**Enhanced Electrical Properties and Stability of P-Type Conduction in ZnO Transparent Semiconductor Thin Films by Co-Doping Ga and N**

#### **Chien-Yie Tsay \* and Wan-Yu Chiu**

Department of Materials Science and Engineering, Feng Chia University, Taichung 40724, Taiwan<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +886-4-2451-7250 (ext. 5312)

Received: 20 October 2020; Accepted: 2 November 2020; Published: 6 November 2020

**Abstract:** P-type ZnO transparent semiconductor thin films were prepared on glass substrates by the sol-gel spin-coating process with N doping and Ga–N co-doping. Comparative studies of the microstructural features, optical properties, and electrical characteristics of ZnO, N-doped ZnO (ZnO:N), and Ga–N co-doped ZnO (ZnO:Ga–N) thin films are reported in this paper. Each as-coated sol-gel film was preheated at 300 ◦C for 10 min in air and then annealed at 500 ◦C for 1 h in oxygen ambient. X-ray diffraction (XRD) examination confirmed that these ZnO-based thin films had a polycrystalline nature and an entirely wurtzite structure. The incorporation of N and Ga–N into ZnO thin films obviously refined the microstructures, reduced surface roughness, and enhanced the transparency in the visible range. X-ray photoelectron spectroscopy (XPS) analysis confirmed the incorporation of N and Ga–N into the ZnO:N and ZnO:Ga–N thin films, respectively. The room temperature PL spectra exhibited a prominent peak and a broad band, which corresponded to the near-band edge emission and deep-level emission. Hall measurement revealed that the ZnO semiconductor thin films were converted from n-type to p-type after incorporation of N into ZnO nanocrystals, and they had a mean hole concentration of 1.83 × 1015 cm−3 and a mean resistivity of 385.4 Ω·cm. In addition, the Ga–N co-doped ZnO thin film showed good p-type conductivity with a hole concentration approaching 4.0 × 1017 cm−3 and a low resistivity of 5.09 Ω·cm. The Ga–N co-doped thin films showed relatively stable p-type conduction (>three weeks) compared with the N-doped thin films.

**Keywords:** p-type oxide semiconductor; ZnO thin film; N doping; Ga–N co-doping; sol-gel spin coating; electrical stability

#### **1. Introduction**

Wide-bandgap oxide semiconductors have gained considerable attention and received great interest due to their potential for applications in optoelectronic devices, photovoltaic devices, and transparent oxide electronics, such as ultraviolet photodetectors, short wavelength light-emitting diodes, thin-film solar cells, transparent resistive random access memory, and transparent field-effect transistors [1–4]. The fabrication of light-emitting diodes, p-n junction photodetectors, and complementary metal-oxide-semiconductor devices requires the combination of both n-type and p-type oxide semiconductors. The lack of availability of stable p-type oxide semiconductors limits the electrical efficiency of p-n junction-based devices or bipolar device applications [3,5]. Therefore, the development of stable and reproducible p-type oxide semiconductor thin films is necessary.

The crystalline wurtzite ZnO exhibits n-type conductivity due to a deviation from chemical stoichiometry and the presence of native point defects such as O vacancies (Vo) and Zn interstitials (Zni) that makes the electrons easily be excited to the conduction band [2,6]. The difficulty in achieving p-type ZnO semiconductors is caused by a combination of deep acceptor levels, the low solubility of acceptors, and a strong self-compensation effect [7,8]. Previous studies demonstrated that the impurity doping of ZnO is an important approach for modulating the carrier concentration and electrical resistivity to satisfy device applications [9,10]. A sufficient amount of acceptors should be generated to overcome the n-type back ground, such as any donor-like defects created through preparation processes. It has been suggested that the group V element nitrogen (N) may be a potential acceptor dopant for O site substitution in ZnO to obtain p-type conductivity, for N has the smallest ionization energy among the group V dopants (N, P, and As) [2,11]. In addition, N (0.146 nm) and O (0.140 nm) have close ionic radii, and so N ions may easily be substituted on O sites of the ZnO lattice [12]. Moreover, the N–O bond length (1.88 Å) is close to the Zn–O bond length (1.93 Å) [13]. Kim's group reported that the incorporation of N as a dopant changed the conductivity of sol-gel-derived ZnO thin films from n-type to p-type and produced ZnO:N thin films with a hole concentration of 8.11 × 1016 cm−3 when the N doping content reached 20 at.% in the resultant solution [14]. However, the production of device-quality p-type conduction in N-doped ZnO thin films remains a challenge due to the low dopant solubility in the host material and high ionization energy [11].

Co-doping of two kinds of extrinsic atoms (using acceptors and donors simultaneously) is considered an effective method for producing stable p-type conductivity in ZnO semiconductor thin films [7,15,16]. Previous experimental studies found that acceptor and donor co-doped ZnO thin films not only exhibited p-type conductivity but also showed reasonable stability and reproducibility in electrical characteristics [17–19]. It was reported that simultaneous doping of nitrogen and group III elements can improve the hole concentration and enhance the stability of p-type ZnO thin film because the doping of the reactive donor element increases the solubility of nitrogen in ZnO [20–23]. According to first-principles calculations, Yan et al. proposed that the formation of passive acceptor-donor (Ga–N) complexes can create an impurity band above the valence-band maximum (VBM) in ZnO crystal based on reduction of the ionization energies of dopants [8]. A theoretical study by Duan et al. reported that the ionization energy of Ga–2N complex is lower than that of the single N acceptor [11]. The generation of N–Ga–N complexes could increase the solubility percentage of N ionized dopants and achieve shallower acceptor levels [6]. A previous report noted that the co-doping of Ga and N in ZnO thin films achieved the best p-type character among the types of impurity co-doped ZnO thin films [8]. Theoretical calculations also predict that Ga–N co-doped ZnO would yield better p-type conduction than Al–N co-doped ZnO [11]. This prediction is consistent with numerous experimental finding.

Several thin film deposition techniques have been employed to produce the group III (B, Al, Ga, and In)–N co-doped p-type ZnO semiconductor thin films including chemical vapor deposition, molecular-beam epitaxy, pulsed laser deposition, radio-frequency (rf) magnetron sputtering, spray pyrolysis, and the sol-gel method [2,7]. Most researchers have focused on the preparation of p-type ZnO thin films and investigated the effects of acceptor-donor co-doping on the physical properties. The authors noted that few studies have reported the electrical stability of solution-processed p-type ZnO thin films. There is no report on Ga–N co-doping p-type ZnO thin films prepared by the sol-gel method. Therefore, we prepared ZnO, ZnO:N, and ZnO:Ga–N thin films on glass substrates via the sol-gel method and spin-coating technique. The structural-physical properties relation of ZnO-based semiconductor thin films was investigated in detail through variation of the structural features to examine if they affect the optical properties and electrical characteristics. In addition, the electrical stability of p-type ZnO-based thin films was verified and is reported in this study.

