Rangifer colonization and propose that the High Arctic archipelagos of Svalbard, Franz Josef Land, and Novaia Zemlia were colonized by reindeer from the Eurasian mainland after the last glacial maximum. Comparing mtDNA control region sequences from the three Arctic archipelagos showed a strong genetic connection between the populations, supporting a common origin in the past. A genetic connection between the three archipelagos and two Russian mainland populations was also found, suggesting colonization of the Eurasian high Arctic archipelagos from the Eurasian mainland. The age of the Franz Josef Land material (>2000 years before present) implies that Arctic indigenous reindeer colonized the Eurasian Arctic archipelagos through natural dispersal, before humans approached this region.In light of current debates on global climate change it has become important to know more on how large, roaming species have responded to environmental change in the past. Using the highly variable mitochondrial control region, we revisit theories of  Climatic oscillations over the Quaternary (2.4 million years ago–present) have had a major impact on the geographic distribution and genetic structure of species through population extinctions and range shifts . The demRangifer tarandus) is a keystone species in the circumpolar North, not only ecologically through the way they impact upon the plant cover [Rangifer was distributed south of the ice sheet in both Eurasia and in North America, and in the Beringia refugium encompassing the Bering land bridge, Alaska, as well as large parts of Siberia [Rangifer exhibit distinct morphological adaptations to different environments, and populations have been subdivided into various ecotypes according to their life-history strategies and ecological conditions such as the woodland or boreal forest or sedentary form, the barren-ground or tundra or migratory form, the mountain form, and the Arctic form [R.t platyrhynchus), distributed on the Svalbard archipelago, the North American Peary caribou (R.t pearyi) primarily distributed on the Canadian Arctic archipelagos, and the recently extinct R.t eogroenlandicus, formerly distributed on Eastern parts of Greenland [R.t. pearsoni) with a view to restoration [Reindeer and caribou  [Rangifer tarandus) [Alopex lagopus) [Gulo gulo) [R.t eogroenlandicus have been shown to comprise of mtDNA haplotypes signalling a common origin in an ancient Beringian and/or Eurasian pre-glacial population [R.t eogroenlandicus shared certain mtDNA haplotypes, morphological similarities, as well as have been observed to migrate from Ellesmere Island to North Greenland, provides convincing evidence for a North American colonization route for these two subspecies [There has been a wide-ranging debate on the colonization routes and dispersal of Arctic reindeer inhabiting the islands in the western Eurasian and North American Arctic , 18, 19.andicus) , reindeelagopus)  and the lo gulo) . Svalbarpulation . The facSvalbard reindeer are characterized by low genetic variability indicating isolation, possible bottlenecks, and subsequent genetic drift as important population processes , 25. TheThe idea that the wild reindeer on some of the Arctic archipelagos might have been genetically linked to the Eurasian mainland was first raised by Gravlund et al.  who founth century onwards [th century were recorded as living without domestic reindeer [A third account of the colonization of some of the High Arctic islands focusses on human-instigated translocations. Resolving the question of possible human induced translocation is important for the issue of introgression from domestic herds, a key parameter for classifying protection status of populations. There is a relatively broad Russian language literature documenting the human interest in wild reindeer, and the movement of reindeer herds between islands in the Eastern Barents Sea in historic times , 37, 38. onwards . There i onwards , althougreindeer . There areindeer , 37, 38.reindeer . HoweverRangifer populations in both North America and Eurasia, there has been a conspicuous lack of studies comparing the reindeer populations on each of these three neighbouring archipelagos to each other–the clearest approach to discussing possible common colonization routes. A genetic link between Svalbard, Novaia Zemlia and Franz Josef Land would tell us if these archipelagic populations have a common origin and also help to answer the question of whether or not the current distribution of the Arctic type is caused by natural dispersal or recent human induced dispersal of domestic reindeer. We also included sequence data from reindeer on the nearest population of domestic reindeer situated on Kolguev Island to test for introgression from domestic reindeer. Two wild mainland- and one wild island population from Russia were included to help answer the question of a possible biographical link between the Arctic archipelagos and the Eurasian mainland.Here, we use the highly variable CR to compare sequence data from contemporary reindeer populations on Svalbard and Novaia Zemlia, with ancient samples from the now extinct population on the Franz Josef Land archipelago. Despite the work done comparing Svalbard reindeer to the mainland 14C dated  and the headwaters of the Pechora River in the Pechro-Ilychskii Nature Reserve, Komi Republic (n = 10), were downloaded from GenBank and included in the analyses in order to test for possible gene flow between the mainland and the archipelagos , Novaia Zemlia (n = 20) and Franz Josef Land (n = 15.) The Svalbard population was sampled at Nordenskiöld Land on Spitsbergen and on Nordaustlandet. Wild reindeer samples from Novaia Zemlia were collected on the South Island . Archaic4C dated . Skin- aipelagos . Additioipelagos . The PezBlood and muscle samples from Svalbard were collected as part of the Man and the Biosphere (MAB) project which started in 1978. The Svalbard MAB field project was coordinated by the Norwegian Polar Institute . Hunting permits of reindeer within the Svalbard MAB field project was approved by the Governor of Svalbard. Sampling of muscle-, skin- and velvet samples from Novaia Zemlia, Belyi Island and Kolguev required no specific permits and was done under an ethics review for the ERC Arctic Domus and performed under the ERC Arctic Domus ethics annex.Muscle and skin samples of wild reindeer from Novaia Zemlia and Belyi Island were collected from dead animals via subsistence hunting. The sampling was conducted by the authorized managers of these populations, according to the regulations stated by the Ministry of Nature Protection of the Russian Federation. Velvet samples from domestic reindeer on Kolguev Island was collected from dead animals during industrial slaughter. No animals were sacrificed for this study and the field work did not involve endangered or protected species.Tissue samples were stored in ethanol (≥ 80%) or kept frozen until analysed, blood samples were stored in EDTA. DNA extraction of muscle-, velvet- and skin samples was performed using DNeasy Blood & Tissue Kit (Qiagen) following the manufactures protocol. DNA extraction from EDTA blood was carried out using DNeasy Blood & Tissue Kit (Qiagen) or by using a boiling method for DNA extraction (SI).°C for 2 min, 95°C for 30 sec, 55°C for 30 sec and 72 oC for 1 min (step 2–4 cycled 30 times) and finally 72 oC for 10 min. PCR reactions were performed in 20 μl total volume using 1–2 μl DNA template, and with the following final concentrations; 1X buffer, 1.5 mM MgCl2, 0.8 mM dNTPs, 5 pmol of each primer, 0.5 μg/μl Bovine Serum Albumin (BSA), 0.5 U/μl AmpliTaq DNA polymerase (Applied Biosystems), and dH2O to make up the remaining volume.A 503 base pair (bp) long fragment from the mitochondrial control region was amplified using the forward primer RtCRF (5`-AAT AGC CCC ACT ATG AGC ACCC-3`)  and the The samples were cleaned for unincorporated primers and nucleotides using Illustra ExoProStar  diluted 10 times. Cycle sequencing was performed in a 10 μl reaction volume, using BigDye v3.1 sequencing kit (Applied Biosystems) following manufacturer`s recommendations. Purification was carried out using standard EDTA/EtOH precipitation. Capillary electrophoresis and data analysis were performed with an ABI 3130xL- or 3500xL instrument (Applied Biosystems). All sequences were sequenced in both directions and the consensus sequences were aligned by ClustalW  and editDNA was extracted from antler powder using DNeasy Blood & Tissue kit (Qiagen) following Bjørnstad and Røed . StandarFrom the ancient material a 266 bp fragment of the mtDNA control region was amplified using the primer pair 259F/524R (5’–TGCCCCATGCTTATAAGCAAG–3’/ 5’–GTGAGATGGCCCTGAAGAAA–’3), or by amplifying two overlapping amplicons of respectively 140 