Discussion
Reconcilling niche-based process and neutral dynamics in a portion of an
infinite system, the regional species pool was already not free
parameter. In general, the divergent ecological-evolutionary mechanisms
operated consistently, and emerged the per capita ecological difference
and equivalence among species at different spatio-temporal-environmental
scales as expectations.
First, the number of guilds in system achieved first to a stationary
state; then, the species richness converged eventually to a dynamical
equilibrium through speciation-extinction balance. The convergence to a
dynamical equilibrium from any initial diversities was perspected in
neutral spatially-explicit conjecture (Hubbell, 2001). I argued from the
duality of neutral model (Cencini et al. 2012) that the simulations
started from monodominance guilds might achieve to a dynamical
equilibrium at convergence time because all walkers of each guild were
coalesced or annihilated.
In simulations, the per capita ecological difference among species only
contributed to the probabilities of immigration success, so the weighted
lottery process in system was more efficient and immediate, and the
functional uniqueness so the relative role of niche-based process in
model communities increased at higher dispersal rates. However, if the
ecological difference contributes to only competitions, or both
competitions and dispersals, these patterns relative to dispersal rates
may be altered.
Second, in the model communities resulted with more than two guilds, the
increase of functional redundancy suggested that the relative role of
neutral dynamics increased in an area encompassing independent
biogeographic units. The pattern of system convergence suggested that
the relative role of neutral dynamics might increase at broader temporal
scale through speciation as well.
The third hypothesis was not completely true. In the model communities
resulted with more than two guilds, the functional uniqueness and
redundancy were refered well by the relative portions explained by
environmental and pure spatial components in the total explained
variation of overall model. Furthermore, the total explained variation
and these relative portions were similar between overall and
hierarchical models; however, the number of guilds coexisted in model
communities was often larger than the number of habitat types identified
by hierarchical guild structure. It was intuitively true that not only
the neutral dynamics among the species of single guild, but also the
competition-colonization trade-off among the species of more than two
guilds with similar environmental optimum and different levels of
specialization operated in the spatial structures found within and among
patchy habitats.
The trajectory and contrast between alternative processes establish the
false premises that the niche-based process is only important in each
local community; while, the neutral dynamics are only important in a set
of local communities (Leibold & Mcpeek, 2006). The critic of variation
partitioning based on canonical analysis was primary based on the false
premises and the complexity to refer the overall parameters of selection
strength and dispersal rate of simulations (Gilbert & Bennett, 2010;
Smith & Lundholm, 2010); however, the neutral assumption can be broken
by the difference of dispersal abilities, and the efficiency of weighted
lottery process is dependent of dispersal rates as noted above. To
compare the performance of existing approaches to disentangle the
influence of niche-based process and neutral dynamics (e.g. generalised
additive model and tree-based machine learning used in macroecology;
Viana, Keil, & Jeliazkov 2019), ecologist must shift focus from the
contribution of local competitions and regional dispersals to detecting
the spatio-temporal-environmental scales on which per capita ecological
difference and equivalence among species are emerged. In this focus, the
autocorrelation method (Diniz-Filho et al., 2012) and the analysis of
functional ecology (e.g. Pillar & Duarte, 2010) can be combined
straightforward to infer divergent ecological-evolutionary mechanisms.
In the present study, the first and second-order orthogonal
environmental variables were used to model unimodal relationship. In the
model communities resulted with only one guild, the functional
uniqueness was zero; however, the relative portion explained by
environmental component in total explained variation was positively
biased in linear environmental gradient, and the type I error of pure
environmental component was inflated in these scenarios. The species
composition might be contrasted between opposite sides of model
communities because of the approximate scales of three for m =
0.01, nine for m = 0.09 and 28 for m = 0.81, and the
phylogenetically closely related species might be clumped there. In the
model communities resulted with more than two guilds, the functional
uniqueness and redundancy were refered well by the relative portions
explained by environmental and pure spatial components. The
insufficiency of raw environmental variable to model unimodal
relationship (Gilbert & Bennett, 2010), and its dependence on
environmental structures (Smith & Lundholm, 2010) seemed to be solved
mostly by the use of orthogonal environmental variables; furthermore,
the neutral dynamics and competition-colonization trade-off were refered
well in the spatial structures found within and among patchy habitats.
Although the orthogonal environmental variables might increase the
spurious correlation between species distribution and environmental
structure across scenarios, this problem would be eventually corrected
by the method proposed recently using a constrained randomization of
environmental variables (Wagner & Dray, 2015; Clappe, Dray, &
Peres-Neto 2018).