#### **2. Materials and Methods**

#### *2.1. Coating Solution Synthesis and Thin Film Deposition*

The series of ZnO-based thin films, including undoped ZnO, N-doped ZnO (ZnO:N), and N–Ga co-doped ZnO (ZnO:N–Ga), were deposited on alkali-free glass substrates (Nippon Electric Glass OA-10, 50 × 50 × 0.7 mm3 in size) using the sol-gel method and spin coating technique. Prior to the deposition of the thin films, the glass substrates were ultrasonically cleaned in detergent, isopropyl alcohol, and acetone, washed thoroughly with distilled water and dried on a hot plate at 110 ◦C for 15 min. Three kinds of analytical reagent (AR)-grade metallic salts, including zinc acetate dihydrate (Zn(CH3CO2)2·2H2O, J.T. Baker, Phillipsburg, NJ, USA), ammonium acetate (CH3COONH4, Sigma, St. Louis, MO, USA), and gallium (III) nitrate hydrate (Ga(NO3)3·*x*H2O, Alderich, St. Louis, MO, USA), were chosen as the source materials of zinc, nitrogen, and gallium, respectively. The first precursor solutions were synthesized by dissolving a stoichiometric ratio of AR-grade metallic salts in 2-methoxyethanol (solvent) and diethanolamine (stabilizing agent) using a stirring hot plate (PC-420D, Corning, Glendale, AZ, USA) at 60 ◦C for 2 h until the resultant solution became clear and transparent. After that procedure, the synthesized sols were aged for 72 h at room temperature under atmospheric environment before being used in the spin-coating process. The concentration of the amount of metal ions in the resultant solutions was maintained at 0.4 M [24] and the molar ratio of metal ions to stabilizing agent was kept at 1:1. In addition, the atomic ratio of [N]/[Zn + N] was 0.3 for two impurity doped thin films (ZnO:N and ZnO:N–Ga), and that of [Ga]/[Zn + N] was 0.01 for ZnO:N–Ga thin films. The respective sols were spin coated on the pre-cleaned glass substrates at 1000 rpm for 30 s, followed by preheating at 300 ◦C in air for 10 min to evaporate volatile materials. That procedure was repeated three times, and then the dried sol-gel films were annealed at 500 ◦C in oxygen ambient for 1 h to remove residual organic compounds of the gel films and achieve crystalline oxide thin films [22].

#### *2.2. Characterization Techniques*

The crystalline structure and crystallinity of the as-prepared ZnO-based thin films were analyzed by Bruker D8 Discover SSS high-resolution X-ray diffractometer (XRD, Bruker, Karlsruhe, Germany) with Cu-Kα radiation (λ = 1.5418 Å) by glancing incidence technique at an incident angle of 0.8◦. Plane-view and cross-sectional view micrographs of the thin film samples were acquired by Hitachi S-4800 field-emission scanning electron microscope (FE-SEM, Hitachi, Tokyo, Japan). A Digital Instrument scanning probe microscope (SPM, NS4/D3100CL/MultiMode, Digital Instrument, Mannheim, Germany) was employed for characterizing the surface morphology and surface roughness of oxide films in tapping mode with a tip scanning rate of 0.7 Hz. The chemical bonding energies and elemental composition of the oxide films were determined by X-ray photoelectron spectroscopy (XPS, ULVAC-PHI PHI 5000 VersaProbe, Kanagawa, Japan). Prior to the XPS examination, Ar ion sputter-etching was performed for 15 s to remove the surface contamination. The optical transmission characteristics of the samples of glass/ZnO-based thin films were examined with a Hitachi U-2900 ultraviolet-visible (UV-Vis) spectrophotometer (Hitachi, Tokyo, Japan). Room-temperature (RT) emission spectra of three ZnO-based thin films were measured by the HORIBA Jobin Yvon photoluminescence (PL) spectrometer (LabRAM HR, Paris, France) with an excitation wavelength of 325 nm. The electrical characteristics of each thin film sample were measured by the van der Pauw method using the Hall-effect measurement system (HMS-3000, Ecopia, Gyeonggi-do, Korea) with a magnetic field of 0.55 T at RT.

#### **3. Results and Discussion**

Various types of nitrogen (N) sources, including NH3, N2, NO, N2O, and CH3COONH4 have been successfully used to prepare of device-quality p-type ZnO semiconductor thin films. In this study, the N dopant was obtained through decomposition of ammonium acetate to acetamide and then to CO, NO, and NO2 [24]. The NO and NO2 acted as the N sources. It was suggested that NO easily forms NO acceptors, which are shallow acceptors in ZnO [2,8]. Moreover, it was necessary to provide oxygen to suppress oxygen vacancy (Vo) formation during the preparation of solution-processed p-type ZnO thin films. Because Vo may form as a compensation center in p-type ZnO [25], we performed the post deposition crystallization heat treatment in pure oxygen atmosphere to decrease the background carrier concentration.

The results of the XRD examination, shown in Figure 1, indicate that the three kinds of ZnO-based thin films were polycrystalline in nature. The intense sharp diffraction peaks suggested that the pure ZnO thin film had better crystallinity (pattern (i) in Figure 1) than those of the two impurity-doped ZnO thin films. Previous studies reported that the crystalline quality of oxide thin films would be degraded by impurity doping [10,26,27]. Comparison of collected XRD patterns (in 2θ range from 25◦ to 65◦) with the standard JCPDS file of ZnO crystal (JCPDS No. 36-1451) indicated the hexagonal wurtzite phase in the ZnO-based thin films. In addition, no X-ray diffraction signals related to other crystal phases, such as GaN, Ga2O3, and Zn3N2, were detected from the set of thin film samples, indicating the formation of a single phase.

**Figure 1.** X-ray diffraction (XRD) patterns of ZnO, N-doped ZnO (ZnO:N), and Ga–N co-doped ZnO (ZnO:Ga–N) thin films deposited on glass substrates by the sol-gel spin-coating process.

A preferential orientation may occur due to nucleation, horizontal growth of the nuclei and vertical growth of the closed layer. As shown in Figure 1, we not only confirmed that these as-prepared ZnO thin films had a predominance of hexagonal wurtzite phase but also observed that they achieved a favorable orientation along the (101) plane. The relative intensities of *I*(101)/*I*(1000) + (002) + (101) for the ZnO, ZnO:N, and ZnO:Ga–N samples were 0.422, 0.406, and 0.387, respectively. The calculated results reveal that the relative diffraction intensity of the undoped sample (0.422) was significantly smaller than that of the relative intensity (0.497) obtained from the standard JCPDS database; incorporation of N and Ga–N into the ZnO changed the growth rate of the (101) plane such that the three main diffraction planes had close growth rates. It is noted that the diffraction signals of the (102) and (103) planes were very weak for the two impurity-doped samples, implying an apparent decrease in crystallinity. According to the above discussion, we found that these sol-gel-derived ZnO-based thin films had no preferential orientation; impurity doping favored random growth of crystallites and caused degradation of the crystallinity. The average crystallite sizes of the thin film samples were quantified from the full widths at half-maximum and the Bragg diffraction angles of the three major diffraction peaks, (100), (002), and (101), with Scherrer's formula and are presented in Table 1. Table 1 summarizes our examined and calculated results on the structural features and optical properties. The average crystallite sizes for the undoped, N doped and Ga–N co-doped ZnO thin films were 26.9, 20.4, and 17.0 nm, respectively. It was determined that all the obtained ZnO-based thin films consisted of nanocrystals; N doping significantly inhibited the crystal growth rate due to lattice distortion, and the Zn:Ga–N samples had the smallest average crystallite size.