bp with primers 259F and 398R (5’- CCTTTCTTGTCAACATGCGTA– 3’) and 178 bp with primers 347 F (5’–TGCCCCATGCTTATAAGCAAG–3’) and 524R. PCR amplification and sequencing were performed as in Bjørnstad and Røed . The seq14C dated 4 antler samples from Franz Josef Land which also amplified successful DNA  were calculated in DnaSP  for the A total of 137 samples from seven populations were analyzed for the mitochondrial control region, including 122 contemporary- and 15 ancient samples. The 400 bp long alignment, comprising the contemporary populations, varied from low to relatively high levels of genetic diversity with a total of 30 haplotypes, overall haplotype diversity (Hd) = 0.910, and nucleotide diversity (π) = 0.019 . The SvaIc, Id, Ie and cluster II [Ic showing an intermediate level of support (posterior probability = 71). In the present study, we found sub-cluster Ic to comprise the 3 haplotypes previously found in Svalbard reindeer (n = 27), but also to include haplotypes found on Novaia Zemlia (n = 12), the Pechora River (n = 3), the Peza River (n = 1) and Franz Josef Land (n = 13) , Belyi Island (n = 1), Peza River (n = 2), Pechora River (n = 1), Novaia Zemlia (n = 2) and Franz Josef Land (n = 1) , Novaia Zemlia (n = 6) and on Belyi Island (n = 6)  Figs  and 2. Hm Taimyr . In the and Figs  and 2. Ireindeer . However 1) Figs  and 2. Freindeer . We foun 6) Figs  and 2. Ondinavia , 69.Ic appear to have experienced a recent population expansion as the mismatch distribution analysis showed no significant deviation from the expected distribution under the sudden expansion model (see 2 were highly significant with Fs = 5.948 (p = 0.000) and R2 = 0.103 (p = 0.002) adding support for a demographic expansion for this sub-cluster. Tajima`s D was negative (-1.018), but not significant (p = 0.173). Date since expansion for sub-cluster Ic was calculated to 5862 years before present (YBP) (95% CI: 535–10375) based on the mean number of pairwise differences, τ (τ = 2.762).Sub-cluster odel see . An expaIe, 13 in sub-cluster Ic, and one sample with a haplotype unique for Franz Josef Land  and 24 haplotypes were identified. However, low levels of genetic variation was found in the ancient Franz Josef Land population  . Three hand Figs  and 2.Ic haplotypes in the two mainland populations could be the result of random haplotype survival from a common refugial population. More extensive sampling of Russian wild reindeer, with a special emphasis on islands in northern Siberia, would be necessary to clarify this further.Our results show that the most common haplotype found on Svalbard is also the most common haplotype found among the contemporary wild population on the South Islands of Novaia Zemlia and the extinct population on Franz Josef Land–suggesting that the population history of wild reindeer on these High Arctic islands was tightly linked. The genetic similarity between these archipelagic reindeer populations indicate gene flow and dispersal from a common source population, and supports the theory that all of these islands were likely colonized from the Eurasian mainland. The hypothesis of an eastern colonization of the Arctic archipelagoes is further supported by a genetic connection between the three archipelagic populations and one individual from Pechora River (Komi Republic), who shared the most common haplotype found on Svalbard, Novaia Zemlia and Franz Josef Land. Also, three similar haplotypes were observed in single samples from wild reindeer in both Pechora river basin and in the Peza river basin. However, the presence of Published radiocarbon dates of archaic bones collected on Franz Josef Land suggest that wild reindeer populated the archipelago 6400–1300 YBP . These rIe). This finding can be explained by the recent translocation of a small number of domestic reindeer from Kolguev to Novaia Zemlia, between 1928 and 1931 [We did find one individual on Novaia Zemlia with a haplotype also found in reindeer on Kolguev Island (sub-cluster and 1931 . The weaRangifer [Lemmus) questioning the centrality of the traditional Beringian refuge for the post-glacial re-colonization of the Arctic. They demonstrate how four different mtDNA linages of the circumpolar lemming (Lemmus) indicate separation by glacial barriers, followed by post glacial colonization from refugia other than Beringia. Shaefer et al. [Ic as recently as 5000–6000 YBP. This implies that the unique genetic composition of these Arctic reindeer populations may have resulted from bottlenecking, isolation, and then subsequent expansion in the High Arctic well after the retreat of the ice, rather than isolation in alternative refugia during the last glacial maximum (LGM). Further studies of the genetic structure in Russian reindeer populations would help to answer this problem.Our results indicate an eastern colonization route of the Eurasian arctic archipelagos. The Bering land refuge has traditionally been most widely discussed as a single origin point for various continental distributions of Rangifer , 75 as wRangifer . HoweverRangifer  performer et al.  in theirr et al.  in theirPicea) and birch (Betula) tree lines in northern Eurasia were located at least 150 km further North from their present location, and in the case of birch, may have reached the seacoast [Post-glacial range shifts and the expansion of reindeer populations, as well as those of other cold-adapted species, is probably connected to the major environmental changes taking place in northern Eurasia during the Holocene . Sea surseacoast , 79. Theseacoast . As mentseacoast , suggestRangifer are in many ways a classic circumpolar species providing an important anchor to the environmental history of the High Arctic, and also to the lives of local people. Contemporary climate change would be expected to alter the distribution and demography of Rangifer today as has been the case in the past. Mammals living in the High Arctic have limited opportunities to migrate further north. To survive, they will have to depend on their ability to adapt where they are. Therefore they are under particular risk. This necessitates having proper management plans with an emphasis on conserving genetic variability for indigenous archipelagic reindeer.This study has established certain strong genetic similarities found between wild reindeer populations on Svalbard, Novaia Zemlia and Franz Josef Land implying that the maternal genetic structure of these archipelagic reindeer populations is indigenous and unique. The study lends considerable weight to the hypothesis that these islands may have colonized from the Eurasian mainland via an eastern route. Moreover the study strongly disproves the suggestion that populations for example on Novaia Zemlia are feral populations of introduced domestic reindeer. It is our hope that these important results will help clarify existing conservation plans for wild reindeer on Novaia Zemlia in the Russian Arctic Strict Nature Reserve and its aim at conserving this important and unique population.S1 FigIc, Id, Ie and II .The Bayesian phylogeny shows 30 control region haplotypes and support for sub-cluster (TIF)Click here for additional data file.S2 FigIc. The mismatch analyses show a unimodal distribution, which is characteristic for a recently expanded population [The observed pairwise difference (blue bars) and the expected mismatch distribution (red bars) under the sudden expansion model among individuals in sub-cluster pulation .(TIF)Click here for additional data file.S1 Table14C dates were calibrated using CALIB 6.1.1 [The IB 6.1.1 , based oIB 6.1.1  with 2σ (PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Microelectromechanical systems (MEMS) resonators have allowed the development of magnetic field sensors with potential applications such as biomedicine, automotive industry, navigation systems, space satellites, telecommunications and non-destructive testing. We present a review of recent magnetic field sensors based on MEMS resonators, which operate with Lorentz force. These sensors have a compact structure, wide measurement range, low energy consumption, high sensitivity and suitable performance. The design methodology, simulation tools, damping sources, sensing techniques and future applications of magnetic field sensors are discussed. The design process is fundamental in achieving correct selection of the operation principle, sensing technique, materials, fabrication process and readout systems of the sensors. In addition, the description of the main sensing systems and challenges of the MEMS sensors are discussed. To develop the best devices, researches of their mechanical reliability, vacuum packaging, design optimization and temperature compensation circuits are needed. Future applications will require multifunctional sensors for