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R. W. Myster

Department of Biology, Oklahoma State University, Oklahoma City, OK 73107, USA.

Corresponding Author Email: mysterrwm1@gmail.com

DOI : https://doi.org/10.51470/JPB.2024.3.1.37

Abstract

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Introduction

Research into how gradients structure ecosystemshas a long history in ecology (1,2,3,4).  For example,studies have shownthat species are distributedrandomly across gradients (1,2,5),  gradients have various effects across latitudes, longitudes, and elevations (4), and gradients due to climate changehave increasing important impacts on ecosystems(6,7). Among gradients,spatial gradients across elevations have been especially good places to investigate this relationshipthey include abiotic drivers – for example,precipitation, humidity, and temperature – that have been shown to have fundamental effects on mountain ecosystems (Chapter8).

     Within the Neotropics, a large elevational gradient is created by the Andean mountainsalong the western edge of South America.  These Andes consist of “cordilleras”(9) where at their eastern flank they drop down to Western Amazonia (10,11,12) and at their western flank to the Pacific ocean (13).Occurring onall the cordillerasbetween 1000 m and 3000 m at sea level (i.e., a.s.l) are Cloud forests(8,14).  These Cloud forests both add to the biodiversityof the Neotropics (e.g., the Andes themselves have 16.4% of all the plant species in the world:15) and significantly contribute toNeotropical hydrological and other biogeochemical cycles (16).

Therefore because of the importance of Andean cloud forests to theneotropics,and because of the importance of theelevational gradient in defining those Cloud forests, I take advantage of a well-defined Cloud forest and elevational gradient in the Andes of Ecuador to sample tree(including tree ferns) species, treefamilies and treephysical structure, and then use that data to test these six hypotheses dealing with plant taxa and ecosystem structure:

(1) Treespecies and treefamilies are distributed along the Andean elevational spatialgradient individualistically (2,17,18) without any pattern of clumping,

(2) Some of these species and families have a mid-elevation peak along that gradientwhich may be due to the overlap of the distributions of the same tree species and treefamilies found at both higher and lower elevations (11),

(3)  Many of the species and family gradient patterns can be fitted significantly tomathematical models(19,20) with a skewed unimodal patterna plateau pattern most common (21,22,23),

(4)  Treephysical structure is distributed along the Andean elevational spatial gradient individualistically(2,17,18) without any pattern of clumping,

(5) Many of these structural patterns can be fitted significantly to mathematical models (19,20) with a symmetric unimodal (Gaussian) patternmost common (21,22,23,24), and

(6) Structural patterns distributed across the Andean elevational gradient are similar to tree structural patterns found in other Cloud forests in the Neotropics(25,26,27).

Material and methods

Study area

The Reserva Biológica San Francisco (RBSF: 3^o58’ 30” S, 79^o4’ 25” W,16,28)in Southern Ecuador was where the study was conducted.  RBSF is in the Andean Cloud forest but not all of it is primary, some of that Cloud forest is secondary due to the past impacts on indigenous peoples (29,30).Soils include Dystrudepts, Haplosaprists, Petraquepts, and Epiaquents(31). Temperaturesspan 9^oto17^o C and annual precipitation from 2200 to 5000 mm per year(31).

Field sampling

In January 2019 my field assistants and I randomly chose an elevational gradientwith a south-facing aspect in an Andean Cloud forest across from Rio San Francisco – RBSF – and set up one50m x 50m (¼ ha) plotat 1900m, 2000m, 2100m, 2200m, 2300m, 2400m, 2500m, 2600m, 2700m and 2800m.  This plot size and shape have been used successfully to sample floristics and physical structure in this same Cloud forest at RBSF and at several of these same elevations (see Chapter16).  In each plot we sampled all trees (including tree ferns) at least 10 cm in diameter at breast height (dbh), measuring at the lowest point where the stem was cylindrical but above the buttresses if the tree was buttressed. We also identified the trees to family, to genus and (if possible) tospecies,using (32) and (33) as taxonomic sources and also consulting the Missouri Botanical Garden website (www.mobot.org) where voucher samplesare kept.  No research permits were necessary because the sampling was non-destructive and did not include any removal of biomass.

