” to cover greater spatial and temporal scales. This owes a lot, directly and indirectly, to Robert Ricklefs’ influential Sewall Wright Award lecture on the Disintegration of the Ecological Community. There is also a strong trend towards re-integrating evolutionary history into studies of community ecology. Coincidentally, or perhaps not, this is occurring as so-called ‘eco-phylogenetic’ approaches have been increasingly criticised. If nothing else, eco-phylogenetics provided a path for, and popularized, the idea of reintegrating evolution into community ecology.

I’ll highlight two particular papers that address this re-integration in surprisingly convergent ways. Both have macroevolution slants (that is, they focus on the impacts and drivers of speciation and extinction, sympatry, allopatry, etc), and an interest in the feedbacks between community interactions and these processes. The first, from Pille Gerhold, James F. Cahill Jr, Marten Winter, Igor V. Bartish and Andreas Prinzing, positions itself as the phoenix from the ashes of eco-phylogenetics (as seen in their particularly enthusiastic title :) ). Evolutionary history, captured by phylogenies, was originally of interest to ecologists not for what it was, but because it could (sometimes, maybe) act as a proxy for species traits and niches. This paper does an excellent job of laying out the various hypotheses that went behind this type of approach and showing why they are not reliably true. If for no other reason, it is worth reading the paper for its clear critique of the foundation of eco-phylogenetics. Using patterns in phylogenies as proxies for the outcomes of particular ecological processes being clearly suspect, the authors argue that explicitly thinking of phylogenetic patterns as the result of both ecological and evolutionary processes is far more informative. [I’ll return to this in a bit with their examples below].

The second paper is written by two big names in their respective fields: Gary Mittlebach (ecology) and Doug Schemske (evolution). The title is a bit vague (“Ecological and evolutionary perspectives on community assembly”), but it turns out that they too have converged on the importance of considering evolutionary history in order to understand community assembly. In particular they focus on the problematic nature of the species pool: species pools are nearly always treated as a static object changing little through time or space and are notoriously difficult to define. However, the species pool underlies null model approaches used to test communities for differences from a random expectation. So defining it correctly is important.

From the early days, Elton and others defined the species pool as the group of species that can disperse to and colonize a community. However, the species pool may be dynamic, and they note “To date, relatively little attention has been focused on the feedback that occurs between local community species composition, biotic interactions, and the diversification processes that generate regional species pools.”

This paper does an excellent job of explaining how macroevolutionary processes can alter a regional species pool. The most obvious example is the process of adaptive radiation in island-like systems, where competition for resources drives ecological divergence and speciation. Darwin’s finches, Anolis lizards, and cichlid fishes provide well-known examples of this rapid expansion of the species pool through inter-specific interactions. On mainland systems, speciation may be more likely to occur in allopatry, and the rate limiting step for range expansion (leading to secondary sympatry and only then increasing a species pool) is often interspecific interactions. One study found that secondary sympatry took 7my on average, though speciation alone took only 3my. So the species pool is the outcome of constant feedbacks between species interactions and evolutionary processes.
From Mittlebach & Schemske. Figure illustrating the feedbacks between evolution and ecological interactions, in producing the species pool.
Both papers provide useful examples of how such incorporating evolution into community ecology may prove useful. As a simple example, Mittlebach and Schemske point out that evolution can greatly alter the utility of Island Biogeography Theory: given enough time, speciation events including adaptive radiations, greatly increase the (non-mainland) species pool and would strongly alter predictions of diversity, especially for distant islands.

The Gerhold et al. paper provides the below illustrations as additional possibilities for how evolution and community interactions may feedback.
From Gerhold et al. Two examples of how evolution and communities might interact.

It is certainly interesting to see this shift towards how we envision and study communities. The historical focus on local space and time no doubt reflects ecologists' attempt to limit the problem to a manageable frame. But there is some logic behind expanding our definition of communities to larger spatial scales and greater time periods, especially since there are usually no true boundaries defining communities in space and time. Answering which specific time scales and spatial scales most useful to understanding communities is difficult: if we increase the time or space we consider, how and when does the additional information provided decline? The next step is to consider evolution in this fashion for real organisms, and evaluate the true utility of this approach.  

Wednesday, October 15, 2014

Putting invasions into context

How can we better predict invasions?

Ernesto Azzurro, Victor M. Tuset,Antoni Lombarte, Francesc Maynou, Daniel Simberloff,  Ana Rodríguez-Pérez and Ricard V. Solé. External morphology explains the success of biological invasions. Ecology Letters (2014) 17: 1455–1463.