monitoring several physical parameters . Microelectromechanical systems (MEMS) allow the development of devices that are composed by mechanical and electrical components with a feature size in the micrometer-scale. MEMS devices include signal acquisition and processing, actuators and control mechanisms . These dMEMS magnetic field sensors use a bias source and a signal processing system. For instance, Dominguez-Nicolás et al.  designedDesign stage is very important in determining the suitable structural configuration, materials, operation principle and sensing technique of MEMS magnetic field sensors. In this stage, MEMS designers can use analytical and numerical methods to predict the performance of MEMS sensors. Designers must consider the rules of the fabrication process to avoid mistakes that affect the performance of the sensors. To obtain reliable designs, designers must have a high level of fabrication and packaging knowledge. In the design stage, simulation and modeling tools are necessary to predict the behavior of MEMS sensors. These tools assist designers in selecting the best fabrication process and materials for the sensors. To develop commercialized MEMS sensors, designers must satisfy the following criteria: (1) optimal design; (2) packaging design; (3) reliable materials properties and standard fabrication process; (4) suitable design and simulation tools; (5) reduction of electronic noise and parasitic capacitances; (6) reliable signal processing systems and; (7) reliability testing.TM [TM [TM [The selection of the sensors fabrication process must consider several factors such as materials, operation principle, dimensions, operating environmental conditions, signal conditioning processes and sensing techniques . The desTM , coventoTM [TM , Intelli [TM [TM  and Sand [TM [TM . These d [TM [TM .(1)Application specification. In this stage, designers must know all the technical information of the sensor application such as magnetic field range, environmental conditions, response time and resolution.(2)Conceptual design. This stage includes several conceptual designs of magnetic field sensors to satisfy the requirements of the applications. Next, these designs are evaluated to select the best conceptual design.(3)Detailed design. Analytical and numerical methods are used to predict the sensor’s performance. These designs must incorporate the sensor packaging, which depends of different factors such as materials, operation pressure, cost, sensing technique and environmental conditions. High temperatures can be generated during the sensor packaging, affecting its behavior. For this, element finite method (FEM) models can be used to estimate the packaging temperature effect on the sensor behavior.(4)Fabrication process. Designers identify the sensor requirements and characteristics of potential fabrication processes. Next, the designer selects the best fabrication process or develops a new process, in which must consider the materials, etching steps, design rules, technical limitations and cost.(5)Material properties verification. Test structures can be fabricated on the same sensor wafer to measure several materials properties of the sensors such as Young’s modulus, facture strength, thermal conductivity, electrical resistivity and dielectric constant.Designers could employ the following methodology to design and fabricate MEMS magnetic field sensors:Generally, the fabrication of MEMS magnetic field sensors can be realized using bulk or surface micromachining processes. These processes use silicon as their main material due to its important mechanical and electrical properties. For instance, silicon has minimum mechanical hysteresis and large rupture stress close to 1 GPa. In addition, silicon doped with phosphorus or boron can improve its electrical properties. The bulk micromachining process employs wet and dry etching techniques to fabricate features in materials through isotropic and anisotropic etching, respectively . Wet etcSurface micromachining process is a fabrication process of MEMS devices that uses the deposition, patterning and etching of different materials layers on a substrate . CommonlPlanar fabrication methods of the microelectronic industry can be used in surface micromachining process. For instance, this process employs tools of the microelectronic industry for depositing the structural and sacrificial layers, as well as the patterning and etching of layers. Thus, devices structures are obtained through etching of the structural layers, which are anchored to the substrate and others structural layers. In the deposition process of structural and sacrificial layers, residual stress gradients can be generated on the structural layers, which can affect the performance of the MEMS sensors. These stress gradients are caused by deposition conditions such as high temperature values and materials layers with different thermal dilation coefficients. The residual stress gradients can be decreased using post-deposition annealing steps.LF) that is generated by the interaction of the electrical current with an external magnetic field (xB) parallel to the sensor structure:zL is width of the rectangular loop, ω and RMSI are circular frequency and root-mean-square (RMS) of the sinusoidal electrical current (AlI) supplied to the aluminum loop, respectively.MEMS magnetic field sensors use resonators to increase their sensitivity due to large strains of the sensor structure. The sensor structure operates at resonance through Lorentz forces or electrostatic forces whose displacements are related to the magnitude of the applied magnetic field. These displacements can be detected using piezoresistive, capacitive or optical sensing techniques. For instance, iR) of the initial resistance (iR) of two p-type piezoresistors:lπ is longitudinal piezoresistive coefficient, lε is longitudinal strain of the piezoresistors under static load, E is Young’s modulus of the piezoresistor material and TQ is total quality factor of the resonant structure.The sensor structure has a deflection and strain generated by the Lorentz force, causing a change (△oV) of the Wheatstone bridge changes due to variation of the piezoresistors resistance:iV is input voltage applied to the Wheatstone bridge.The output voltage (S) is obtained as the ratio between the output voltage (ΔoV) of the Wheatstone bridge to the magnetic field shift (ΔxB):The sensor sensitivity (sE) to the energy lost per cycle (CE) caused by the damping sources:The quality factor of a resonator affects its sensitivity and resolution. This factor can be determined as the ratio of the total energy stored in resonator (ζ):For small displacements of the resonator, the quality factor is related with the damping ratio  of a resonator can be determined considering different damping sources.fQ, sQ, and tQ are quality factors associated with the fluid damping, support damping and thermoelastic damping, respectively.Total quality factor  supported by four bending beams (130 × 12 × 15 μm3). The plate oscillates due to the interaction between the sinusoidal electrical current and magnetic field parallel to the plate. This oscillation generates a bending motion (136.52 kHz) of the beams, which contain two piezoresistors (40 × 8 × 1 μm3). It alters the resistance of two piezoresistors, modifying the output voltage of the Wheatstone bridge. Thus, the sensor has an electrical output response for monitoring magnetic fields at atmospheric pressure. This sensor has a quality factor of 842 at atmospheric pressure, sensitivity of 403 mV·T−1, theoretical resolution of 143 nT·Hz−1/2, theoretical noise of 57.5 nV·Hz−1/2 and power consumption close to 10 mW. These technical parameters of the MEMS sensor are adequate for monitoring residual magnetic fields into ferromagnetic tubes. However, this sensor registered a voltage offset and a non-lineal electrical response.