The performance of autocorrelation method (Diniz-Filho et al., 2012) was
unexpected but interesting, and the spatial autocorrelation pattern of
alternative processes reversed across ecological-evolutionary scales.
The neutral dynamics always emerged the pattern of spatial
autocorrelations in an area encompassing independent biogeographic
units; while, the relative role of niche-based process increased and
tended to emerge the pattern of spatial autocorrelations in an area
packed within a biogeographic unit. It was intuitively true that the
phylogenetically closely related species were clumped, and the neutral
dynamics might vanish the pattern of spatial autocorrelations if the
model communities were packed much more compactly within a biogeographic
unit. Furthermore, the neutral dynamics through speciation as well as
competition-colonization trade-off might emerge the pattern of spatial
autocorrelations in patchy habitats.
In the fish communities of Bolivian Amazon lowlands, we observed the
hierarchical guild structure across a range of spatio-temporal scales
(Yunoki & Torres, 2016; Yunoki et al., 2017; Yunoki et al., 2018;
Yunoki et al., 2019). We presumed that only the neutral dynamics emerged
the spatial structures among patchy habitats if all environmental
factores were included in analysis, that the neutral dynamics did not
emerge the pattern of spatial autocorrelations, and that the neutral
dynamics should not be associated with functional species traits. The
last argument is because of neutral assumption. The temporal structures
found in the successional patchy habitats on a fine spatio-temporal
scale usually presented autocorrelation patterns, and were accompanied
by the seasonal and inter-annual change of functional species traits. We
interpreted these patterns as environmental filtering. The spatial
structures found among patchy habitats on a very broad spatial scale
presented the autocorrelation pattern in turbid rivers but in varzea
lakes, and the spatial structures found in turbid rivers and transparent
black-clear water bodies but in varzea lakes were accompanied by the
divergence of functional species traits (i.e. traits divergence pattern
associated to hierarchical guild structure; see Pillar & Duarte, 2010).
Furthermore, the group of sedentary species was dominant in turbid
rivers and transparent black-clear water bodies, and presented
autocorrelation patterns there; while, the group of migratory species
was dominant in varzea lakes, and did not present autocorrelation
pattern there. We interpreted these patterns like the natural selection
of sedentary species in the stable environment of turbid rivers and
transparent black-clear water bodies as the species might have been
adapted to a minor environmental factor missing from analysis, and the
neutral dynamics of migratory species in the seasonal environment of
varzea lakes (Yunoki et al., 2019); however, the natural selection of
sedentary species in turbid rivers and transparent black-clear water was
doubtful. Just as the patterns of nucleotide and amino acid substitution
in population genetics (Holsinger, 2015), the natural selection may
operate in the initial stage of diversification when the population
sizes of each species may be huge; then, conserved for their
descendants, and this expectation seems to be approximated by the mode
of speciation and a range of initial conditions of regional species pool
implemented here. The massive extinction of archaic faunas triggered by
climate change during the Paleogene, and the roles of niche conservatism
and environmental heterogeneity originated by principal geomorphological
features (e.g. water types) for the origin of modern biodiversity during
the Neogene were also perspected in the recent synthesis of biogeography
of Neotropical freshwater fishes (Albert & Reis, 2011). The simulations
reported here were simple and unrealistic in many aspects; however,
established the similar patterns with our field observations and seemed
to allow their interpretations. The spatial structures and
autocorrelation pattern found among patchy habitats of turbid rivers and
transparent black-clear water bodies on a very broad spatial scale
between north and south of Bolivian Amazon lowlands associated with the
functional species traits of fish communities; furthermore, the
autocorrelation patterns found there for the group of sedentary species
but migratory species could be explained by the competition-colonication
trade-off, and the speciations of sedentary species with
niche-conservatism.
The niche-based process is deterministic and results first in a
stationary state of guilds; while, the neutral dynamics are stochastic
and result eventually in a dynamical equilibrium of species through
speciation-extinction balance. The relative role of neutral dynamics
increases at broader spatio-temporal scales because the species richness
but the number of guilds increases through speciation. The neutral
dynamics among the species of single guild and the
competition-colonization trade-off among the species of more than two
guilds with similar environmental optimum and different levels of
specialization operate in patchy habitats. The neutral assumption can be
broken by the difference of dispersal abilities, and the efficiency of
weighted lottery process is dependent of dispersal rates. The
implication of present study is that the methodological advance and
field studies to disentangle the influence of alternative processes must
shift focus from the contribution of local competitions and regional
dispersals to detecting the spatio-temporal-environmental scales on
which the per capita ecological difference and equivalence among species
are emerged through divergent ecological-evolutionary mechanisms.