Data analysis

The number of stems for each treefamily and the number of stems for each common treespecies (defined as those species having at least 5% of thetotal stems of their family) was first compiled in each sampling plot on the elevational gradient. Next for each plot, these structural parameters were computed (1) treestem density,  number of stems in each size class: 10 ≤ 19cm dbh, 20 ≤ 29 cm dbh, 30 ≤ 39cm dbh and ≥ 40 cm dbh, and the mean dbh for all stems combined, (2) family richness, genus richness and species richness, (3) Fishers’ alpha (α) diversity (34: http://groundvegetationdb-web.com/ground_veg/home/diversity_index), (4) total basal area(∑πr²; r =  dbh of an individual stem / 2), (5) above-ground biomass (ABG: [35]), and (6) canopy closure (sum of all tree crown areas in a plot divided by the area of that plot, where crown areas are estimated from regressions on dbh [36]).

The number of stems at each elevation for each common tree species, for each treefamily, and for each structural parameter was then subjected to a curve-fitting analysis (19, 20, 37) using (1) a symmetric unimodal model (i.e., a standard normal Gaussian distribution: 38),(2) a skewed unimodal model,(3) a linear modeland (4) a plateau model (21,22,23).  Each model employed least-squares regression analysis after the appropriate transformation (38,39,40,41) where the stem data did not have an upper bound (42).  The independent variable was elevation and the dependent variables were number of stems in a species or a family, or a structural parameter(software @ www.MyCurveFit.com was used).  Significant regressionsare expressed in the results as (1) the Y-intercept of the best-fit regression line, (2) the slope of that line, (3) the amount of variation explained by that line (R²), and (4) the p-value of the best-fit line.

Results

All 29 treefamilies had individualistic distributions, peaking at different elevations (e.g., the families Aquifoliaceae [at 2400 m] and Euphorbiaceae [at 2300 m] peaked at mid-elevation: Table 1).  Melastomataceae (with 461 stems), Lauraceae (with 430), Clusiaceae (with 182), and Rubiaceae (with 180) were most common.  Solanaceae (with 1 stem), Malvaceae (with 3), Annonaceae (with 4), Cyatheaceae (with 4), and Hypericaceae (with 4) were least common.  Clusiaceae, Lauraceae, and Melastomataceae had stems in every plot.  Annonaceae, Hypericaceae, Malvaceae, Monimiaceae, Solanceae, and Sapindaceae had stems in only one plot, where the plot at 2400m was the only one with more than one (2) of those families.  Ten of those families fit curves (mathematical models) significantly: a symmetric unimodal curve for Aquifoliaceae (R² = 80%, p = 0.03), Clusiaceae (R² = 78%, p = 0.29), Euphorbiaceae (R² = 62%, p = 0.05),Primulaceae (R² = 90%, p = 0.01) and Podocarpaceae (R² = 82%, p = 0.03), a skewed unimodal curve for Lauraceae (left:R² = 88%, p = 0.02) and Melastomataceae (left:R² = 65%, p = 0.05), a linear curve for Myrtaceae (Y-intercept = 0.51, slope = 0.31, R² = 75%, p = 0.25) and a plateau curve for Primulaceae (R² = 85%, p = 0.02) and Rubiaceae (R² = 70%, p = 0.04).

All of the most common tree species were in separate tree families – except for two species in Clusiaceae, three species in Lauraceae, three species in Melastomataceae, and two species in Rubiaceae – and also had individualistic distribution patterns with peaks at different elevations: (Anacardiaceae) Tapirira cf guianensis(2000m), (Clusiaceae) Clusia alata(2200m), (Clusiaceae) Clusiadoaoides(2300m), (Euphorbiaceae) Alchornea lojaensis(2300m), (Lauraceae) Nectandra membrenacea(2200m), (Lauraceae) Ocotea sp. (2500 m),  (Lauraceae) Persea weberbouri(2600m), (Melastomataceae) Graffeariedaenarginata(1900m), (Melastomataceae) Miconia puntacta(2700m), (Melastomataceae) Tibouchina lepidota(2400 m), (Myrtaceae) Myrcia sp. nov.(2500m), (Rubiaceae) Palicouria uiloana(2000 m) and (Rubiaceae) Elaeagia cf. obovatoi(1900m).  Four of those species fit curves (mathematical models) significantly: a symmetric unimodal curve for Clusia alata (R² = 68%, p = 0.04) and skewed unimodal curves for Nectandra membranaceae (left:R² = 85%, p = 0.03), Graffearieda enarginata(left: R² = 62%, p = 0.05) and Miconia punctata(right:R² = 54%, p = 0.05).

