Discussion
A conspicuous absence of stationarity between fish species richness and
the tested hypotheses was determined, but climate was observed to
contribute the most to the richness distribution of stream fishes. The
water-energy dynamics were the most probable metabolic restriction
mechanism acting on the community structuring of stream fishes.
Regarding species richness distribution at a macro scale, two
characteristics must be considered: i) spatial data autocorrelation and
ii) stationarity in fish richness-macroecological variables
relationship. Autocorrelation of data modifies both the relationship and
the significance of the relationship between the variable of interest
and the predictor(s)(BINI et al. 2009). In the present study, the
spatial autocorrelation was controlled when the GWR radius that had a
Moran’s I index close to zero was chosen, isolating the second
characteristic (stationarity) and facilitating its analysis.
The absence of stationarity relationship found can be derived from
environmental heterogeneity, usually associated to altitude variation
that causes climatic anomalies and modifications of local
conditions(Kerr and Packer 1997; O’Brien et al. 2000; Rahbek and Graves
2001). Depending on the location and altitude variation, this landscape
heterogeneity can increase or decrease diversity. The presence of
mountain ranges, like Serra do Mar (southwest) in this study, results in
an increased humidity on its windward side and the formation of drier
and warmer areas on its leeward side, since the wind and humidity are
blocked by the windward side. Local variation of temperature,
precipitation and wind regimen influence on microclimate, which
consequently change habitat availability and quality. As a result, sites
favored by the windward effect can display greater species richness
whereas those under the leeward influence could show less species
richness, as predicted for all models. The topography gradient was
observed as a functional factor structuring fish assemblages in streams
of the Tocantins-Araguaia basin, (lower altitudes in the Araguaia
(lower) and elevated ones in the Tocantins basin(Carvalho and
Tejerina-Garro 2015)). Additionally, geographic heterogeneity increases
geographic area(O’Brien, Field, and Whittaker 2000) and allows events of
allopatric speciation to occur by interrupting geneflow between
populations due to physical discontinuities in the riverbed (waterfalls
and dams) or physiochemical changes (pH, temperature(Rahbek and Graves
2001). An increase of available area favors more individuals and species
that can occupy a region. In regions with high elevation, such as Serra
do Mar (2,366 m) and Espinhaço (2,072 m), it is common to observe low
temperatures and, in lower elevations (near the ocean), higher
temperatures. This thermal difference can make species diversity smaller
than what was predicted for the region, due to local extinction of
species less tolerant to cold weather(Girard et al. 2015; Mas-Martí et
al. 2014). This mechanism could occur in regions that have an elevated
altitude, such as observed in the Brazilian central region in this
study. On the other hand, geographic heterogeneity (quantified by the
topography) can create more complex habitats, and allow the coexistence
of more species, than regions with not so conspicuous
elevations(Bickford and Laffan 2006).
Regarding the macroecological variables, climate was observed to be what
most influences richness distribution of stream fishes. Sixty percent of
the diversity gradients had their observed patterns explained by
climatic factors, some of them with R-squared close to 90%(Hawkins et
al. 2003). In this case, the most important factors for determining
species richness are water availability and energy input (Hawkins et al.
2003). The non-stationarity in the relationship between richness and
climate was also studied by Hawkins et al.(Hawkins et al. 2003), who
observed temperature as more important in high latitudes (colder places)
than in low ones (tropical regions). In this study, the variation of
temperature was observed as the factor of greatest influence on fish
richness, presenting positive relationships in the Brazilian central
region and negative in the Amazonian one. The non-stationarity of the
relationship between stream fish richness and temperature oscillation
can be explained by the climatic heterogeneity of the study area and the
climate influence on the taxonomic diversification of the fish. Fish
populations found in the Brazilian central region are inserted in a
savannah landscape characterized by a tropical climate, with a
well-defined dry season and rainfall concentrated in only one period of
the year(Marengo and Valverde 2007). This climate type is characterized
by seasons with 250 mm or <10 mm of precipitation per month
and soil temperature varying between 20 and 40°C(N. B. F. dos Santos,
Júnior, and Ferreira 2011). Fishes from the Amazonian region are located
in areas with equatorial climate, where annual precipitation is 2,000 mm
distributed equally throughout the months of the year, presenting an
average soil temperature of 27°C varying less than 3°C(S. R. Q. dos
Santos et al. 2011). Therefore, fish populations present in savannah
areas predominant in the Brazilian central region sampling sites are
exposed to a greater range of temperature variation, thus eliminating
the occurrence of species with a small thermal range. On the other hand,
in Amazonian areas, where the thermal variation is lower, tolerance to
changes in temperature should not be a key factor in species selection.