−1 in air, a resonant frequency of 2.6 MHz and a quality factor of 16,900. Nevertheless, the MEMS sensor requires more studies of the effects of temperature on its performance.Mehdizadeh et al.  designed3), four flexural beams (18 × 15 × 15 μm3), two support beams (60 × 36 × 15 μm3) and a Wheatstone bridge with four p-type piezoresistors. The device exploits the Lorentz force and operates at its seesaw resonant frequency (100.7 kHz) without vacuum packaging. A standard bulk micromachining process and SOI wafers are used to fabricate the device, as shown in −1, a resolution of 2.5 μT and a power consumption of 12 mW. Due to the advantages of this sensor, it could be employed in applications of non-destructive magnetic testing to detect flaws and corrosion of ferromagnetic materials. For this application, the sensor needs reliability studies of its behavior under different conditions of temperature, moisture, and fatigue.A MEMS magnetic field device see  with a sz-axis along) to surface of the resonant structure. It consists of a set of fixed stators and a shuttle suspended with two thin beams , although this sensor only detects the components of the magnetic field in one direction and its performance depends on the temperature. This sensor could include a temperature device, embedded on the same MEMS readout electronic, for post-acquisition compensation of temperature alterations. Therefore, this sensor requires initial calibration and temperature compensation.eams see , which ftors see . The sen2) clamped to two doubly fixed crossbars and two sets of comb drive electrodes joined to each parallel beam. The first set of comb drive electrodes operates as actuation mechanism and the second set of electrodes acts as a sensing mechanism. The DEFT resonator is driven using an electrostatic force that activates its in-plane (46.8 kHz) and anti-phase (49.3 kHz) vibration modes , which is then coupled to current-carrying silicon beams through a microleverage mechanism. This resonator uses electrostatic actuation and capacitive sensing through 30 comb fingers on each side of the flexural beam, as shown in −1/2, a quality factor of 540 and a resonant frequency of 21.9 kHz. The sensor’s sensitivity improves when the quality factor increases. With this, the sensor could be used for compass applications. This sensor uses a large silicon beam (500 × 40 × 3 μm3) subject to flexural loads, which needs mechanical reliability testing. Li et al.  designed−1/2. This sensor can be used for non-destructive magnetic testing of ferromagnetic materials. In addition, with an improvement in the vacuum pressure (about 10 Pa), this sensor could be employed as a compass in electronic devices and gadgets. For a constant magnetic field, the device registered a non-linear displacement variation when the input current magnitude overcomes the 400 μA.Aditi and Gopal  fabricat2) of the CCR are perpendicular to one another. An alteration of the magnetic field produces a rotation of the magnet and flexible mirror, which is proportional to the magnetic field. Then, a diode laser is used to interrogate the CCR, splitting in two beams the reflected optical beam. The displacements of these optical beams are measured through a photodetector array. This sensor does not require power consumption and it can continuously operate in extremely harsh environments such as industrial, aerospace and the automotive sector. This sensor can detect a magnetic field from 1030 μT to 7540 μT with an uncertainty of 113 μT at 1-m optical-interrogation range. To improve the sensor’s performance the mirror curvature could be decreased and the mirror’s size and magnet volume increased, as well as attaching the mirrors directly to the magnet. This sensor has a potential application in wireless sensing systems that operate in harsh or hostile locations that do not need to provide sensor-node energy. However, more studies on the effects of etch holes on the sensor sensitivity should be performed.Vasquez and Judy  designed3) coated with an aluminum layer (2500 × 2500 × 0.8 μm3). The membrane is supported by two torsional springs (2100 × 100 × 12 μm3), in which an aluminum wire (30 μm width and 0.8 μm thickness) is deposited, as shown in −1, a resonant frequency of 364 Hz, a quality factor of 116, a resolution of 0.4 nT with a bandwidth of 53 mHz and noise floor level of 1.78 nT·Hz−1/2. This sensor can detect a magnetic field between a range of nanoTeslas and Teslas. Nevertheless, the variation of the sensor behavior due to heating of the membrane and springs should be studied.Park et al.  designedMagnetic field sensors based on MEMS resonators have potential applications such as telecommunications, military, industrial, automotive, biomedical and consumer electronic products. This is due to the important advantages of MEMS sensors, which include small size, lightweight, low power consumer, wide dynamic range, compact signal conditioning and high sensitivity. These sensors could detect flaws or residual stresses of ferromagnetic materials using non-destructive testing such as Eddy current inspection and the magnetic memory method (MMM) ,36,37. EDomínguez-Nicolas et al.  develope2, vacuum packaging of 0.35 mbar, resonant frequency of 19.95 kHz, quality factor of 2500, a sensitivity of 0.85 V·mT−1 and a detection limit of 120 nT·mA·Hz−1/2.Other future applications of MEMS sensors include the micro-, nano- and pico-satellites. Miniaturization of satellites allows for the reduction of their launch costs, which can be achieved using sensors of small size, reliable, low energy consumption and high sensitivity. For instance, satellites must measure small magnetic fields during their space missions. For this application, Lamy et al.  and Ranvt1 and t2) using the MEMS devices. Thus, vehicle speed will be determined using the ratio of the devices separation distance to the time difference t1 and t2. The vehicle size will be related with the magnitude change of the Earth’s magnetic field.Traffic detection systems could measure the speed and size of vehicles considering MEMS devices, which are located in parallel alongside the road . These dMEMS resonators used in Lorentz force-based magnetic field sensors offer several advantages with respect to conventional sensors, allowing for their use in future applications. The Lorentz force-based sensors provide a wide measurement range by changing the magnitude of the excitation current. In addition, vacuum packaging reduces the air damping, increasing the quality factor and sensitivity of the MEMS resonators. These sensors need a low energy consumption and have a compact structure, which can be fabricated through a standard micromachining process. Also, MEMS sensors can use different sensing techniques  to detect magnetic field. Each technique presents specific benefits that are employed for the designers to develop the best sensor for a specific application. For example, piezoresistive sensing is adequate for the bulk micromachining process and simple signal processing systems. With these systems, an electrical output signal related with the magnetic field is obtained. The piezoresistive sensing registers a voltage offset and requires temperature compensation circuits. On the other hand, capacitive sensing is mostly used in the superficial micromachining process and it converts the applied magnetic field into an electrical output signal. This technique has little temperature dependence and allows the fabrication of electronic circuits on the same chip of the magnetic field sensor. It permits the reduction of the device size and parasitic capacitances. Generally, the sensors with capacitive sensing have high air damping at atmospheric pressure; therefore they need vacuum packaging to increase their sensitivity. Finally, optical sensing has immunity to electromagnetic interference (EMI) and demands less electronic circuitry than both capacitive and piezoresistive sensing. Sensors with optical sensing can operate in hostile or harsh environments without providing energy for the sensor. The superficial and bulk micromachining processes are suitable for this sensing technique. Nevertheless, all these sensing techniques have problems with the heating of the sensor structure due to the Joule effect, generating thermal stress and displacements of the resonators. More studies about the modifications in the behavior of the MEMS resonators due to the Joule effect must be performed. In addition, the mechanical reliability of the resonators must be studied to ensure the best performance of the MEMS sensors.−1·mA−1, and packaging pressures of about 0.75 mbar. Thus, it can reach large full-scale ranges up to ±2.4 mT, adjusting the driving current, which exceeds the full-scale of conventional devices such as the Hall effect and anisotropic magneto-resistance (AMR) [ce (AMR) .Reliability studies are required to know the devices behavior under different environments and operation conditions. In addition, studies of the main damping sources of resonators must be considered in the design phase of devices ,50. DesiMagnetic field sensors formed by MEMS resonators have important advantages in respect to conventional devices, allowing for their implementation in future applications. Most of these sensors use silicon resonators that exploit the Lorentz force and different sensing techniques such as piezoresistive, optical, or capacitive. The piezoresistive sensing is a simple technique with an easy fabrication process; although it can have voltage offset and temperature dependence. Capacitive sensing has little temperature dependence but suffers from parasitic capacitances, which can be decreased with monolithic fabrication. Optical readout offers immunity to electromagnetic interference (EMI) and decreases the electronic components. The main fabrication processes include surface and bulk micromachining, considering materials such as silicon, polysilicon, silicon dioxide, aluminum and gold. However, MEMS devices need more reliability research in order to predict their performance under different environments and operation conditions. Future works must consider the reduction of damping and electronic noise, as well as the integration of different sensors on a single chip for monitoring several physical parameters.