Data accessibility
R sources are available in
https://github.com/takayukiyunoki/spatialIBM.git. Appendix A presents
the summary statics of scenarios. Bolivian Amazon lowland fish
metacommunity data. Freshwater Metadata Journal 7: 1-6.
http://dx.doi.org/10.15504/fmj.2015.7
References
Albert, J. S., & Reis, R. E. (2011). Historial biogeography of
Neotropical freshwater fishs. University of California Press
Bell, G. (2003). The interpretation of biological surveys. Pros R Soc,
270, 2531–2542. doi: 10.1098/rspb.2003.2550
Bivand, R., Altman, M., Anselin, L., Assunção, R., Berke, O., Bernat,
A., … Yu, D. (2019). spdep: Spatial Dependence: Weighting
Schemes, Statistics and Models (R package version 1.1-3)
Borcard, D., Legendre, P., & Gillet, F. (2011). Numerical ecology with
R. Springer Science & Business Media, NY
Cencini, M., Pigolotti, S., & Muñoz, M. A. (2012). What Ecological
Factors Shape Species-Area Curves in Neutral Models? PLoS ONE, 7(6),
e38232. doi: 10.1371/journal.pone.0038232
Chave, J., & Leigh, E. G. (2002). A spatially explicit neutral model of
beta-diversity in tropical forests. Theor Popul Biol, 62, 153–168. doi:
10.1006/tpbi.2002.1597
Chave, J., Muller-Landau, H. C., & Levin, S. A. (2002). Comparing
classical community models: theoretical consequences for patterns of
diversity. Am Nat, 159, 1–23. doi: 10.1086/324112
Clappe, S., Dray S., & Peres-Neto, P. R., (2018). Beyond neutrality:
disentangling the effects of species sorting and spurious correlations
in community analysis. Ecology, 99, 1737–1747. doi:
https://doi.org/10.1002/ecy.2376
Coppock, A., Cooper, J., & Fultz, N. (2019). randomizr: Easy-to-Use
Tools for Common Forms of Random Assignment and Sampling (R package
version 0.20.0)
Diniz-Filho, J. A. F., Siqueira, T., Padial, A. A., Rangel, T. F.,
Landeiro, V. L., & Bini, L. M. (2012). Spatial autocorrelation analysis
allows disentangling the balance between neutral and niche processes in
metacommunities. Oikos, 121, 201–210. doi:
10.1111/j.1600-0706.2011.19563.x
Dray, S., Legendre, P., & Blanchet, G. (2016). packfor: Forward
Selection with permutation (Canoco p.46) (R package version 0.0-8)
Durrett, R., & Levin, S. (1996). Spatial Models for Species-Area
Curves. J. theor. Biol., 179, 119–127. doi:
10.1006/jtbi.1996.0053
Economo, E. P., & Keitt, T. H. (2008). Species diversity in neutral
metacommunities: a network approach. Ecol Lett, 11, 52–62. doi:
10.1111/j.1461-0248.2007.01126.x
Etienne, R. S. (2005). A new sampling formula for neutral biodiversity.
Ecol Lett, 8, 253–260. doi: 10.1111/j.1461-0248.2004.00717.x
Etienne, R. S. (2007). A neutral sampling formula for multiple samples
and an ‘exact’ test of neutrality. Ecol Lett, 10, 608–618. doi:
10.1111/j.1461-0248.2007.01052.x
Etienne, R. S., & Olff, H. (2004). A novel genealogical approach to
neutral biodiversity theory. Ecol Lett, 7, 170–175. doi:
10.1111/j.1461-0248.2004.00572.x
Gause, G. F. (1934). The Struggle For Existence. Williams and Williams
Gilbert, B., & Bennett, J. R. (2010). Partitioning variation in
ecological communities: do the numbers add up? – J. Appl. Ecol., 47,
1071–1082. doi: 10.1111/j.1365-2664.2010.01861.x
Giraudoux, P., Antonietti, J. P., Beale, C., Pleydell, D., & Treglia,
M. (2018). pgirmess: Spatial Analysis and Data Mining for Field
Ecologists (R package version 1.6.9)
Hankin, R. K. S. (2007). Introducing untb, an R Package For Simulating
Ecological Drift Under the Unified Neutral Theory of Biodiversity.