Stemdensityincreased from 772/ha to 980/ha, but not monotonicallybecause total tree stems per unit area decreased from 2100m to 2200m (Table 2).  In every plot the smallest stem size was most common and the next smallest stem size had almost all of the remaining stems.  All canopies were in the 60-70% closure range.  Three structural patterns fit curves (mathematical models) significantly: linear curves for total stems(Y-intercept = 1.77, slope = 0.11, R² = 85%, p = 0.02) and for % of the total stems 20 < 29 cm dbh (Y-intercept = 22.54, slope = -1.81, R² = 69%, p = 0.04), and a plateau curve for % of the total stems > 40 cm dbh (R² = 73%, p = 0.03).

Discussion

Hypotheses 1, 2 and 3

There was support forthe first hypothesisbecause species and families were distributed individualistically along the gradient (2) peaking at different elevations, similar to (17) and (18) and where species peaked at different times after abandonment from cultivation (a temporal gradient:4),some support for thesecond hypothesis because three species and two families showed a mid-elevational peak at 2300 m or 2400 m,and some support for hypothesis threebecause four species out of 13, and 10 families out of 29, had significant curveswith the symmetric unimodal most common.

There was a substantial number of plant families and a large variation in number of stems within the families.  Plant families found across this elevational gradient were also commonly found (1) in primary cloud forestswithin RBSF (31), within the Andes (43,44,45), and the Neotropics (46), and (2) in secondary cloud forestswithin RBSF (16), within the Andes (43,44,45,47,48) and the Neotropics (46).Curve-fitting algorithms found significant models for more than a third of the familieshalf of those were symmetric unimodal (Gaussian)models (40).  All of the most abundant families had significant models,which included both of the families that fit a skewed unimodal model.There were differences among thefamilial response curves across the elevational gradient(24).Linear monotonic familial responses were rare and most of the abundant species did not reach their peak abundance at mid-elevation (11). Distributions of species across this ACF elevational gradient were more continuous (which supports an ecocline perspective) than discrete (which would have supported an ecotone perspective:see discussion in [47]).  Moreover,the number of families among sampling points did not increase with elevation (see Rapoport’s rule:49).

Common plant families sampled in this study were also common in the cloud forests in the Dominican Republic (Melastomataceae:50), in Thailand (Lauraceae:51), in India (Melastomataceae, Clusiaceae and Rubiaceae:52), in Sri Lanka(Clusiaceae:53) and Cameroon (Melastomataceae, Lauraceae, Clusiaceaeand Rubiaceae:54).  Cloud forest studies outside the Neotropics that computed structural parametersusing different tree sizes (starting at 5 cm dbh in Kenya:[55]and at 2 cm dbh in Hawaii:[56]) however, made comparisons with this study difficult.

As suggested in hypothesis two, several of the hump-shaped (symmetric unimodal) curves found in this study may bedue to overlapping species distributions from the bottom and the top of the RBSF elevational gradient (the mid-domain effect:[61]), as was true for fern species richness along another elevational gradient (40). Also for ferns, this time in Costa Rica, species richness showed a symmetric unimodal curve with elevation (peaking at 1700 m:[62]).  In Australia, Eucalypt species (Eucalyptus sp.) had skewed unimodal curves where the direction of the skew depended on the optimum temperature (21).Most of the curves for salt marshes vegetation were skewed unimodal (63) and for swamp forest vegetation unimodal curves were seen for species with low tolerance, but not seen for species that favored gradient extremes (23).

For tree pollen collected in PeruAlchorneasp.and familiesUrticaceae (Moraceae), Bignoniaceae and Cecropiaceae dominated below 1000m, Acalyphasp., Alchorneasp.andCecropiaceae, Rubiaceae and Urticaceae (Moraceae) between 1000m and 1600m,Hedyosmumsp., Alnussp.and Poaceae and Combretaceae (Melastomataceae) between 1600m and 2000m, Alnussp. between 2000m and 2700m, and above 2700m, Polylepissp., and Asteraceae, Poaceae and Myrsine (64).In the Bolivian Andes richness of epiphytes peaked at 1500 m and then declined with elevation, with ridges having lower richness than slope forests and usually hump-shaped curves(65).But effects of the elevational gradient may be less than effects of forest edge and topographical heterogeneity (66).