Significant altitudinal changes were detected in microbial abundances as well as in diversity and composition of taxonomic and functional communities as a consequence of the differences in pH, soil organic matter (SOM) and nutrient contents and soil temperatures measured between both sites. A network analysis revealed that deciduous forest site  presented a lower resistance to environmental changes than that of coniferous forest site . Significant seasonal effects were detected only for the diversity  and composition of microbial functional gene community, which was related to the non-significant increased SOM and nutrient contents detected in autumn respect to spring and the presumable high capacity of soil microbial communities to respond in functional terms to discreet environmental changes.The study of soil microbial responses to environmental changes is useful to improve simulation models and mitigation strategies for climate change. We here investigated two Alpine forest sites (deciduous forest  For example, C fixed by primary producers in forests exceeds C loss by respiration by 7–25%2 and the current C stocks in the world’s forests have been estimated to be 861 ± 66 Pg C3. Nonetheless, Earth’s climate is warming and this increase in global temperature will probably have consequences on the balance between annual soil C inputs (photosynthesis) and losses (respiration)4. In this balance, it is especially relevant the role of soil microorganisms, key players in soil organic matter  transformation and decomposition, and, as a consequence, in soil respiration6. Despite their pivotal role, our knowledge regarding how soil microorganisms respond to environmental changes is still scarce7, and microbial dynamics have only recently started to be incorporated in ecosystem models8. As the predictive capacity of soil carbon cycling models enhances when microbial ecology aspects are considered, it is evident that soil microorganisms are essential for understanding and predicting ecosystem processes9.Forests, which cover 38 million square kilometers of the planet and contain more than three trillions of trees, have a crucial role on the climate, the geochemical cycles and especially on the global carbon (C) cycling since they act as important C sinks11. In the last few years, with the rapid development of high-throughput sequencing techniques, many studies have documented soil bacterial14, archaeal16 and/or fungal19 altitudinal patterns as well as the factors driving these patterns over a high variety of mountain ecosystems. Most of these studies are mainly focused on taxonomic traits of microbial communities; however, if functional traits of microbial communities are also assessed, the useful information that can be extracted from them for climate change mitigation models will increase20. Patterns of microbial functional signatures, such as structural genes relevant to metabolic pathways, energetics and regulatory circuits, provide a better understanding of ecosystem functions, biogeochemical cycles and microbial responses to environmental changes22. In this context, GeoChip, a microarray-based metagenomic technology, has shown to be a specific and sensitive tool for studying microbial gene diversity and metabolic potential in many different environments24. In fact, previous surveys have already used this technology to investigate the effect of altitude on microbial functional gene community21. GeoChip has also been applied along with gene amplicon sequencing to assess both functional and taxonomic properties of soil microbial communities at different altitudes. Ding, et al.25 found significant differences in soil bacterial (based on Illumina amplicon sequencing of 16S rRNA genes) and microbial functional (GeoChip-based) community composition and diversity between shrubland and coniferous forests at the forest timberline. While, Shen, et al.22, studying an altitudinal gradient on Changbai Mountain in China, discovered that microbial functional gene richness exhibited a dramatic increase at the treeline ecotone, but the 16S-rRNA gene sequencing-based bacterial taxonomic diversity did not show a similar pattern.Mountain ecosystems are characterized by drastic changes in climate and biotic characteristics over short altitudinal and geographical distances, and have thus been regarded as powerful natural experiments to study soil microbial response to environmental changes26. The importance of season is also dependent on the type of vegetation, whose composition varies with altitude10. Deciduous forests are characterized by the photosynthetic activity of trees only during the vegetative period and a short litterfall period in autumn. In contrast, coniferous forests present a longer vegetative period and enrich the soil with a more recalcitrant litter compared to that of deciduous vegetation28. Therefore, integrative surveys, where taxonomic and functional traits of soil bacterial and fungal communities are studied in response to changing environmental conditions  and taking into consideration seasonal aspects, can contribute useful and reliable information for improvement of simulation models and mitigation strategies for climate change.Seasonal aspects of soil microbial communities are generally neglected in works correlating altitude with taxonomic and functional patterns of microbial communities. Nonetheless, seasonality is related to alternating climatic conditions and shapes belowground C, nitrogen (N) and other nutrients transfer by changes in tree physiology. Trees release large proportions of their accumulated C and N in the form of tree residues  and root exudates to the soil17 confirmed the significance of altitude determining changes in abundance and Illumina-based analysis of diversity and composition of bacterial and fungal communities  and elucidated the main driving factors of those changes considering only one season; (ii) França, et al.29 reported for the first time culturable bacterial and yeast diversity of Alpine forest soils and discovered distinct differences between culturable bacterial and yeast diversity related to altitude, season and isolation temperature; and (iii) Siles, et al.30 described significant altitudinal and seasonal shifts in some soil physicochemical properties and PLFA (phospholipid fatty acid)-based abundance and structure of soil microbial communities as well as altitudinal variations  in soil microbial functionality . Notwithstanding all these works, there is still a lack of detailed information regarding the altitudinal and seasonal shifts in taxonomic diversity and composition of both bacterial and fungal communities as well as their functional traits in the aforementioned forest ecosystems using comprehensive and state-of-the-art tools.Our recent investigations have been focused on the characterization of altitudinal and/or seasonal (spring to autumn comparisons) shifts in soil microbial communities using different methodological approaches at four Alpine forest sites (representing a climosequence) located over an altitudinal gradient from 545 to 2,000 m above sea level (asl). Using this altitudinal gradient: (i) Siles & Margesinvs. coniferous forest) in spring and autumn to evaluate the effect of altitude and/or season on: (i) soil physicochemical properties and soil temperature; (ii) bacterial and fungal abundance (through qPCR); (iii) diversity and structure of bacterial and fungal communities (using Illumina amplicon sequencing); (iv) network structures of bacterial and fungal communities (using a random matrix theory-based approach); and (iv) microbial diversity and structure of potential functional gene community (using GeoChip 5.0). Finally, we aimed to discover the main factors explaining the altitudinal and seasonal shifts in abundance, diversity and composition of bacterial and fungal communities as well as in microbial functional gene community. We hypothesized that the significantly different bacterial and fungal communities characterizing these two forest sites would differently respond in taxonomic and functional terms to the changes in soil physicochemical properties and environmental conditions determined by seasonality.We here studied two Alpine forests