Fridley, J. D. and Sax, D. F. (2014), The imbalance of nature: revisiting a Darwinian framework for invasion biology. Global Ecology and Biogeography, 23: 1157–1166. doi: 10.1111/geb.12221

Active research programs into invasion biology have been ongoing since the 1990s, but their results make clear that while it is sometimes possible to explain invasions post hoc, it is very difficult to predict them. Darwin’s naturalization hypothesis gets so much press in part because it is the first to state the common acknowledgement that the struggle for existence should be strongest amongst closely related species, implying that ‘invasive species must somehow be different than native species to be so successful’. Defining more generally what this means for invasive species in terms of niche space, trait space, or evolutionary history has had at best mixed results. 

A couple of recent papers come to the similar-but rather different-conclusion that predicting invasion success is really about recognizing context. For example, Azurro et al. point out that despite the usual assumption that species’ traits reflect their niches, trait approaches to invasion that focus on the identifying traits associated with invasiveness have not be successful. Certainly invasive species may be more likely to show certain traits, but these are often very weak from a predictive standpoint, since many non-invasive species also have these traits. Morphological approaches may still be useful, but the authors argue that the key is to consider the morphological (trait) space of the invaders in the context of the morphological space used by the resident communities.
Figure 1. From Azurro et al. 2014. A resident community uses morphospace as delimited by the polygon in (b). Invasive species may fill morphospace within the same area occupied by the community (c) or (d)) or may use novel morphospace (e). Invasiveness should be greatest in situation (e). 
The authors use as an illustration, the largest known invasion by fish - the invasion of the Mediterranean Sea after the construction of the Panama Canal, an event known as the ‘Lessepsian migration’. They hypothesize that when a new species entering a community that fills some defined morphospace will face one of 3 scenarios (Figure 1): 1) they will be within the existing morphospace and occupy less morphospace than their closest neighbour; 2) they will be within the existing morphospace but occupy more morphospace than their closest neighbour; or 3) they will occupy novel morphospace compared to the existing community. The prediction being that invasion success should be highest for this third group, for whom competition should be weakest. Their early results are encouraging, if not perfect – 73% of species located outside of the resident morphospace became abundant or dominant in the invaded range. (Figure 2)
Figure 2. From Azurro et al. 2014. Invasion success of fish to the Mediterranean Sea in relation to morphospace, over multiple historical periods. Invasive (red) species tended to exist in novel morphospace compared to the resident community. 
A slightly different approach to invasion context comes from Jason Fridley and Dov Sax, who revision invasion in terms of evolution - the Evolutionary Imbalance Hypothesis (EIH). In the EIH, the context for invasion success is the characteristics of the invaders' home range. If, as Darwin postulated, invasion success is simply the natural expectation of natural selection, then considering the context for natural selection may be informative. 

In particular, the postulates of the EIH are that 1) Evolution is contingent and imperfect, thus species are subject to the constraints of their histories; 2) The degree to which species are ecologically optimized increases as the number of ‘evolutionary experiments’ increases, and with the intensity of competition (“Richer biotas of more potential competitors and those that have experienced a similar set of environmental conditions for a longer period should be more likely to have produced better environmental solutions (adaptations) to any given environmental challenge”); and 3) Similar sets of ecological conditions exist around the world. When these groups are mixed, some species will have higher fitness and possibly be invasive. 

Figure 3. From Fridley and Sax, 2014.
How to apply this rather conversational set of tenets to actual invasion research? A few factors can be considered when quantifying the likelihood of invasion success: “the amount of genetic variation within populations; the amount of time a population or genetic lineage has experienced a given set of environmental conditions; and the intensity of the competitive environment experienced by the population.” In particular, the authors suggest using phylogenetic diversity (PD) as a measure of the evolutionary imbalance between regions. They show for several regions that the maximum PD in a home region is a significant predictor of the likelihood of species from that region becoming invasive. The obvious issue with max PD being used as a predictor is that it is a somewhat imprecise proxy for “evolutionary imbalance” and one that correlates with many other things (including often species richness). Still, the application of evolutionary biology to a problem often considered to be primarily ecological may make for important advances. 
Figure 4. From Fridley and Sax 2014. Likelihood of becoming invasive vs. max PD in the species' native region.

Monday, July 7, 2014

Phylogeny, competition and Darwin: a better answer?

*Sorry for the low frequency of posts these days – I seem to be insanely busy this summer 

Oscar Godoy, Nathan Kraft, Jonathan Levine. 2014. Phylogenetic relatedness and the determinants of competitive outcomes. Ecology Letters.