The average transmittance values a and average absorption coefficients b were calculated from the recorded transmittance data of wavelengths from 400 to 800 nm.

Figure 2 shows the cross-sectional FE-SEM micrographs of ZnO-based thin films deposited onto glass substrates, which revealed good thickness uniformity. It can be seen that these oxide thin films possessed significantly granular microstructures, had homogeneous particle size distributions, and exhibited nano-sized pores inside the sol-gel-derived ZnO-based thin films. In addition, it was significantly observed that the mean particle size of the N-doped thin film was smaller than that of the undoped thin film, and the Ga–N co-doped thin film possessed the finest grade of particles. The microstructural features of the developed thin films, observed by FE-SEM, were consistent with the results of the XRD examination. The thickness of each thin film sample was estimated from the corresponding cross-sectional FE-SEM micrograph. The mean film thicknesses of the undoped, N-doped, and Ga–N co-doped ZnO thin films were found to be 85, 45, and 40 nm, respectively. The difference in film thickness between the three kinds of ZnO-based thin films can be ascribed to the variation in the viscosities of the coating solutions with the concentration of solute (metallic salts). The corresponding plane-view FE-SEM micrographs are shown in Figure S1. In that figure, it is clearly shown that the free surfaces of the films were flat, with no visible pores or cracks in the observation area.

The topographic features of each thin film sample were examined by SPM to study the surface morphology and measure the root-mean-square (RMS) roughness. SPM images of the sol-gel-derived ZnO-based thin films deposited on glass substrates are presented in Figure 3. Those images show a uniformly granular morphology and dense surface. The significant differences between the three SPM images were the particle size and surface flatness. It was observed that the co-doped sample had the smallest particles and exhibited a flat surface morphology. The average particle sizes of the ZnO, ZnO:N, and ZnO:Ga–N thin films, as estimated from the corresponding 2D SPM images (Figure S2) in

ImageJ software, were 77.3, 46.2, and 39.3 nm, respectively. The observation of surface morphological characteristics of the thin film samples by SPM had a good agreement with the XRD results. Table 1 lists the values of RMS roughness of the ZnO, ZnO:N, and ZnO:Ga–N thin film samples, which were 3.6, 2.7, and 1.1 nm, respectively. As noted above, the impurity doping reduced the particle (crystallite) size and thereby decreased the surface roughness.

**Figure 3.** Three-dimensional (3D) scanning probe microscope (SPM) micrographs of the free surfaces of ZnO-based thin films: (**a**) undoped, (**b**) N-doped, and (**c**) Ga–N co-doped samples.

XPS examinations were performed to determine the elements responsible and identify the chemical bonding states of the ZnO-based thin films produced in this study. The narrow scan XPS spectra of Zn, O, and N regions of ZnO-based thin films are presented in Figure 4. The core level peaks of Zn 2*p* are presented in Figure 4a; they revealed similar XPS spectra for the three samples. The peaks of binding energies at 1021.0 and 1044.2 eV for the ZnO sample corresponded to the Zn 2*p*3/2 and Zn 2*p*1/2 components, respectively (spectrum (i) in Figure 4a). The electronic state of Zn 2*p*3/2 is associated with the Zn2+ ions in the ZnO lattice [7,28]. In addition, the Zn 2*p* XPS spectra of the two impurity-doped samples showed slight shifts toward the lower binding region due to changes in the strength of the metal–oxygen bond energy because of the incorporation of N and Ga–N dopants into the ZnO nanocrystals.

As shown in Figure 4b, the recorded O 1*s* spectra were obviously broad and asymmetric on the high binding energy side (solid lines). Each O 1*s* spectrum was deconvoluted to three distinct sub-curves (dashed lines) by Gaussian curve fitting and denoted with OI, OII, and OIII for representing three types of oxygen levels in oxide thin film samples [29,30]. The low binding energy component (OI) exhibited the strongest intensity, was centered at 529.6 eV, and was associated with O2− ions bonded with metal ions (Zn2+ and/or Ga3+), such as Zn–O bonds, in the ZnO lattice. The component at the middle binding energy (OII) was centered at 531.1 eV, usually associated with the loosely bound oxygen, and related to the oxygen vacancy concentration. The component at the high binding energy region (OIII), with a weak signal, was ascribed to chemisorbed or dissociated oxygen species, such as hydroxyl groups (–OH) bonded to the surfaces and/or the grain boundaries of the polycrystalline oxide films. The calculated OII/Ototal intensity ratios for the ZnO, ZnO:N, and ZnO:Ga–N nanostructured thin films were 20.44%, 21.30%, and 27.46%, respectively. Such intensity ratios are suggested for evaluating the relative concentrations of oxygen vacancies between the oxide thin film samples. The calculated results reveal that the co-doped ZnO thin film had a higher OII/Ototal ratio than those of the un-doped and single-doped ZnO thin films, indicating that weaker metal–oxygen bonds formed in the former sample than in the latter samples. The reduced strength in the metal–oxygen bonds can be attributed

to the Ga doping in ZnO:N crystals, which in turn led to a significant increase in the oxygen vacancy concentration in the ZnO:Ga–N thin film.

**Figure 4.** X-ray photoelectron spectroscopy (XPS) spectra of (**a**) Zn 2*p*, (**b**)O1*s*, and (**c**)N1*s* of the three ZnO-based thin films.

Figure 4c shows the N 1*s* XPS core level spectra and the single peak centered at about 397.0 eV, which was related to the formation of the No–Zn bond [23,31] and indicated that N ions were incorporated into the ZnO lattices of the N doped and Ga–N co-doped thin films. It was also demonstrated that Ga–N co-doping enhanced the solubility of the acceptor dopant (N) in ZnO nanocrystals (spectrum (iii) in Figure 4c. Ravichandran et al. suggested that the presence of N in the energy region, about 397.3 eV, is indicative of p-type conductivity [32]. In addition, the Ga 2*p* spectrum of the Ga–N co-doped sample is presented in Figure S3. The peak spectrum located at 1117.4 eV corresponds to the electrical state of Ga 2*p*3/2 and is ascribed to Ga3+ in gallium(III) oxide [33]. The actual contents of N and Ga + N in the ZnO:N and ZnO:Ga–N thin film samples were estimated by XPS analysis to be 0.52 at.%, and 1.1 at.% + 0.76 at.%. The examined results confirm that the N and Ga + N were successfully substituted into the ZnO nanocrystals and the solubility of desirable accept dopant (N) was enhanced through co-doping of Ga and N.

The effects on the optical characteristics of the incorporation N and Ga–N in ZnO thin films were investigated by optical transmission measurement. Figure 5a shows three typical optical transmittance spectra, which were recorded from the samples of glass/oxide thin films in the wavelength range of 200–1000 nm. These transmittance spectra exhibited high transparency (>85.0%) in the visible region and displayed an abrupt increase in absorption in the near-UV spectral range. The character of the optical absorption edge (band-edge absorption) is related to direct optical excitation from the valence band to the conduction band and the magnitude of the bandgap energy. The slight shift toward the short wavelength region of the absorption edge in the two impurity-doped thin film samples may be associated with the finer microstructures.