situated at two different altitudes and differing in the vegetation cover type , Acidobacteria (9.43–44.32%), Bacteroidetes (4.99–27.79%), Actinobacteria (2.99–14.34%) and Verrucomicrobia (4.00–10.96%), accounted for more than 82% of the total number of sequences in all the samples , with an average number of sequences per sample of 36,314 . Total bacterial diversity was distributed among 25 different phyla, although the five most dominant phyla, les Fig. . Alphaprria Fig. .Figure 1Basidiomycota (the number of classified sequences in this phylum ranged from 14.46 to 88.07% across the 24 samples), Ascomycota (5.28–88.07%) and Zygomycota (0.62–8.42%), which accounted for more than 80% of total sequences  among Zygomycota . Fungal diversity was distributed among 5 different phyla, and the predominant ones were ces Fig. . The preota Fig. .Bacterial richness, Shannon index and the richness estimators Chao1 and ACE were significantly higher at submontane site M compared to subalpine site R Table . Howeverp = 0.0001; ANOSIM, R = 0.89, p = 0.0001), but not by season , nor by the interaction altitude × season . These results were corroborated by NMDS ordination; NMDS axis 1 clearly separated samples from deciduous forest site M in spring and autumn from those of coniferous forest site R also in both seasons, evidencing no effect of season on bacterial community structure  and Spartobacteria as well as subgroup Gp6 of Acidobacteria were present at a higher relative abundance at deciduous forest site M; whereas, acidobacterial subgroups Gp1 and Gp2, Gammaproteobacteria and subdivision 3 of Verrucomicrobia were detected to a significantly higher extent at coniferous forest site R. Significantly seasonal variations were not noticed for any of the aforementioned taxonomic groups at any of the sites ; Mycobacterium and Asanoa (Actinobacteria); as well as Tepidimonas (Betaproteobacteria) were more abundant at submontane site M in comparison with subalpine site R. Instead, OTUs classified as Povalibacter and Permianibacter (Gammaproteobacteria); Rhodoplanes and Roseiarcus (Alphaproteobacteria); Telmatobacter, Acidipila and candidatus Solibacter (Acidobacteria); Limisphaera (Verrucomicrobia); as well as Labilithrix (Deltaproteobacteria) were present to a higher extent at subalpine site R. Only 2 of the top 76 most abundant OTUs showed significant seasonal effects; OTU10 (belonging to subgroup Gp2 of Acidobacteria) and OTU41 (classified as Steroidobacter (Gammaproteobacteria)) were more abundant in autumn than in spring at subalpine site R , but not by season , nor by the interaction altitude × season . In concordance with these results, fungal NMDS grouped the samples in two different clusters, one of them comprising the samples of deciduous forest site M in spring and autumn and another one including the samples from coniferous forest site R in both seasons , Geminibasidium (OTU58), Umbelopsis (OTU46) and Pachyphloeus (OTU54) genera were more abundant at deciduous forest site M, while OTUs classified at genus level as Elaphomyces (OTU1), Russula (OTU4) or Mortierella (OTU61) were found to a higher extent at coniferous forest site R  in the network of submontane site M, a key topological property to describe how well a node is connected with the others, was higher (ca. twofold) than that of the subalpine site R, suggesting the existence of a more complex microbial network and coupling at submontane site M. On the contrary, an increased modularity value, as a measurement of system resistance, was noticed at coniferous forest site R  Fig. , revealeAcidobacteria , 3 Actinobacteria, 5 Alphaproteobacteria, 3 Betaproteobacteria, 2 Deltaproteobacteria, 3 Gammaproteobacteria, 1 Planctomycetia, 7 Sphingobacteriia, 1 WPS-2 and 2 unclassified OTUs among Bacteria, as well as 1 Leotiomycetes, 2 Pezizomycetes, 1 Sordariomycetes and 2 unclassified OTUs, among Fungi (Table Gammaproteobacteria (1) and Actinobacteria (1) among Bacteria, as well as Agaricomycetes (1) and Eurotiomycetes (1) among Fungi; 1 bacterial node and 2 fungal nodes were unclassified OTUs , with only a few links and almost always linked to the nodes within their own modules  Fig. . A totalgi Table . At the gi Table . They weUs Table .A total of 20,729 functional genes were detected by GeoChip 5.0 across the 12 samples analyzed, with an average number of detected genes per sample of 15,098 (SD = 385). These genes were mainly involved in six functional categories: C cycling (41.88%), organic remediation (23.26%), N cycling (12.91%), sulfur (S) cycling (8.03%), metal homeostasis (7.32%) and phosphorus (P) cycling (4.42%) Table . Other tp = 0.0001; ANOSIM, R = 0.69, p = 0.0024), season  and the interaction altitude × season . These results were corroborated in NMDS ordination of soil samples; NMDS axis 1 separated samples of submontane site M from those of subalpine site R, while spring and autumn samples from both sites were separated by the NMDS axis 2  were the most abundant in this pathway over all the samples, and no significant altitudinal or seasonal shifts were detected for any of these genes (data not shown).Regarding C cycling, we firstly examined the genes related to C fixation. Among the four different C fixation pathways represented in GeoChip 5.0, the Calvin cycle appeared to be the predominant one at both sites in both seasons. The genes amyA, Table ara, xylanase, xylA and mannanase genes, Table vanA, limEH, cdh, vdh, camDCAB and lmo) in autumn were recorded only at submontane site M, while those autumn increases in genes related to cellulose  degradation were only recorded at subalpine site R. Except for aromatic compound - degrading genes, the abundance of genes measured at subalpine site R in autumn was always significantly higher than that of submontane site M in spring. It is worth noting that the coniferous forest site R in autumn presented significantly increased levels of genes involved in the degradation of both labile (starch) and recalcitrant  C substrates  were the most quantitatively abundant among all the S-cycling-related genes across all the samples, showing the coniferous forest site R in autumn the significantly highest normalized signal intensity values  was higher than that of ppk genes (encoding polyphosphate kinase proteins for polyphosphate biosynthesis). Both groups of genes were significantly higher at subalpine site R compared to submontane site M and showed to increase in autumn at both sites  Table . On the 1. In this study, we have demonstrated that soil bacterial and fungal communities from two Alpine forest sites differing in altitude and vegetation cover type, significantly differed in terms of abundance as well as taxonomic and functional gene diversity and composition . However, significant seasonal changes (comparing spring and autumn) were limited to changes in functional diversity and composition (GeoChip-based) at both sites. To the best our knowledge, it is one of the first surveys where bacterial and fungal communities as well as their functional properties are assessed at the same time taking into consideration altitudinal and seasonal aspects.Integrative studies investigating abundance, taxonomy and functionality of soil microbial communities are important for understanding the response of microorganisms to changing environmental conditions and the ecosystem functioning17. Other recent studies have also found clear soil bacterial and fungal compositional differences among different altitudes33, evidencing that altitude factor is a major driver of variation in microbial composition. Since altitude is not an environmental variable itself, it has been proposed that soil microbial communities’ structure over altitudinal gradients is mainly governed by changes in vegetation cover type13. Plant composition influences belowground conditions by driving changes in soil environmental properties such as soil pH, litter quantity and quality, soil nutrient contents and soil moisture34. Conversely, the belowground community can affect the above-ground community by carrying out a wide spectrum of decomposition processes35. In this way, it is broadly accepted that soil physicochemical parameters served as a bridge to link the aboveground and belowground communities26. Therefore, we see the differences in microbial communities between both sites in connection with the different type of vegetation predominating at each forest site (the deciduous vegetation dominates at site M and coniferous one at site R) since pH as well as SOM and TOC contents showed to be the main drivers of bacterial and fungal abundance, diversity and community structure. The subalpine site R presented increased levels of C, N and other nutrients respect to the submontane site M, which can be explained by the higher recalcitrance of coniferous