Ecology is hard in part because of the things we can’t (at least easily) measure: fitness, interaction strengths, and the niche, all fundamental ecological concepts. Since we are unable to measure these concepts directly, ecologists have come up with proxies and correlates. Take Darwin’s hypothesis that competition should be greater between closely related species. It relies a chain of assumptions about proxy relationships – first that relatedness should correlate with greater similarity of traits, secondly that similar traits should correlate with greater niche overlap. The true concept of interest, the niche, is un-measurable (if it is an n-dimensional hypervolume) so instead shared evolutionary history provides possible insight into species coexistence.

Ecophylogenetic studies have adopted Darwin's hypothesis as an example of how  molecular phylogenies may provide information about evolutionary history which in turn informs current ecological interactions. Phylogenies ideally capture feature diversity, and so (all things being equal) should provide information about similarity between species based on their relationship.  Despite this, studies have been mixed in terms of finding the relationship predicted by Darwin between phylogenetic relatedness and competition. It is not clear whether this mixed result suggests problems with the phylogenetic approaches being used, or non-generality of Darwin’s hypothesis.

Oscar Godoy, Nathan Kraft, and Jonathan Levine attempt to explore this question once again, but through the lens of Chesson’s coexistence framework (2000). Chesson’s framework describes competitive differences between species not as a single quantity, but instead the outcome of both stabilizing niche differences and equalizing fitness differences between species. This framework predicts that competitive differences should be greatest when species have similar niches (low stabilizing niche differences) and/or when they have large differences in fitness. This divisions alters the predictions from Darwin's hypothesis: if closely related species have similar niches, they should compete more strongly, but on the other hand, if closely related species have similar fitnesses, they should compete less strongly. Darwin’s hypothesis as it has been tested may be too simplistic.

The authors used an experiment involving 18 California grassland species to look at first, whether competitive ability is conserved, and more generally to explore whether phylogenetic distance predicts “the niche differences that stabilize coexistence and the fitness differences that drive competitive exclusion?” Further, can this information be used to predict the relationship between phylogeny and competitive outcomes? To determine this, they quantified germination, fecundity, seed survival, and interaction coefficients for the 18 species based on competition with different competitors (both by identity and density), and quantified the strength of stabilizing and equalizing forces (as in previous works). With this information, they calculated for each species the average fitness and ranked species in a competitive hierarchy using a fully parameterized annual plant population model. Species’ competitive rank did in fact show a phylogenetic signal (Figure 1), and the strongest competitors were clustered in the Asteraceae and its sister node.
Fig 1. Relationship between competitive rank among the 18 CA grassland species.
Competitive rank was then decomposed into fitness differences and niche differences. Fitness differences showed the clearest relationship with phylogeny - distantly related competitors had significantly greater asymmetries in fitness, closely related species had similar fitnesses (Figure 2). However stabilizing niche differences showed no phylogenetic signal at all (Figure 3, solid line).
Fig. 2. Relationships between fitness differences and phylogenetic distance.
Fig 3. Solid line - observed niche distances as a function of phylogenetic distance. Dashed line, size of distances actually needed to assure coexistence.
The authors could then calculate, for a given pair of species with a given phylogenetic distance, the expected fitness difference (based on the fitness difference-phylogeny relationship), and given this, the amount of stabilizing niche differences that would be necessary to prevent competitive exclusion between pairs of species. When they did this, they found that the required stabilizing niche differences were much larger than those that actually existed between the plants. This was especially true between distant related species(dashed line, Figure 3). Darwin’s hypothesis, that closely related species should be more likely to coexist, seemed to be reversed for these species.

How should we interpret these results more broadly? Is this reinforcement of the use of phylogenetic information to answer ecological questions, provided the questions are asked correctly? One of the most interesting contributions of this paper is their discussion of the oft-seen, but poorly incorporated, increase in variation in a trait (here fitness differences) as phylogenetic distances increase. This uneven variance often leads to phylogenetic-trait correlations being labelled non-significant, since it violates the assumptions of linear models. In contrast, here the authors suggest that this uneven variance is important. “For example, even if on average, both niche and fitness differences increase with phylogenetic distance, the increasing variance in these relationships means that only distant relatives are likely combine large competitive asymmetries with small niche differences (rapid competitive exclusion), or large niche differences with small competitive asymmetries (highly stable coexistence). Overall, our results suggest that increasing variance in niche or fitness differences with phylogenetic distance may play a central role in determining the phylogenetic relatedness of coexisting species.”