**Figure 5.** (**a**) Optical transmission spectra, (**b**) plot of (αhν) 2 versus photo energy (hν), and (**c**) plot of ln(α) versus photon energy (hν) of ZnO, ZnO:N, and ZnO:Ga–N thin film samples.

The recorded optical transmittance spectra indicated that the ZnO:N and ZnO:Ga–N samples exhibited a similar transmission characteristic and had higher transparency than the ZnO sample, covering the visible light region due to the former (impurity-doped samples) being thinner and having a flatter free surface (see Figures 2 and 3) compared with the latter (undoped sample). The average transmittances of undoped and impurity-doped ZnO thin film samples in the visible region (400 to 800 nm) were about 85.0% and approached 90.0% (the fourth column in Table 1). It is known that the optical transmittance of functional oxide thin films is strongly affected by film thickness, surface roughness, densification, crystallinity, and structural homogeneity.

The absorption coefficient (α), one of the intrinsic optical parameters of functional oxide films, can serve as a rough estimation from measured transmittance data with the Beer–Lambert law (α = [ln(1/T)]/df), where df is the thickness of the examined thin film sample, by assuming reflectance

to be negligible (*R* = 0). The calculated results are listed in Table 1. These results reveal that the average absorption coefficient of the undoped sample approached 1.9 × 104 cm−1 in the visible region and that an abrupt increase in the absorption coefficient (>26.3%) occurred in the two impurity-doped samples due to the microstructural changes and crystallinity degradation as well as impurity band formation. For optoelectronic materials, the relationship between the absorption coefficient (α) and optical bandgap (*E*g) can be expressed as the following relation [34]:

$$\alpha(\text{hv}) = A \left(\text{hv} - E\_{\text{\textdegree}}\right)^{n} \tag{1}$$

where h is Planck's constant, ν is the frequency of incident photons, the coefficient *A* is an energy-independent proportionality constant related to the band tailing states, and the exponent *n* varies according to the energy-band structure of the examined optoelectronic material. For direct allowed transitions, the value of *n* is 1/2. Figure 5b shows the plot of variation of (αhν) 2 with the photon energy (eV) (namely, the Tauc plot), which was acquired from the optical transmittance spectra. A Tauc plot is used to determine the optical bandgap energy in semiconductors by extrapolating the straight-line portion near the onset of the absorption edge of the absorption curve to intercept the photon energy axis. The estimated optical bandgap energy of the impurity-doped ZnO thin films was 3.24 eV, which was slightly narrower than that of the undoped ZnO thin film (3.27 eV). Narrowing of the optical bandgap has been reported for ZnO:N, ZnO:Al–N, and ZnO:In–N thin films. That feature can be explained by the change in crystallite size and the formation of shallower impurity (acceptor and donor) states.

The Urbach energy (*E*U) width has been considered for the study of the structural disorder and band-tail states of semiconductor thin films with impurity doping [22,35]. It can be estimated by the following expression [36]:

$$
\alpha = \alpha\_0 \exp\left(\hbar \mathbf{v} / E\_\mathbf{U}\right),
\tag{2}
$$

where α0 is a constant. We determined the Urbach energy from the plot of ln (α) versus photo energy (hν) (Figure 5c) from the inverse of the slope of the linear segment of the optical absorption spectrum and found that the *E*U values increased from 89.2 meV (ZnO) to 123.8 meV (ZnO:N) and 148.5 meV (ZnO:Ga–N). These changes demonstrated that the crystallinity degraded with increases in the dopant species.

Polycrystalline ZnO thin films exhibit strongly stimulated emissions through excitons even at room temperature, which is one of their unique optical properties [37]. Two emission signals appeared in each RT PL spectrum (Figure 6), including a sharp and intense UV emission peak centered at around 379 nm and a weak broad emission band in the visible region. The near-band-edge (NBE) UV emission is created by excitonic emission. A similar UV emission peak (380 nm) has been reported for ZnO thin films deposited on sapphire by sol-gel technique [32]. It is well known that the broad emission in the visible region is due to deep-level emission related to intrinsic defects such as Zn interstitials and O vacancies. It is worth noting that the decrease in the intensity of NBE emission after doping with N and Ga–N indicated the degeneration of the films' crystallinity and was ascribed to the increment of non-radiative recombination due to effects induces by N [22,24]. The N and Ga–N doping also led to a slight shift in the NBE emission peak as noted in the optical transmittance spectra in the near-UV spectral region. Moreover, the suppression of the visible emission may be attributed to the compensation of intrinsic defects by the acceptor dopants (spectra (ii) and (iii) in Figure 6). This feature confirmed the greater solubility and stability of the N dopant incorporated in the co-doped ZnO thin film.

**Figure 6.** Comparison of the room-temperature photoluminescence (PL) spectra of ZnO, ZnO:N, and ZnO:Ga–N thin film samples.

Various electrical parameters, including the carrier type, carrier concentration, Hall mobility, and electrical resistivity, of the ZnO-based semiconductor thin films, measured by Hall-effect measurement, are presented in Table 2. It was confirmed that the pure ZnO thin films had n-type characteristics with a mean electron concentration of 3.19 × 1015 cm−3, a mean carrier mobility of 20.77 cm2/V·s, and a mean resistivity of 94.32 Ω·cm. The measurement results also indicate that the as-prepared ZnO:N thin films exhibited a mean hole concentration of 1.83 × 1015 cm−3, a mean hole mobility of 8.85 cm2/V·s, and a mean resistivity of 385.4 Ω·cm. This change in conductivity type from n to p was related to the incorporation of N dopant into ZnO, which induced shallow acceptors of NO. However, the hole concentration and resistivity should be improved to achieve device-quality p-type oxide films. It is noted that the mean hole concentration of the p-type ZnO:Ga–N thin films approached 4.0 × 1017 cm−3; this magnitude is comparable to those in previous reports on sol-gel-derived Al–N and In–N co-doped p-type ZnO thin films [22,24]. In addition, the mobility of the co-doped sample was only about 3.0 cm2/V·s, possibly due to the formation of the Ga–2N complex; this low mobility caused carrier scattering and restricted the hole mobility. Thus, the hole mobility of the p-type ZnO semiconductor thin films for device applications should be improved in future work.

**Table 2.** Electrical characteristics of the three ZnO-based thin films and the electrical stability of p-type ZnO:N and ZnO:Ga–N thin films.

\* The symbols of n and p represent the type of major carrier concentration. (n: electron and p: hole).

Ye et al. summarized the electrical properties of several types of group III (B, Al, Ga, and In)–N co-doped p-type ZnO thin films and found that the hole concentration density, Hall mobility, and electrical resistivity were in the ranges of 3 × 1016 to 5 × 1018 cm−3, 0.1 to 15 cm2/V·s, and 0.015

to 2 × 102 Ω·cm, respectively [7]. The main considerations of the acceptor-donor co-doping method for achieving p-type conduction in ZnO thin films are to lower the ionization energy of acceptors and donors, enhance the solubility of acceptor dopants [38], and reduce the self-compensation effect to create shallower acceptor levels (falling within the range of 0.1–0.2 eV above the valence-band maximum (VBM)).