This could explain why we observed both tolerant and intolerant species
to temperature variation in this region. Consequently, a negative
correlation pattern is observed between temperature variation and stream
fish richness.
The non-stationary relationship between temperature oscillation and fish
richness found in this paper was also observed in snakes (Elapidae) and
attributed to historical factors of the group’s recent
diversification(Braga et al. 2014). The influence of temperature (as
well as precipitation) driven diversification in recent taxonomic groups
and favoring diversity gradients has ample acceptance in recent
literature (Hawkins and Porter 2003b; Rodríguez et al. 2005; e.g.:
Hawkins et al. 2003) Two mechanisms, the trophic cascade (greater amount
of energy available in the system results in an increase of primary
productivity) and the metabolic requirements (different species with
different temperature tolerances) are proposed to explain the influence
of temperature over the richness gradient(Hawkins et al. 2003).
The results presented here, suggest the relation between the stream fish
sampled, the trophic cascade and the metabolic requirements mechanisms.
The annual estimate evapotranspiration (AET) in June, which represents
the measurement of energy input to the system, is the variable with the
second greatest magnitude in determining the observed richness pattern.
This variable had a negative relationship with stream fish in the
Amazonian region and a positive one in the Brazilian Central region,
therefore supporting the idea of physiological restriction. This result
strengthens the hypothesis that Amazonian fish have low tolerance to
thermal variation, the inverse occurring in fish from the central
regions. Additionally, terrestrial primary production predicted fish
richness, suggesting the influence of the trophic cascade mechanism.
High terrestrial primary productivity is associated with areas that have
dense vegetation coverage (England and Rosemond 2004). Forested riparian
zones make available large inputs of leaves and terrestrial insects to
the instream environment(Meyer et al. 2007), as is the case with the
sampled streams (1st and 3rd order).
The input of resources from terrestrial vegetation occurs in two ways;
i) vertically – leaves, fruits, seeds and plant parts directly falling
into the streams; and ii) horizontally – lixiviation of these resources
from adjacent areas into the waterbody during the rainy season and/or
pulses of inundation(Junk W., Bayley E.P. 1989; Junk and Wantzen 2004).
With the entry of allochthonous resources, there is an increased
resource availability for primary consumers, thus supporting a richer
and more abundant food web.
A particularity of terrestrial primary production observed in this study
is its negative effect on fish richness. It suggests that the metabolic
restriction mechanism is more important than the trophic cascade
mechanism. Organisms in this region, including aquatic ones, are exposed
to a greater thermal amplitude(Marengo and Valverde 2007), which,
together with increased terrestrial primary productivity, limits species
richness. This effect possibly occurs due to increased surface shading
of the streams’ main channel caused by dense riparian vegetation, since
a greater primary productivity is related to areas with denser
vegetation(England and Rosemond 2004). The dense vegetation stabilizes
local microclimate(Monadjem and Reside 2008; Vieira, Dias-Silva, and
Pacífico 2013) reducing climatic heterogeneity (cold water) and
consequently species richness, possibly due to local extinction of
fishes that had a higher optimum temperature.
The Water-Energy hypothesis is the main predictor of species richness
considering the physiological mechanism². This hypothesis predicts a
positive relationship between species richness and water quantity in
lower latitudes and energy in places of higher latitudes². This
relationship was observed in the present study for fish richness in
Brazilian streams, where portions located close to the equator
(Amazonian region) had a positive relationship to water quantity
(average annual precipitation) and negative to energy input (AET in
June). Portions with higher latitudes (Brazilian Central region) had a
positive relationship with fish richness, while water had no
relationship. The tradeoff between water and energy and diversity seems
to be more dependent on water scarcity than energy restriction. Hawkins
et al.² found that annual precipitation is the variable that determines
diversity patterns in birds from the Australian continent, challenging
what was expected by the literature, since the region is in an area of
high latitude. In another study, Kessler(Kessler 2001) found that
pteridophyte richness was a function of precipitation. This relationship
was observed in Andean regions, where the expected would be energy
(temperature, AET) acting as the limiting factor². These two
relationships demonstrate that geographic and climatic heterogeneity
generate non-stationary relationships, supporting the hypothesis stated
in this paper, that is, the metabolic mechanism acts in a more
deterministic way than the food web mechanism, although both are not
mutually exclusive².
In conclusion, the diversity pattern of fishes in streams is a function
of climatic variables and terrestrial primary production, where both the
Water-Energy dynamic and metabolic restriction mechanism are more
evident. The metabolic restriction mechanism divides Brazil in two
regions: i) Amazonian, with a more stable climate and populations with
low tolerance to thermal variation; and ii) Central, with greater
temperature amplitude and populations more resistant to thermal
variation.