Species description
Araucaria angustifolia belongs to the family Araucariaceae. A. angustifolia is named as Araucaria, Paraná pine, Brazilian pine, and candelabra tree (Breuninger et al. 2008). An evergreen endangered tree to 40 m with thick, tough, and triangular-like leaves and a long straight trunk. These leaves are broad at the base. However, they are razor-sharp at the edges and tip (Hoogh and Dietrich 1979). A. angustifolia used to cover an area of 233,000 km2 from Brazilian territory. Nowadays, it has lost 97% of its original area to logging, agriculture and Pinus plantations (Dobner Junior et al. 2019). It is tolerant of most soil types. It grows best in well drained, slightly acidic soils inside subtropical climate (e.g., with abundant rainfall more than 1,200 mm). In Brazil, its seeds (called pinhão) are used as a food resource and medicine for the regional farmers, and it played an important role for the small population of natives in the past.
Study area location
The study sites are geographically located at the Florestal Gateados Enterprise in Campo Belo do Sul, Santa Catarina, highlands of Southern Brazil (Table 1). The mean annual temperature of the area is + 15ºC and receives a mean annual precipitation of 1,750 mm. The experimental area was dominated by soybean in the 80’s following a conventional farming system. The soil type in the experimental area was classified as Cambisols (WRB 2006). Köppen’s classification defines the climate of the experimental area as humid-subtropical (Cfb) type (Alvares et al. 2013).
Table 1
General description of the studied sites. with and altitude of 1,135 m above sea level
Site quality
|
Latitude
|
Longitude
|
Altitude (m)
|
Slope (%)
|
No. plots
|
No. soil samples
|
No. monoliths
|
Low
|
S27º51’14”
|
O50º49’21”
|
1,200
|
12
|
25
|
100
|
100
|
High
|
S28º01’50”
|
O50º51’18”
|
1,135
|
6
|
25
|
100
|
100
|
Reconnaissance survey
A preliminary discussion was held with the managers of the Florestal Gateados Enterprise in order to get general information about the monospecific A. angustifolia plantation that covers a total area of 530 ha. Subsequently, a reconnaissance survey was conducted across the monospecific stand to have an overall impression about the study area. At this point, site quality was selected only in terms of dominant height, i.e., according to the accumulated productivity. The relationship between dominant height (h100) and age as a measure of site quality (Skovsgaard and Vanclay 2008) was obtained from 460 sample plots (500 m²). Stands were selected as closest as possible to 30 years of age, thus representing a well-established stand. Then, two sites adjacent to each other were selected for further study (Table 2). In terms of dominant height, at the age of 30 years, an 8-m difference was verified, which, when translated to mean annual increment (MAI) in volume, represents 14 to 31 m³ ha− 1 yr− 1. Both stands were not thinned and thus allowed robust comparisons in terms of growth and yield.
Table 2
Site characteristics including age, site index (SI) stocking (N), quadratic mean diameter at breast height (dg) dominant diameter at breast height (d100), dominant height (h100), basal area (G), standing volume (V), and mean annual increment (MAI) in volume.