For verification of the electrical stability of the p-type oxide thin films, the electrical characteristics were measured in time frames of two to four weeks with periods of 7 days (Table 2). We found that these p-type ZnO:N thin films reverted to n-type over time. The Ga–N co-doped ZnO thin films exhibited more stable p-type conductivity at 3 weeks than did the N-doped ZnO thin films (<three weeks) due to the formation of the GaZn–NO pairs, which could offer more efficient incorporation of N dopants and activation of acceptors, and hence the creation of impurity bands above the VBM of ZnO, which increased the hole concentration. A comparison of the electrical characteristics in previous reports on solution-processed group III element (B, Al, In, and Ga)–N co-doped p-type ZnO thin films with those of the developed ZnO:Ga–N thin films in this work is provided in Table 3. It was found that the electrical properties of ZnO-based thin films fabricated by spray pyrolysis technique are better than those of oxide films prepared by sol-gel process.

**Table 3.** Comparison of the electrical characteristics of solution-processed group III elements-N co-doped p-type ZnO thin films with the developed ZnO:Ga–N thin film in this study.

The authors examined the optical and electrical properties of the three kinds of sol-gel-derived ZnO-based thin films and connected them with the structural characteristics. Besides comparing the physical properties of the oxide thin films, our experimental investigations clarified the stability of p-type ZnO:N and ZnO:Ga–N thin films. Our study indicates that the p-type ZnO:Ga–N semiconductor thin films exhibited good electrical stability, without significant degeneration of the electrical characteristics, for at least three weeks. We have successfully fabricated Ga–N co-doped ZnO transparent semiconductor thin film, which may be applicable as a stable p-type layer in the development of ZnO-based homojunction or heterojunction electronic and optoelectronic devices.

#### **4. Conclusions**

Electrically stable p-type ZnO transparent semiconductor thin films have been deposited on alkali-free glass substrates with Ga and N co-doping by the sol-gel spin-coating process. XRD patterns showed that these sol-gel-derived ZnO-based thin films were polycrystalline with a single hexagonal structure, and the microstructural features were significantly changed by the impurity doping. XPS analysis confirmed the incorporation of N and Ga–N into the two kinds of impurity-doped thin films and demonstrated that Ga and N co-doping enhanced the solubility of N dopant in ZnO thin films. The N-doped and Ga–N co-doped ZnO thin films both exhibited high transparency (~90%) in the visible spectrum. It was confirmed that the nature of conductivity of ZnO semiconductor thin films changed from n-type to p-type when the N dopant was incorporated in the ZnO nanocrystals. In the present study, we succeeded in realizing good conductivity in p-type ZnO thin film through simultaneous co-doping of Ga and N, achieving a hole concentration of 3.96 × 1017 cm−3, mobility of 3.1 cm2/V·s, and resistivity of 5.09 Ω·cm. The p-ZnO:Ga–N transparent semiconductor thin films exhibited electrical stability for almost four weeks.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2079-6412/10/11/1069/s1, Figure S1: Plane-view FE-SEM micrographs of ZnO-based thin films: (a) undoped, (b) N-doped, and (c) Ga–N co-doped samples, Figure S2: 2D SPM images of ZnO-based thin film surfaces: (a) undoped, (b) N-doped, and (c) Ga–N co-doped samples, Figure S3: Core level XPS spectrum of Ga 2*p* of the Ga–N co-doped ZnO thin film.

**Author Contributions:** Conceptualization, C.-Y.T. and W.-Y.C.; methodology, C.-Y.T. and W.-Y.C.; investigation and resources, C.-Y.T.; data curation, C.-Y.T. and W.-Y.C.; writing—original draft preparation and writing—review and editing, C.-Y.T.; project administration, C.-Y.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors gratefully acknowledge the Precision Instrument Support Center of Feng Chia University for providing XRD, FE-SEM, and SPM characterization facilities and the Instrument Center of National Chung Hsing University, Taichung, Taiwan, for help with the XPS measurement.

**Conflicts of Interest:** The authors declare that there are no known conflict of interest associated with this publication and there is no significant financial support for this study that could have influenced its outcome.

#### **References**

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**1. Introduction**

In natural systems, environmental heterogeneity occurs at varying scales in space and time affecting the diversity of species. However, processes operating at one scale not necessarily scale up or down [1]. For marine benthic communities, environmental heterogeneity at small spatial scales (cm to few meters) are mostly due to biologically produced habitat heterogeneity and biotic interactions, while at larger spatial scales (10's m to kilometers) environmental factors may prevails over biotic features [2]. Regardless of the mechanism behind environmental heterogeneity, it promotes species diversity by increasing the range of resources and reducing niche overlap, which in turn promotes species coexistence [3,4].

In the intertidal zone of rocky shores, sessile organisms such as macroalgae and sponges increase small-scale habitat heterogeneity, harboring a diverse associated fauna [5–7]. The community structure of the associated fauna is affected by several intrinsic properties of the host, such as their

physical architecture, the number of microhabitats, sediment deposition, food resources, refuge from predators, and wave damping effects [8–13]. In addition to this small-scale heterogeneity, variations in hydrosedimentary processes at larger scales (meters to kilometers) also affect the structure and diversity of hard-bottom-associated communities, mainly through their effects on larvae supply, dispersal and colonization of organisms [14,15].

Hard-bottom-associated fauna is usually composed by small crustaceans, nematodes, mollusks, annelids and other groups that use the host substratum as shelter and food. These organisms belong to two different ecological compartments, macrofauna and meiofauna, which are distinguished by their different size as well as different life-history traits. Besides being larger and more mobile, most macrobenthic species exhibit a planktonic larval stage so that larval dispersal can be extensive [16]. In contrast, meiofauna have direct benthic development, are smaller and exhibit lower mobility. Additionally, meiofauna is expected to be more specialized in their habitat and food resources [2,17–20], whereas macrofauna are relatively more generalist [21]. As a result, meiofauna, especially nematodes, are generally more affected by within-habitat variability when compared to larger-sized, more mobile macrofauna [16]. The latter, on the other hand, are more prone to be influenced by larger-scale environmental heterogeneity, for instance, in hydrodynamic conditions and position on the shore [22].

In this study, we aim to describe how community structure and diversity of hard-bottom-associated fauna are related to (i) small-scale (cm–m) changes in biological (secondary) substrate in a rocky shore and (ii) larger-scale (10's m) changes in environmental conditions of a same biological substrate in different habitats (rocky shore x mangrove roots). At both scales, we expect habitat heterogeneity to create a mosaic that will contribute to local and regional biodiversity. As such, we have tested the hypotheses that different biological substrates harbor different associated communities (H1) and that the same biological substrate growing in different habitats harbor distinct associated communities (H2). Yet, we expect the meiofauna and macrofauna communities to respond differently to habitat heterogeneity. We expect the meiofauna communities to be more affected by within-habitat environmental variability (i.e., to show differences in composition between the biological substrates) and the macrofauna to be more influenced by larger-scale changes in environmental conditions (i.e., to show differences in species composition between the different habitats).