litter (high C/N ratio and lignin content as well as low pH36), producing a greater C sequestration and lower nutrient immobilization rates17. Furthermore, some surveys, comparing evergreen and deciduous forests, have indicated that evergreen forests accumulate more SOM in the forest floor than deciduous forests located in similar climatic zones25. Therefore, the increased levels of C and other nutrients detected at site R may contribute to enhance microbial growth and would explain the higher bacterial and fungal abundance detected at this forest site, as we formerly explained17. Likewise, the differences in SOM and other nutrient contents between both sites could explain to some extent the soil microbial composition characterizing each forest site. For example, Gammaproteobacteria were detected to a significantly higher extent at coniferous forest site R and the copiotrophic lifestyle of this group of bacteria has been highlighted37, while Actinobacteria, adapted to resource limited conditions38, were more abundant at deciduous forest site M. Nevertheless, it is generally recognized that the general classification of bacterial taxa as oligotrophic or copiotrophic is a clear oversimplification of large variations in ecological attributes and microbial lifestyles. In the case of fungal community, from a functional perspective, both mycorrhizal and saprotrophic fungi have shown to dominate in forest soils28. Both groups were indistinctly detected in the present study at both sites in spring and autumn, although they taxonomically differed between the two forest sites. Ectomycorrhizal fungi such as Pachyphloeus and Amanita predominated at deciduous forest site M while those classified as Elaphomyces and Russula dominated at coniferous forest site R. Saprotrophic fungi belonging to Umbelopsis genus were more abundant at submontane site M; instead, saprotrophic Mortierella fungi dominated at subalpine site R. In concordance, some other studies have also shown significant taxonomic differences in forest soil mycorrhizal and saprotrophic fungi in response to changes in soil depth and/or season28.According to our initial hypothesis, diversity and community structure of bacterial and fungal communities differed between the submontane forest site M and the subalpine site R, which concurs with the results of our previous work also studying these forest sites39. The higher average degree and shorter path distance of submontane site M network revealed that bacterial and fungal interactions were more intensive at this site, probably as a consequence of certain deterministic processes such as habitat filtering, and reflecting shared ecological tolerances. Network theory suggests that strongly connected communities are more susceptible to disturbance40. This assumption, along with the shorter path distance of deciduous forest site M network, indicated that microbial community at site M would respond slowly to perturbations in comparison with subalpine site R. The more susceptible community structure of submontane site M was also reflected in the lower evenness of bacterial community, which showed to positively correlate with community stability in a previous work41. In addition, as modularity may serve as an indicator of system resistance by compartmentalizing social-ecological systems42, the higher modularity of subalpine site R network indicated that the microbial system of this site would be more resistant to changes at taxonomic level. Interestingly, Ding, et al.25, also found that the soil bacterial network of a broadleaved evergreen forest presented a higher resistance to environmental changes than that of a broadleaved deciduous forest. On the other hand, we discovered that most of the OTUs identified as module hubs and connectors  by the Zi-Pi plot . It is also worth noting that some of the generalist OTUs found in submontane site M network , were present as specialists in coniferous forest site R network, suggesting that the same OTU exhibited different roles depending on the particular environmental and soil physicochemical properties.Network analyses are valuable tools for providing important details on community assembly rules such as cooperation, competition or habitat filtering, and to represent mathematical interactions or couplings among different microbial groupslot Fig.  at both lot Fig.  and S4, 30. The differences in the significance of the factor season between both works are likely a consequence of the different methodological approaches used  and the higher number of samples and forest sites investigated in our former study30. Some other surveys using amplicon sequencing for the analysis of soil microbial communities have shown a significant effect of season on diversity and composition of different forest soil bacterial44 and fungal28 communities in concomitance with significant changes in soil physicochemical properties. Therefore, in the present study, the no significant seasonal changes in the taxonomic diversity and composition of both bacterial and fungal communities is seen in connection with the lack of significant seasonal changes in most of the soil physicochemical properties tested. However, and according to Mantel test results, the trend of increased (but non-significant) SOM and nutrient contents in autumn respect to spring at both sites could explain the significant seasonal changes in the diversity and structure of microbial functional gene community observed at both sites. These findings would be supporting the idea that soil microbial communities of these sites are able to significantly respond in functional terms to subtle shifts in environmental conditions. However, more drastic differences in environmental conditions  are needed to detect significant differences in microbial communities in abundance and taxonomic terms. In this regard, Burke, et al.45 suggested that microbial responses to environmental shifts may be better predicted by functional genes rather than taxonomy.Contrary to our initial hypothesis, both diversity and community structure of bacterial and fungal communities did not significantly vary between spring and autumn at any of the forest sites. Our initial hypothesis was supported by one of our previous works, which showed significant spring to autumn shifts in soil PLFA-based microbial community structures of four forests over an altitudinal gradient, including the two forests studied here46. Several recent surveys are focused on finding these relationships at different ecosystem scales by combining bacterial and fungal ribosomal gene sequencing with whole genome shotgun sequencing or GeoChip analyses22. For example, Fierer, et al.47 showed a strong positive correlation between taxonomic and functional gene diversity in the tallgrass prairie soils of the midwestern United States, suggesting a low degree of functional redundancy. Likewise, Zhang, et al.48, comparing the soil microbial communities of a natural mature forest with those of a natural secondary forest, found a strong positive correlation between the soil bacterial community structure and the GeoChip-based functional community composition. However, there are other studies showing no significant correlations between microbial taxonomic and functional composition39, which is explained as a consequence of the functional redundancy of soil microbial communities49. The contradictory results may be caused by differences in terms of techniques, scale or ecosystem investigated22. In our study, although Mantel test results showed that microbial functional gene composition significantly correlated with taxonomic diversity and structure of bacterial and fungal communities, the no concomitant seasonal changes in microbial taxonomy and function may be related to the aforementioned functional redundancy of soil microbial communities. In fact, when Mantel tests are repeated using only the data for each site in spring and autumn, significant correlations are not found. Functional redundancy of species is recognized as a common feature in soils49, which is based on findings revealing that some species carry out similar roles in communities and/or ecosystems46. However, functional redundancy is difficult to establish because it requires detailed information about the microbial populations that perform a specific process. Furthermore, microorganisms that are functionally redundant under one set of conditions may not be under other different conditions. In general, our knowledge about the distribution of functional traits across microbial taxa is still scarce46. Therefore, more studies using multiple comparable techniques are needed in the future to test taxonomic and functional relationships across different ecosystems.The relationships between taxonomic and functional diversity in soils are