Site quality
|
Age
Years
|
SIa
|
N
trees ha− 1
|
dg
cm
|
d100
cm
|
h100
m
|
G
m² ha− 1
|
V
m³ ha− 1
|
MAI
m³ ha− 1 yr− 1
|
Low
|
31
|
16
|
2,281
|
17.7
|
28.0
|
14.2
|
56.6
|
420.2
|
13.6
|
High
|
30
|
26
|
1,488
|
26.0
|
40.2
|
22.0
|
79.0
|
922.7
|
30.9
|
Amplitude
|
11–51
|
|
60 − 2,560
|
11–50
|
17–78
|
7–23
|
4–53
|
19–556
|
-
|
a Site index, the dominant height at an index age of 40 years, according to the classification proposed by Schneider et al. (1992). |
Scheme of sample plot
A systematic sampling approach was implemented to conduct the field study. A total of twenty-five plots, 20 × 25 m were established in each site. The first plot was laid out systematically using a compass, 250 m away from the edge to avoid an edging effect. The transect lines (five transects per site) were made along the centre of each studied site and 50 m away from each other. Soil samples were taken in the centre of the plots at the 0–20 and 20–40 cm soil depths. The samples were homogenized, air-dried, and organic residues were removed manually. Then, soil samples were dried in an oven at 60°C, sifted in a 2-mm mesh sieve, and subjected to analyses. Clay content was 540 and 405 g kg-1 at the 0–20 and 20–40 cm of soil depths, respectively. Soil pH (H2O) was 5.6 to 6.3 at 0–20 cm, and 5.2 to 5.7 at the 20–40 cm soil depth; CEC values at soil pH were 16.4 and 19.3 cmolc kg-1 at the 0–20 and 20–40 cm soil depths, respectively. Total organic carbon and available P (Mehlich 1) ranged from 20.6 to 14.5 g kg-1 and from 3.8 to 1.4 mg kg-1, at the 0–20 and 20–40 cm soil depths, respectively (EMBRAPA 1997; Tedesco et al. 1995). The inventories were conducted in July and December 2019.
Data analysis
Soil organic matter formation
To characterize the litter compartment and layers of organic matter at intermediate stages of decomposition (F-layer and H-layer), four soil monoliths by each studied plot were collected accordingly to Fassbender (1993). Before collecting the soil monoliths, an area of 20 × 20 cm on the soil surface was delimited for separately sampling the litter layer. After that, we extract soil monoliths with the following dimensions 20 × 20 × 20 cm. Next, we wrapped them with plastic film and transported all the monoliths with minimal disturbance until analysis. During our analysis, the monoliths were dissected into the litter, F-layer, H-layer, and A horizon. We considered the F-layer to be the material composed of partly decomposed litter, the H-layer the material with well-decomposed litter, and the A horizon composed exclusively of mineral material (Toutain 1987). The ratio of organic matter layers was calculated using the following equation: ROMLi = dmi/Tm, where dmi is the dry mass of each layer (e.g., litter, F-layer, H-layer, and A horizon), and Tm are both the total dry mass of the soil monolith (20 × 20 × 20 cm) and dry biomass of litter.
Fine roots
To estimate fine root (diameter: <2 mm) dry biomass, we collected roots from the soil samples of each layer (e.g., litter, F-layer, H-layer, and A horizon) during the monoliths processing described above. Fine roots in these layers were washed using a 0.5-mm nylon mesh bag. We sorted fine roots into living and dead roots based on morphology and condition. Only living roots were considered to estimate dry biomass. Fine roots included both tree and herbaceous species because it was difficult to distinguish between these precisely. Fine root dry biomass (g) was determined after drying the samples for 48 h at 70°C. Samples of each layer (litter, F-layer, H-layer, and A horizon) from monoliths were air-dried and passed through a 2-mm sieve.
Soil chemical properties
We determined soil pH in a suspension of soil and distilled water (1:2.5 ratio) (Black 1965). Total soil nitrogen and soil organic carbon were estimated according to the methodology described by Okalebo et al. (1993). Phosphorus (Psbe) was determined using the Olsen’s P protocol (Olsen et al. 1954). The soil moisture was measured by the gravimetric method, where a fresh soil sample was weighed, oven-dried until no further mass loss, and then reweighed (Black 1965).
Soil fauna
At the end of the winter and summer, we sampled four soil monoliths (20 × 20 × 20 cm) to extract and characterize the soil fauna community per studied plot (e.g., low- and high-productivity sites), and collected the organisms manually using metal clips. They were stored in containers with 70% alcohol until identification as recommended in Tropical Soil Biology and Fertility (Anderson and Ingram 1989). These were later counted and identified under a stereoscopic microscope, at the level of a major taxonomic group. The term “taxonomic group” was used in the soil macroarthropod study, meaning either a Class, as Order or even Family, to comprise a set of individuals with a similar life form. The communities were characterized based on the following parameters: (a) richness and (b) Shannon Diversity Index (H) (Shannon and Weaver 1949). We assessed the frequency of occurrence of each taxonomic group by both studied sites. In addition, we classified the taxonomical groups according to their functional groups as described by Souza and Freitas (2018). The frequency of occurrence was calculated using the following equation: FOi = ni/N, where ni is the number of times an organism was observed, and N is the total number of organisms observed from each studied ecosystem.