#### **2. Material and Methods**

#### *2.1. Study Area*

This study was conducted in the Southeastern Brazil, in the Araçá Bay (São Sebastião, SP; Figure 1).  The variety of different habitats in short distances makes this as a special region to test our hypothesis regarding habitat heterogeneity from smaller to larger scales. In addition to the ecological questions, our study will help to support a discussion regarding anthropogenic impacts at the bay. Araçá Bay is under the threat of the eminent expansion of the port of São Sebastião, which will cover 75% of the bay with a structure suspended by pillars, potentially reducing the occurrence of macroalgae [24], which host diverse associated fauna [25].

**Figure 1.** Map indicating the location of the Araçá Bay. The arrow indicates the sampling site.

#### *2.2. Experimental Design*

To test the hypothesis that different biological substrates harbor different associated communities increasing local diversity (H1) we have sampled meio- and macrofauna communities from different biological substrates growing in the intertidal zone of a rocky shore from southeastern Brazil. We decided to use the natural heterogeneity present on rocky shores so the natural substrates were (1) a "turf" of different macroalgae species ('Turf') composed mainly by algae of the genus *Gelidium*, (2) the macroalgae *Caulerpa racemosa (*'*Caulerpa*') and (3) the sponge *Hymeniacidon heliophila* ('Sponge'). While the turf substratum was homogeneous, the remaining substrata were dominated by Caulerpa and Sponges, respectively, but turfing algae were still present in abundance underneath the dominant species.

To test the hypothesis that the same biological substrate growing in different habitats harbor distinct associated communities (H2) we sampled the macroalgae *Bostrychia* sp. in two sites that were 20 m apart from each other but were structurally different: a rocky shore and the pneumatophores from the mangrove *Laguncularia racemosa.* In both experiments, four replicate samples were taken for each substrate.

#### *2.3. Sampling and Sample Processing*

Four replicate samples were taken at each substrate by scraping a quadrat (10 × 10 cm) using a scraper therefore removing both the hard-bottom surface and the biological substrate. Samples from the different substrates of the rocky shore (H1) were taken within the same tidal level whereas for the comparison of the same substrate (the macroalgae *Bostrychia* sp.) between two different habitats (H2), the tidal level was slightly different. In that case, samples from the pneumatophores from the mangrove *Laguncularia racemose* were taken from an upper tidal level compared to those from the rocky shore. All samples were fixed with 10% formalin and, to better detach organisms from the substrate, were sonicated for 3 min (60 W, 3 times for 1 min with 30 s intervals) [26]. Samples were then sieved through 500 μm and 45 μm mesh sieves to retain macro and meiofauna, respectively. Samples were stored in 4% formalin and stained with Rose Bengal. Meiofauna were further extracted by flotation with Ludox TM 50 (specific density 1.18) [2], divided into four subsamples by using a Folsom plankton splitter. For each sample, one subsample was selected. Macrofauna and meiofauna were counted and identified under a stereomicroscope. Particularly for the meiofauna, 10% of the nematodes per subsample were randomly picked, evaporated slowly in anhydrous glycerol and mounted on permanent slides for identification. Nematodes were identified to the genus level and further separated into morphospecies.

#### *2.4. Statistical Analyses*

Because the sampled substrata had distinct growth forms and consequently different biomass, the abundance of both meio- and macrofauna were pondered by the sample volume, which was measured by water dislodgment in a graduated cylinder.

Univariate and multivariate analyses were conducted on log (x + 1) transformed data and presence/absence data, independently for macrofauna, meiofauna community, and for nematode assemblages, as *Nematoda* was the most abundant group in all samples. Abundance data was used to assess differences in community multivariate structure (including taxa composition and taxa abundances) whereas presence/absence data aimed at looking for differences in taxa composition only (i.e., turnover of taxa between the substrates for H1 and the habitats for H2). For each group, density, species richness and Shannon' diversity (H') were compared among substrata (H1) and between habitats (H2) using one-way analyses of variance (ANOVA). Density data was log (x + 1) transformed to equalize the importance of abundant and rare species and used to build a similarity matrix among samples. Differences on species composition were assessed using Sorensen distance for presence/absence, while community structure was assessed using Bray–Curtis distance for log (x + 1) transformed data. For both measures, differences among substrata (H1) and between habitats (H2) were tested independently using Permutational analysis of variance (PERMANOVA) [27] and visually presented by a Non-metric Multidimentional Scaling ordination (NMDS). For all analyses the species that contributed the most for the differences among groups were assessed through Similarity percentage analysis (SIMPER) [28].

#### **3. Results**

#### *3.1. Di*ff*erent Biological Substrates Harbor Di*ff*erent Associated Communities*

#### 3.1.1. Meiofauna Community and Nematode Assemblage

The meiofauna was represented by 13 higher taxa with mean densities ranging from 238 to 522 individual mL−1 (Table S1). Nematodes were the most abundant taxa, corresponding to 58% of all organisms collected, followed by harpacticoid copepods (20%). Ostracods (6%), polychaetes (6%) and amphipods (5%) were the next most abundant groups. Other groups represented less than 2% of the meiofauna.

Meiofauna abundance, the number of meiofaunal groups, diversity and community structure did not differ among substrates (Tables 1 and 2). However, analysis on presence/absence data revealed significant differences in the composition of meiofauna communities and nematode assemblages between the substrata (Table 2). The composition of meiofauna communities associated with the sponge was significantly different from both "Turf" and "*Caulerpa*" communities (post-hoc *p* < 0.05, Figure 2). This difference was mainly due to the exclusive occurrence of oligochaetes in all "sponge" samples (SIMPER analysis: 36.5% and 44.7%, contribution for differences between "Sponge" and "Turf" and "Sponge" and "*Caulerpa*", respectively).

**Table 2.** Differences in multivariate structure of macrofauna, meiofauna and nematode assemblages between substrates using PERMANOVA. Bold values indicate *p* < 0.05.

**Figure 2.** Non-metric multidimentional scaling (NMDS) ordination representing the similarity between samples from the substrates "Turf" (Δ), "Caulerpa" (-) and "Sponge" (x) for macrofauna, meiofauna and nematode assemblages.

Nematodes were represented by 56 species with densities ranging from 115 to 308 ind. mL−1 (Table S2). *Spilophorella meyerabichi* was the dominant species in all substrates (45% to 48%). *Enoplus* sp.1 (5%–9%), *Halaphanolaimus* sp.1 (7%–8%) and *Oncholaimellus* sp.1 (5%–8%) were the next most abundant species. All other nematode species occurred in low densities, each accounting for less than 4% of the assemblage. Similarly to the meiofauna, nematode abundances, species richness, diversity and community structure did not differ among substrates (Tables 1 and 2). Analysis on presence/absence

data, however, revealed significant differences in the species composition of nematode assemblages between "Caulerpa" and "Turf" assemblages (post-hoc *p* < 0.05, Figure 2). A total of 13 species contributed to 50% of the total dissimilarity between the two substrates (Table S3). Indeed, from a total of 60 species, the different substrates shared from 22 (Caulerpa vs. Turf) to 26 species (Sponge vs. Turf) only. In addition, each substrate showed a relatively high number of exclusive species ("Caulerpa": 11 species, "Sponge": 11 species and "Turf": 7 species).