Journal of Statistical Software, 22, 1–15. doi:
10.18637/jss.v022.i12
Holsinger, K. E. (2015). Lecture Notes in Population Genetics available
at
http://darwin.eeb.uconn.edu/eeb348-notes/Lecture-Notes-in-Population-Genetics.pdf
under the Creative Commons Attribution License. Full terms at
https://creativecommons.org/licenses/by/4.0/
Hubbell, S. P. (2001). The unified neutral theory of biodiversity and
biogeography. Princeton University Press
Hutchinson, G. E. (1957). Concluding remarks. Cold Spring Harbor Symp
Quant Biol, 22, 415–427. doi: 10.1101/SQB.1957.022.01.039
Jabot, F., Etienne, R. S., & Chave, J. (2008). Reconciling neutral
community models and environmental filtering: theory and an empirical
test. Oikos, 117, 1308–1320. doi: 10.1111/j.2008.0030-1299.16724.x
Janzen, T., Haegeman, B., & Etienne, R. S. (2015). A sampling formula
for ecological communities with multiple dispersal syndromes. J. theor.
Biol., 374, 94–106. doi: 10.1016/j.jtbi.2015.03.018
Legendre, P., Borcard, D., Blanchet, F. G., & Dray, S. (2012). PCNM:
MEMspatial eigenfunction and principal coordinate analyses (R package
version 2.1–2)
Leibold, M. A., & Mcpeek, M. A. (2006). Coexistence of the niche and
neutral perspectives in community ecology. Ecology, 87, 1399–1410. doi:
10.1890/0012-9658
Marcon, E., & Hérault, B. (2015). entropart: An R Package to Measure
and Partition Diversity. Journal of Statistical Software, 67, 1–26.
doi:
10.18637/jss.v067.i08
Marcon, E., & Hérault, B. (2019). entropart: Entropy Partitioning to
Measure Diversity (R package version 1.6-4)
Mouquet, N., Munguia, P., Kneitel, J. M., & Miller, T. E. (2003).
Community assembly time and the relationship between local and regional
species richness. OIKOS, 103, 618–626. doi:
10.1034/j.1600-0706.2003.12772.x
Munoz, F., Couteron, P., Ramesh, B. R., & Etienne, R. S. (2007).
Estimationg parameters of neutral communities: From one single large to
several small samples. Ecology, 88(10), 2482–2488. doi:
10.1890/07-0049.1
Munoz, F., Grenié, M., Denelle, P., Taudière, A., Laroche, F., Tucker,
C., & Violle, C. (2018). ecolottery: Simulating and assessing community
assembly with environmental filtering and neutral dynamics in R. Methods
Ecol Evol, 9, 693–703. doi: 10.1111/2041-210X.12918
Munoz, F., & Huneman, P. (2016). From the neutral theory to a
comprehensive and multiscale theory of ecological equivalence. The
Quarterly Review of Biology, 91, 321–342. doi: 10.1086/688098
Munoz, F., Ramesh, B. R., & Couteron, P. (2014). How do habitat
filtering and niche conservatism affect community composition at
different taxonomic resolutions? Ecology, 95, 2179–2191. doi:
10.1890/13-0064.1
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P.,
McGlinn, D., … Wagner, H. (2019). vegan: Community Ecology
Package (R package version 2.5-6)
Paradis, E., Blomberg, S., Bolker, B., Brown, J., Claude, J., Cuong, H.
S., … de Vienne, D. (2019). ape: Analyses of Phylogenetics and
Evolution (R package version 5.3)
Peres-Neto, P. R., & Legendre, P. (2010). Estimating and controlling
for spatial structure in the study of ecological communities. Glob Ecol
Biogeogr, 19, 174–184. doi: 10.1111/j.1466-8238.2009.00506.x
Pillar, V. D., & Duarte, L. S. (2010). A framework for metacommunity
analysis of phylogenetic structure. Ecol Lett, 13, 587–596. doi:
10.1111/j.1461-0248.2010.01456.x
R Core Team (2019). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/
Ricotta, C., de Bello, F., Moretti, M., Caccianiga, M., Cerabolini, B.
E. L., & Pavoine, S. (2016). Measuring the functional redundancy of
biological communities: A quantitative guide. Methods Ecol Evol, 7,
1386–1395. doi: 10.1111/2041-210X.12604
Rosindell, J., Hubbell, S. P., & Etienne, R. S. (2011). The Unified
Neutral Theory of Biodiversity and Biogeography at Age Ten. Trends Ecol.