This discussion is important for questions about phylogenetic relatedness and coexistence – variability is part of the answer, not evidence against the existence of such relationships. However, a few caveats seem important: Because fitness differences and niche differences as defined in the Chesson framework may not be easily associated with traits (since a single trait might contribute to both components), it seems that it will be a little difficult to expand these analyses to less rigourous experimental settings. This might also be important to hypothesize how fitness or niche differences per se become associated with phylogenetic differences, since traits/genes are actually under selection. But the paper definitely provides an interesting direction forward.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology and Systematics 31:343-366.

Monday, April 21, 2014

Null models matter, but what should they look like?

Neutral Biogeography and the Evolution of Climatic Niches. Florian C. Boucher, Wilfried Thuiller, T. Jonathan Davies, and Sébastien Lavergne. The American Naturalist, Vol. 183, No. 5 (May 2014), pp. 573-584

Null models have become a fundamental part of community ecology. For the most part, this is an improvement over our null-model free days: patterns are now interpreted with reference to patterns that might arise through chance and in the absence of ecological processes of interest. Null models today are ubiquitous in tests of phylogenetic signals, patterns of species co-occurrence, models of species distribution-climate relationships. But even though null models are a success in that they are widespread and commonly used, there are problems--in particular, there is a disconnect between how null models are chosen and interpreted and what information they actually provide. Unfortunately, simple and easily applied null models tend to be favoured, but they are often interpreted as though they are complicated, mechanism-explicit models.

The new paper “Neutral Biogeography and the Evolution of Climatic Niches” from Boucher et al. provides a good example of this problem. The premise of the paper is straightforward: studies of phylogenetic niche conservation tend to rely on simple null models, and as a result may misinterpret what their data shows because of the type of null models that they use. The study of phylogenetic niche conservation and niche evolution is becoming increasingly popular, particularly studies on how species' climatic niches evolve and how climate niches relate to patterns of diversity. In a time of changing climates, there are also important applications looking at how species respond to climatic shifts. Studies of changes in climate niches through evolutionary time usually rely on a definition of the climate niche based on empirical data, more specifically, the mean position of a given species along a continuous abiotic gradient. Because this is not directly tied to physiological measurements, climate niche data may also capture the effect of dispersal limitations or biotic interactions. Hence the need for null models, however the null models used in these studies primarily flag changes in climate niche that result from to random drift or selection in a varying environment. These types of null models use Brownian motion (a "random walk") to answer questions about whether niches are more or less similar than expected due to chance, or else whether a particular model of niche evolution is a better fit to the data than a model of Brownian motion.

The authors suggest that the reliance on Brownian motion is problematic, since these simple null models cannot actually distinguish between patterns of climate niches that arise simply due to speciation and migration but no selection on climate niches, and those that are the result of true niche evolution. If this is true, conclusions about niche evolution may be suspect, since they depend on the null model used. The authors used a neutral, spatially explicit model (known as an "alternative neutral biogeographic model") that simulates dynamics driven only by speciation and migration, with species being neutral in their dynamics. This provides an alternative model of patterns that may arise in climate niches among species, despite the absence of direct selection on the trait. The paper then looks at whether climatic niches exhibit phylogenetic signals when they arise via neutral spatial dynamics; if gradualism a reasonable neutral expectation for the evolution of climatic niches on geological timescales; and whether constraints on climatic niche diversification can arise simply through bounded geographic space. Simulations of the neutral biogeographic model used a gridded “continent” with variable climate conditions: each cell has a carrying capacity, and species move via migration and split into two species either by point mutation, or else by vicariance (a geographic barrier appears, leading to divergence of 2 populations). Not surprisingly, their results show that even in the absence of any selection on species’ climate niches, patterns can result that differ greatly from a simple Brownian motion-based null model. So the simple null model (Brownian motion) often concluded that results from the more complex null model were different from the random/null expectation. This isn't a problem per se. The problem is that currently interpretations of the Brownian motion model may be that anything different from null is a signal for niche evolution (or conservation). Obviously that is not  correct.

This paper is focused on the issue of choosing null models for studies of climate niche evolution, but it fits into a current of thought about the problems with how ecologists are using null models. It is one thing to know that you need and want to use a null model, but it is much more difficult to construct an appropriate null model, and interpret the output correctly. Null models (such as the Brownian motion null model) are often so simplistic that they are straw man arguments – if ecology isn't the result of only randomness, your null model is pretty likely to be a poor fit to the data. On the other hand, the more specific and complex the null model is, the easier it is to throw the baby out with the bathwater. Given how much data is interpreted in the light of null models, it seems that choosing and interpreting null models needs to be more of a priority.