#### 3.1.2. Macrofauna Community

The macrofauna was represented by 17 higher taxa with densities ranging from 56 to 303 ind. mL−1 (Table S4). The most abundant group was Gammaridae (Amphipoda), which accounted for 45% of all organisms collected, followed by Tanaidacea (20%) and Polychaeta (17%). All other taxa contributed with less than 2% each for the total macrofauna sampled. As for the meiofauna, macrofauna densities, number of taxa, diversity and community structure were similar in the three substrata (Table 1). However, differently from meiofauna and nematode assemblages, macrofauna composition as shown by presence/absence data did not differ between the substrata (Table 2, Figure 2).

## *3.2. The Same Biological Substrate Growing in Di*ff*erent Habitats*/*Environments Harbor Di*ff*erent Associated Communities*

#### 3.2.1. Meiofauna Community and Nematodes Assemblage

Meiofauna was represented by 16 taxa, from which 10 occurred in both environments (Table S5). Mangrove and rocky shore samples each had 3 exclusive taxa. *Nematoda* was the most abundant taxa, representing 73% and 62.5% of total meiofauna collected in the mangrove and rocky-shore samples, respectively. Halocaridina, Rotifera and Copepoda (adult + nauplii) were the next most abundant groups, and accounted for 6%, 9% and 8% in the mangrove and 19%, 5% and 9% in the rocky shore, respectively. Meiofauna densities did not differ between habitats (Table 3), ranging from 44 to 1278 ind. mL−1 and from 119 to 374 ind. mL−1 in the mangrove and rocky shore, respectively. The number of taxa and community structure did not differ between the two habitats either (Tables 3 and 4, Figure 3).

Nematodes were represented by 28 species with densities ranging from 15 to 982 ind. mL−1 (Table S6). Samples from the rocky shore were dominated by *Araeolaimus* sp.1 (35%), followed by *Eleutherolaimus* sp.1 (12%) and *Microlaimus* sp.1 (11%) whereas mangrove samples were dominated by *Thalassomonhystera* sp.1 (46%), followed by *Araeolaimus* sp.1 (19%). Similarly to total meiofauna, nematode densities and species richness did not differ between habitats, but diversity (H') was significantly higher in the rocky shore (Table 3). Contrasting to meiofauna community, nematode assemblages from the two environments were significantly different (Table 4, Figure 3). SIMPER analysis showed that dissimilarities between rocky shore and mangrove samples (aver. dissimilarity = 54.28%) mainly resulted from differences in abundance of a few dominant organisms (Table S7). Rocky-shore samples were characterized by higher abundances of *Eleutherolaimus* sp.1 and *Microlaimus* sp.1, whereas mangrove samples were characterized by higher abundances of *Thalassomonhystera* sp.1 and *Parachanthoncus* sp.3. Altogether these four species accounted for 50% of dissimilarities. In addition to differences in the abundance of species, analysis on presence/absence data revealed significant differences in species composition (PERMANOVA *p* = 0.023). A total of eight species contributed to 50% dissimilarities in species composition between the habitats (Table S8). From the 28 species associated with *Bostrychia* sp., only 9 species were common to both environments (Table S5). A total of 13 species were exclusive to the rocky shore whereas a total of 6 species occurred exclusively at the mangrove environment (Table S5).

**Table 3.** Differences in univariate indices from macro- and meiofauna between habitat/environment (H2) using one-way ANOVA. Analysis of macro- and meiofauna densities were done on log (x + 1) data.

**Table 4.** Differences in multivariate structure of macrofauna, meiofauna and nematode assemblages from the different environments using one-way PERMANOVA. Data were log (x + 1) transformed. Bold values indicate *p* < 0.05.

**Figure 3.** MDS ordination representing the similarity between samples from *Bostrychia* sp. from the rocky shore (-) and mangrove roots (). Data were log (x + 1) transformed.

#### 3.2.2. Macrofauna Community

Macrofauna associated with *Bostrychia* sp. was represented by 13 taxa, from which 9 were exclusive to the rocky shore and only 4, which were also present in the rocky shore, occurred in mangrove samples (Table S9). In both environments, Chironomidae and Tanaidacea were the most abundant groups. However, whereas in the rocky shore they accounted for 49.5% of the total macrofauna, in the mangrove the two groups represented 98% of all organisms collected.

Total macrofauna densities did not differ between habitats (Table 3) ranging from 5 to 7 and from 2.75 to 9.5 ind. mL−1 in the rocky-shore and mangrove samples, respectively. The average number of taxa per sample was significantly lower in the mangrove (2–4 taxonomic groups) than in the rocky-shores communities (5–9 taxonomic groups) (Table 3). Accordingly, diversity was also higher in the rocky shore (Table 3). The difference in species richness and composition resulted in distinct macrofauna communities in the two habitats (Table 4, Figure 3). This difference was mainly due to the absence of Cirripedia, Collembola and Bivalvia and the higher abundances of Chironomidae and Tanaidacea in mangrove samples (SIMPER analyses, Table S10).

#### **4. Discussion**

Habitat complexity mediates species coexistence increasing diversity in several communities [29–35]. Here, complexity generated by the biogenic structures and by the surrounding environment creates heterogeneous habitats that support a diverse associated fauna. However, the scale in which environmental changes affected the fauna depended, as expected, on the benthic component analyzed. While the small-sized meiofauna were affected by heterogeneity at both scales, the larger and more mobile macrofauna was unaffected by heterogeneity within habitat but responded to larger-scale differences in the structure of the studied habitats (rocky shore and mangroves).

The influence of biogenic small-scale habitat heterogeneity for meiofauna community structure is well documented for soft sediment environments [36–39]. Evidences for communities from hard-bottom substrates however are limited but corroborate the importance of the complexity for diversity. Meiofauna species from phytal communities are known to be host-specific [40–43] and the increase in algae morphotypes results in a diversified meiofaunal community. Biological substrates can influence the composition and structure of the associated fauna by its architecture [8], the amount of sediment accumulated on it [12,44], the protection it offers against predators [13,45], shelter from extreme physical condition [46,47] and the provision of food resources [10]. In the current study, sponge communities were differentiated by the occurrence of meiofaunal oligochaetes, which are ectocommensals of sponges [48,49]. Oligochaetes inhabiting such filter-feeding organisms benefit from being in an environment with intense water flowing, ensuring high oxygen availability and food (microorganisms and organic detritus), and a lower exposure to the variable conditions of free sediments [48]. In contrast, nematode assemblages associated with sponges did not differ from the other substrates but showed a high turnover of species between "Turf" and "Algae". The species that contributed to the differences occurred in very low densities hampering any conclusion of host association. However, the "Turf" environment creates an intricate mesh of filaments that accumulates much more sediment when compared to the "Algae" environment, probably influencing species abundances and composition. Given the different macroalgal morphologies, it is also possible that differences in irradiance and temperature between the two substrates, especially at daytime and low tide [47], might have played a role. Although the mechanism is not clear, the habitat heterogeneity created by the substrates had an important role in favoring the occurrence of rare species therefore maintaining the regional diversity.