Arbuscular mycorrhizal fungi and soil nematodes
To sample the spores of arbuscular mycorrhizal fungi and soil nematodes, we sampled undisturbed soil cores (n = 4 per studied plot and 300 g of soil each core), wrapped them, and stored them with minimal disturbance until specimen’s extraction as recommended by Souza and Freitas (2018). For AMF extraction, spores and sporocarps from the field were extracted by the wet sieving technique (Gerdemann and Nicolson 1963) followed by sucrose centrifugation (Jenkins 1964). Initially, the extracted spores were examined in water under a dissecting microscope and they were separated based on morphological characteristics. Subsequently, they were mounted in polyvinyl alcohol in lacto-glycerol (PVLG) with and without the addition of Melzer’s reagent (Walker et al. 2007). Species identification was based on the descriptions provided by Schenck and Perez (1987), and by consulting the online AMF collection of the Department of Plant Pathology, the University of Agriculture in Szczecin, Poland (http://www.agro.ar.szczecin.pl/~jblasxkowski/) and the International Culture Collection of Arbuscular Mycorrhizal Fungi Database—INVAM (http://invam.caf.wvu.edu). The AMF communities were characterized based on the following parameters: (a) richness and (b) Shannon Diversity Index (H) (Shannon and Weaver 1949). We assessed the frequency of occurrence of each taxonomic group at both studied sites. For soil nematodes, we used the method described by Buchan et al. (2013), to separate free-living nematodes from soil components (e.g., organic matter and clay). We counted the soil nematodes under a binocular microscope. Next, the soil nematodes were fixed with a 4% hot (70ºC) formaldehyde solution. Finally, nematode identification using trophic groups was carried out according to Yeates et al. (1993).
Microbial biomass carbon
Soil samples were put into pots. They were brought to and maintained at ca. 50% water-filled porosity and incubated at 18°C for 45 days. The amount of distilled water was based on the bulk density and initial moisture content of the soil. Water reposition was calculated weekly using a mass balance of each pot. Four replications from each studied site were sampled after 5, 15, 30, 45, and 60 days of incubation. Microbial biomass carbon (Cmic) was determined using the fumigation-extraction protocol described by Vance et al. (1987). We divided the soil (20 g of fresh soil per pot) into fumigated and non-fumigated controls. The Cmic was extracted with 40 mL of 0.5 M K2SO4 and stored at − 18°C until analysis. Organic carbon contents of the extracts were determined by the rapid dichromate oxidation method described by Okalebo et al. (1993).
Statistical analysis
Before analysis, all the variables were tested for normality (e.g., by Shapiro-Wilk test) and homoscedasticity (e.g., by the Bartlet test), and log transformations were applied to meet both required criteria. To find possible spatial autocorrelation, we used the Moran.I function (Gittleman and Kot 1990). We did not detect any relationship between the variables and the sampling points, indicating spatial independent samples. Soil properties, soil biota, and microbiota community composition, and microbial biomass carbon were analyzed with a non-parametric paired t-test followed by the Monte Carlo test (1000 permutations). The dissimilarities between the site quality (e.g., by Bray-Curtis distance measure) were analyzed using non-metric multidimensional scaling (NMDS), which provided a graphical ordination of the variables that when presenting a measure of stress less than 0.01, indicate an excellent fit of the model (Zuur et al. 2007). It also enables us to reduce the number of the variables used to determine which abiotic or biotic variable explained most of the variation in the productivity sites (Oksanen et al. 2013). All analyses were run using R 3.4.0 statistical software (R Core team 2018).