Hypotheses 4, 5 and 6

There was some support for hypothesis fourbecause some structural parameters were individualistic, some support for hypothesis five,because there were significant curves butunimodal patterns were not most common, and mixed support for hypothesis sixbecause Cloud forest structural parameters in this plot compared well to another sampling in Ecuador and also to sampling in Venezuela but not as well to samplings in Costa Rica or Puerto Rico.  Most other studies did not have the data to confirm the result of similarities of basal area and AGB with elevational changes ([67] did not show this).When compared to other Neotropic Cloud forests, total stems in this RBSF Cloud forest were similar (25,26,27,44,67) but the basal area was less.  This gradient had a higher density at 1400m in Ecuador (44) but similar in Venezuela (27), with larger stems at 1400 m both in Ecuador (44), and in Costa Rica (25,26).  Species richness and basal area was similar to Ecuador (44) but not to Costa Rica (25,26) or Puerto Rico (67).

     The most general result for physical structure was that as stem density increased with elevation, stem size decreased which lead to similarities in basal area, AGB and canopy closure among the sampled points across the gradient.  Two of the three significant curves were linear which may relate to the linear variation of temperature and precipitation along this gradient (16).  In addition, many of the changes in physical structure along this elevational gradient reflect known trends of a decrease in primary productivity, height of canopy and emergent trees, and number of strata as elevation increases (44,45).  There was not an increase in stems of all sizes with elevation in this study and diversity showed little variation, compared to elsewhere in EcuadorCloud forests (44).  There were mid-elevation peaks in Puerto Rican montane forest forbasal area, above-ground biomass, canopy height, and mean species richness, and an increase in the proportion of native and endemic species with elevation (68).  Finally across most of the tropical latitudinal gradientrichness patterns are symmetrically hump-shaped, but at the edges of the Andes (18° N and 18° S latitude) there are differences (69). 

Conclusion

I conclude for the three floristic hypotheses (1) tree floristic patterns along the Andean elevational gradient were individualistic with no families clumping in distribution with other families, (2) Aquifoliaceae and Euphorbiaceaedid show a mid-elevation crest in abundance (i.e., a symmetric unimodal/Gaussian pattern) and (3) many of the familial patterns were fitted to common mathematical modelswith all models present and a symmetric unimodal pattern most common.

     I conclude for the three structural hypotheses (1)several structural patterns (i.e. basal area, AGB, richness, diversity) along this gradient were not individualistic, (2) several patterns were fitted to common mathematical models with a linear curvemost common and (3) patterns compared well to several other Neotropic Cloud forests.  While stem density increased with elevation,the size of stems decreasedmaking several parameters (e.g., basal area, above-ground biomass) similar regardless of elevation.  Across the gradient richness and diversity wereconsistently lowand the study Cloud forest had a more closed canopy than Amazon forests.

     This study constructed and analyzed response patterns, and then could only compare those patterns against hypotheses about pattern.  This study does set-up, however, future studies that could investigate mechanisms causing the shapes of these and other response curves.  Those mechanisms could include (1) competition and physiological tolerance(57), (2) geometric constraints on habitat for unimodal response curves, and (3) resource availability and temperature relationships within the elevational gradient for linear response curves (see discussion in 58).  In particular, for the Andes higher relief and steeper rainfall gradients as well as more human disturbance and climate change on the eastern flank of the Andes compared to the western flank, might help explain future response curves if they show aloss of plant families and a reduction of structure over time (11,59,60).I suggest that many of these mechanisms relate to the plant-plant replacement process (8,71) by influencing (assuming a low degree of seed rain limitation:[72])seed (i.e., predation, pathogens, germination) and seedling (i.e., herbivory, pathogens, competition) processes (i.e., the regeneration niche [73]).  And so an important research area in the future should be how insects, birds, mammals (all potential predators), and fungi (a potential pathogen) taxa vary and function along Andean elevational gradients (see chapters in [8]).

     Finally the floristics and the physical structure data sampled here in the primary Andean Cloud forest (and elsewhere in the primary Andean Cloud forest: author, unpub. data) creates an important baseline data set which can be used in future comparative studies of primary, but also secondary Andean Cloud forests as they recover from disturbances across their elevational gradients (e.g., tree-fall, landslides, conversion to agriculture and pasture after slash-and-burn, roads and urban development). Long-term sampling of both primary and secondary Cloud forest in the Andes (see chapters in [8]) also contributes to this research objective. 

Funding

This work was self-supported.

Acknowledgements

I thank Jessica Paccha and Edgar Dario Ramon Castillo for their help in the field and PedroPaladines for logistic assistance at the RBSF field station.  I also thank D. Strong, D. Bonte, K. Gross, J. Powers, H. Asbjornsen, D. Williams, V. Novotny, T. Whitfeldand D. Gibson for commenting on a past draft of the manuscript.Please visit my webpage (www.researchgate.net/profile/Randall_Myster) for more details about this and similar research.

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