Evol., 26(7), 340–348. doi: 10.1016/j.tree.2011.03.024
Smith, T. W., & Lundholm, J. T. (2010). Variation partitioning as a
tool to distinguish between niche and neutral processes. Ecography, 33,
648–655. doi: 10.1111/j.1600-0587.2009.06105.x
Viana, D. S., Keil, P., & Jeliazkov, A. (2019) Partitioning environment
and space in species-by-site matrices: a comparison of methods for
community ecology and macroecology. bioRxiv preprint. Accessed November
3, 2020. doi:http://dx.doi.org/10.1101/871251
Wagner, H. H., & Dray, S. (2015) Generating spatially constrained null
models for irregularly spaced data using Moran spectral randomization
methods. Methods Ecol Evol, 6, 1169–1178. doi: 10.1111/2041-210X.12407
Wickham, H. (2017). reshape2: Flexibly Reshape Data: A Reboot of the
Reshape Package (R package version 1.4.3)
Wickham, H., François, R., Henry, L., Müller, K., & RStudio (2019).
dplyr: A Grammar of Data Manipulation (R package version 0.8.4)
Yunoki, T., & Torres, L. V. (2016). Fish metacommunity dynamics in the
patchy heterogeneous habitats of varzea lakes, turbid river channels and
transparent clear and black water bodies in the Amazonian lowlands of
Bolivia. Environ Biol Fish, 99, 391–408. doi: 10.1007/s10641-016-0481-1
Yunoki, T., Torres, L. V., Pouilly, M., & Hablützel, P. I. (2017).
Comunidades ictícolas en diferentes tipos de aguas, Amazonía boliviana.
Dissertation, I Congreso boliviano de ictiología.
https://www.researchgate.net/publication/320716089_yunoki_bio-eco_oral
Yunoki, T., Torres, L. V., & Cholima, R. B. (2018). A metacommunity
ecological approach to understanding the community organization of fish
in artificial ponds of the Mamoré River floodplain in the Amazonian
lowlands of Bolivia. Environ Biol Fish, 101, 1329–1341. doi:
10.1007/s10641-018-0780-9
Yunoki, T., Torres, L. V., & Cholima, R. B. (2019). Organización de las
comunidades de peces en las tierras bajas de Amazonía boliviana.
Dissertation, II Congreso boliviano de ictiología.
https://www.researchgate.net/publication/337919664_YUNOKI_diversidad_oral
Figure legends
Figure 1. The environmental structures of model communities in a grid of
10 x 10 local communities in the center of small system. The
environmental structure was quadrupled for large system.
Figure 2. A work flow script illustrating individual-based model. (a)
parameters (b) first time step.
Figure 3. System convergence; one-dimensional scatter plots showing the
time ratio to stationary guild number in the convergence time of species
richness. The number of scenarios started from multiple guilds was
provided following the text of axis-x in parentheses.
Figure 4. Summary statics of the model communities resulted with only
one guild were compared across ecological-evolutionary scales in three
environmental structures. (a) Functional uniqueness and redundancy were
compared to the relative portions explained by environmental and pure
spatial components in total explained variation. (b) Type I error rate
of environmental and pure environmental components and power rate of
other components. The number of model communities was provided following
the text of axis-x in parentheses.
Figure 5. Summary statics of the model communities resulted with more
than two guilds were compared across ecological-evolutionary scales in
three environmental structures. (a) Functional uniqueness and redundancy
were compared to the relative portions explained by environmental and
pure spatial components in the total explained variation of overall
model; then, the total explained variation and these relative portions
were compared between overall and hierarchical models. The portions
explained in hierarchical model was calculated for the scenarios in that
the hierarchical guild structure was significant. vs.guild-habitat, the
number of guilds coexisted in model communities was compared to the
number of habitat types identified by hierarchical guild structure. (b)
Power rate of all components in overall and hierarchical models. The
number of model communities was provided following the text of axis-x in
parentheses.
Figure 6. Performance of autocorrelation method in the model communities
resulted with only one guild. (a) Mantel correlation of false
environmental and true spatial components. (b) Mantel test against the
null hypothesis of less than zero. The numbers of false environmental
and true spatial components were provided following the text of axis-x
in parentheses.
Figure 7. Performance of autocorrelation method in the model communities
resulted with more than two guilds. (a) Mantel correlation of true
environmental component in overall model and pure spatial components in
each environmental context. (b) Mantel test against the null hypothesis
of less than zero. The number of true environmental components in
overall model and the number of environmental contexts in hierarchical
model were provided following the text of axis-x in parentheses.
Competing Interests
I have no conflicts of interest to disclose.
Author Contributions
T.Y. developed the theoretical formalism, performed the numerical
simulations and analytic calculations, and wrote the original draft and
review.
Acknowledgements
I would like to thank editor in chief Prof. Jennifer Firn for the
opportunity to revise my manuscript, also associate editor and two
anonymous reviewers for the careful review and constructive suggestions.