Contrary to the meiofauna and nematode assemblages, differences in substrata identity did not affect macrofauna community therefore agreeing with our expectation, i.e., the meiofauna was more influenced by the smaller-scale heterogeneity investigated in this study than the macrofauna. These results suggest that the larger and relatively more motile macrofauna can possibly move through all studied substrates within habitats and the small-scale variations of environmental conditions offered by the different substrates did not represent contrasting selective pressures for this group. Similar observations were reported for macrofauna inhabiting different type and morphology of sponges [50]. It is possible though that any eventual small-scale variation among biological substrata within the studied zone of the rocky shore is species-specific, so our analyses conducted at lower taxonomic resolution may have prevented us to observe species-specific differences among communities. However, in a region close to our study site, the structure of amphipod assemblages analyzed to the specific level was not affected by algae identity and architecture, but only by its position through the intertidal zone [51]. Even hosts with very distinct architecture, when occurring in the same position of the shore, supported similar associated communities [51]. These findings suggest that factors controlling community assembly of macrofaunal organisms may operate on a wider scale.

Accordingly, the primary substrate and/or the surrounding environment where this occurred influenced both the nematode assemblages and macrofauna. Fauna associated to the same substratum, but under different environmental conditions, are often distinct [52] because hosts do not necessary buffer the distinct selective pressures to which associated fauna is exposed. Mangrove samples were characterized by higher abundances of *Thalassomonhystera* sp.1, which is a genus frequently found in mangrove sediments and mangrove litter (e.g., [53,54], probably as a consequence of both the higher proximity to the sediment and the higher sedimentation in the *Bostrichetum* communities from mangrove roots. In addition, there was a significant overlap in species composition between the two habitats and the number of shared species was relatively low (32%) suggesting that the surrounding environment was highly selective. For the macrofauna, the longer exposure to air to which associated fauna is exposed in mangroves restricted the occurrence of marine groups as Cirripedia, Collembola and Bivalvia reducing diversity, but facilitated the occurrence of typically terrestrial organisms such as Chironomidae larvae. While we did not address the mechanism behind the absence of typically marine species in mangrove habitats, the longer exposure to air can restrict the access of reproductive propagules, but also increase post-recruitment mortality, which probably caused the differences observed here. Differences in taxonomic composition of the associate fauna from the same secondary substrate in different habitats were also observed for fauna associated with mussel beds growing on soft vs. hard substrates [52]. While it has been suggested that changes in wave exposure and the proportion of faeces and pseudo-faeces of mussels deposited within the mussels bed would explain such differences [52]; here we suggest that changes in environmental conditions as distance to the soft-bottom, the amount of sediment deposited over the secondary substrate and aerial exposure would influence the differences in the associated fauna between macroalgae on rocky shores vs. mangrove environments.

#### **5. Conclusions**

Changes in species composition between environments and substrates highlight the importance of habitat diversity at different scales in maintaining the diversity of the associated fauna. In addition, whereas both substrate identity and the surrounding environment were important in structuring the smaller-sized meiofauna, the macrofauna was more influenced by larger-scale changes in the surrounding ecosystem. This implies that the larger and more motile macrofauna explores the environment in a larger spatial scale compared to the meiofauna, which are smaller and more sedentary (therefore restricted to a smaller space), suggesting that effects of spatial heterogeneity on communities are dependent on organism size and mobility. Besides, species turnover occurred mainly regarding species with low abundances (i.e., rare species). These results stress the importance of considering the rarer groups in ecological studies and/or impact assessments on local and regional diversity. It is important to note, however, that because our study is limited to a single sampling location, the generalizations of our findings must be further tested in other habitat types and regions. Also, because of the frequency of occurrence and abundance of rare species might change as a function of sampling effort, different rarity patterns could emerge if more samples were taken.

Considering the eminent expansion of the harbor over rocky shores and mangroves from the Araçá Bay and the effects of artificial substrate construction and shading, leading to large modifications on biological substrates on rocky shores [24,55], the anthropogenic impact in the area has the potential to promote large changes to both meiofauna and macrofauna biodiversity in the area. Because both macro- and meiofauna biodiversity are positively related to important ecosystem processes [56,57], such effects can potentially go beyond structural changes in biodiversity and are likely to translate into major impacts to ecosystem functioning.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1424-2818/12/1/39/s1, Table S1: Mean density (inds.ml−1) and standard deviation (in parenthesis) of meiofauna higher taxa in the different biological substrates sampled in a rocky-shore from Araçá Bay, Southeast Brazil; Table S2: Mean density (inds. mL−1) and standard deviation (in parenthesis) of nematode morphospecies in the different biological substrates sampled in a rocky shore from Araçá Bay, Southeast Brazil; Table S3: Mean density (inds. mL−1) and standard deviation (in parenthesis) of macrofauna higher taxa in the different biological substrates sampled in a rocky shore from Araçá Bay, Southeast Brazil; Table S4: Mean density (inds. mL−1) and standard deviation (in parenthesis) of meiofauna higher taxa associated with *Bostrychia* sp. sampled from the rocky shore habitats and mangrove roots at Araçá Bay, Southeast Brazil; Table S5: Mean density (inds. mL−1) and standard deviation (in parenthesis) of nematode species associated with *Bostrychia* sp. sampled from the rocky shore habitats and mangrove roots at Araçá Bay, Southeast Brazil; Table S6: Mean density (inds. mL−1) and standard deviation (in parenthesis) of macrofauna taxa associated with *Bostrychia* sp. sampled from the rocky shore habitats and mangrove roots at Araçá Bay, Southeast Brazil; Table S7: SIMPER analysis showing nematode species ranked according to average Sorensen dissimilarity from presence/absence data between the biological substrates 'Caulerpa' and 'Turf'; Table S8: SIMPER analysis showing nematode species ranked according to average Bray–Curtis dissimilarity from log (x + 1) data between the rocky shore habitats and mangrove roots; Table S9: SIMPER analysis showing nematode species ranked according to average Bray–Curtis dissimilarity from log (x + 1) data between the rocky shore habitats and mangrove roots; Table S10: SIMPER analysis showing macrofauna groups ranked according to average Bray–Curtis dissimilarity from log (x + 1) data between the rocky shore habitats and mangrove roots.

**Author Contributions:** All authors have equally contributed to the conceptualization of the idea and the methods used. Data acquisition (sampling and samples processing) were coordinated by G.M.D., R.A.C., G.F. and F.G. had conducted statistical analysis and data interpretation was done by all authors. F.G. prepared the original draft with considerable contributions from G.M.D. All authors have substantially contributed to the writing of the final text. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by research funds granted by São Paulo Research Foundation (FAPESP) to Biota Araçá Project (#2011/50317-5). We thank Felipe Dutra and Viviane Gomes for support with field and laboratory work. We are very grateful to Antônia Cecília Zacagnini Amaral, who acquired funding and wisely administrated the Biota Araçá Project, for this opportunity.

**Conflicts of Interest:** The authors declare no conflicts of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.