largely unknown50. For example, the degradation of highly humified SOM has shown to be energy limited, determining very slow SOM decomposition rates in soils. However, the input of labile C may provide fresh energy and enable microorganisms to degrade SOM more efficiently51. Complementarily, former studies have also suggested that changes in soil N content and availability determine alterations in rates of microbial-mediated SOM and litter decomposition. Altogether, these findings evidence that the availability of labile C and N controls the decomposition of complex substrates and thus the availability of nutrients for plants50. In this context, in our study, the lack of altitudinal or seasonal shifts in the genes related to the major C fixation pathway , on one hand, and the predominance of genes associated with C degradation among those related to C-cycling, on the other hand, could be evidencing a dependency of the soil microorganisms from these two forest ecosystems to external C inputs . We see this finding in line with the significantly increased signal intensities of C degrading genes and the trend of higher soil C contents detected in autumn respect to spring at both sites. Autumn is characterized by the leaching of nutrients and compounds from the upper soil horizons after litterfall and/or the deposition of easily decomposable compounds from roots in soil after the end of the vegetation period, as it has been discussed in our former article30. It is worth noting that coniferous forest site R in autumn presented significantly increased levels of genes encoding enzymes for the degradation of both labile and recalcitrant C sources, which is seen in connection with the results of the network analysis showing a more resistant microbial community to environmental changes at this site and with the significantly higher potential β-glucosidase, xylosidase and cellobiohydrolase activities detected at this site respect to site M in one of our previous works investigating these forest sites30.The quantity and quality of available C and N in soil are important regulating soil microbial-mediated processes and functionsamoA and hao) and NH4+-N , nor between the abundance of denitrification genes (nirS and nirK) and NO3−-N , suggesting a decoupling between the gene signals detected and the real activity of microorganisms. In this context, it is important to bear in mind that GeoChip microarray data reflect potential functional capacity in the environment52.Regarding the Geochip-based analysis of N-cycling genes, it is interesting to note that we did not find a significant correlation between the total abundances of nitrification genes (vs. coniferous forest), soil physicochemical properties and soil temperatures, had a strong significant effect on abundance, taxonomic diversity and structure, and network structure of soil bacterial and fungal communities, as well as on soil microbial functional gene diversity and composition. However, season (comparing spring and autumn) only exerted a significant effect on functional gene diversity and composition. Altitudinal and seasonal changes in abundance, diversity and community structure of bacterial and fungal communities as well as in microbial functional gene composition were mainly explained by variations in soil pH, SOM and nutrient contents as well as in soil temperatures. Therefore, the detection of only significant seasonal changes in microbial functional diversity and community structure is likely a consequence of the non-significant increased SOM and nutrient contents detected in autumn respect to spring at both forest sites and the presumable high ability of soil microbial communities to respond in functional terms to discreet environmental changes. In the future, further studies, considering forests in a wider range of altitudes and the four seasons of the year, are needed to deepen our understanding regarding the effect of the changing environmental conditions related to shifts in altitude and season on soil microbial communities.In conclusion, we here demonstrated that altitude, comparing two Alpine forest sites located at two different altitudes and characterized by different vegetation cover types . A detailed description of each site has previously been reported54. Briefly, the submontane site M  is located 8 km south of Bozen/Bolzano on the small peak Kleiner Priol at an altitude of 545-570 m asl. The pedogenic substratum consists of rhyolite (quartz-porphyry) and the soil was classified as dystric cambisol (FAO). The site consists of mixed deciduous forest, dominated by Quercus pubescens, Q. robur, Fraxinus ornus, Pinus sylvestris and Ostrya carpinifolia. The climate is mild continental with submediterranean influences, with a mean annual air temperature of 11.0 °C and an annual precipitation of 900 mm.The two investigated sampling sites M (Montiggl) and R (Ritten) are two long-term monitoring sites, which were installed in the Italian Alps in 1992 within the framework of ICP IM Picea abies, P. cembra, Larix decidua and Vaccinium myrtillus. The climate is subalpine-continental with a mean annual air temperature of 4.0 °C and an annual precipitation of 1000 mm.The subalpine site R  is located 7 km north of Bozen/Bolzano below the Rittner Horn at an altitude of 1,724-1,737 m asl. The pedogenic substratum consists of rhyolite (quartz-porphyry) and the soil was classified as haplic podzol (FAO). The site consists of coniferous forest close to the timber line, dominated by h horizon (top 10 cm); the number of sampled cores (2–5) depended on the thickness of the sampled Ah horizon at each site30. The distance between sampling spots in each sampling area was site-dependent17. To determine the effect of season at each site and to take into account the different vegetation periods at the investigated sites, soil samples were collected both in late spring  and autumn . The 24 soil samples (6 samples × 2 sites × 2 seasons) were transported immediately after sampling in cooled boxes to the laboratory, sieved (2 mm mesh) and stored at 4 °C for physicochemical characterization or at −80 °C prior to DNA extraction.Six sampling spots, distributed uniformly over each site (100 × 100 m) and considered as independent replicates, were chosen to cover within-site variability. Soil samples were collected from each of these sampling spots from the A30. Inorganic N forms, NH4+-N and NO3−-N, were measured in 1 M KCl soil extracts, as reported by Rhine, et al.55 and Cataldo, et al.56, respectively.Each soil sample was characterized regarding soil pH, electrical conductivity (EC), SOM, total organic carbon (TOC), total nitrogen (N), C/N (TOC/N) ratio as well as plant available P, K and Mg, as formerly describedh horizon .Soil temperature was measured in triplicate at each site using DS1921G Thermochron iButton dataloggers buried at a depth of ca. 5 cm in the A260/A280 and A260/A230. Subsequently, the DNAs were quantified using QuantiFluor™ dsDNA System  and DNA concentration for each sample was standardized to 20 ng μL−1.Total DNA from each soil sample (24) was extracted in triplicate from 250 mg of soil fresh mass using Power Soil™ DNA Isolation Kit  following the manufacturer’s instructions. Triplicate DNA extracts from each soil sample were then pooled, and DNAs were purified using DNA Clean & Concentrator Kit  according to manufacturer’s instructions. The quality of DNAs was spectrophotometrically checked by NanoDrop  based on the absorbance ratios A57 for bacteria and FR1/FF39058 for fungi using SYBR® Green as detection system  under the PCR mixtures and thermal conditions previously described17. Bacterial and fungal standard curves were generated using a recombinant plasmid containing one copy of 16S rRNA and 18S rRNA genes, respectively, as described by Siles & Margesin17. The curves were drawn by plotting the Ct values as a log function of the copy number of 10-fold serial dilutions of the plasmid DNA. The relationship between Ct and the target-gene copy number, on the one hand, and the copy numbers of the real-time standard, on the other, were calculated as previously described59. The copy numbers of bacterial 16S rRNA and fungal 18S rRNA genes were expressed as copy number per gram dry mass soil.The quantification of bacterial 16S rRNA and fungal 18S rRNA genes was conducted by qPCR from each DNA sample (24) with the pair of primers Eub338 /Eub51861. From each site and season, six DNA samples were amplified in triplicate using HotStarTaq Plus Master Mix Kit  containing barcoded forward primers, under the next thermal conditions: initial denaturation at 94 °C for 3 minutes, followed by 28 cycles of denaturation at 94 °C for 30 s, primer annealing at 53 °C for 40 s and extension at 72 °C for 60 s as well as a final elongation step at 72 °C for 5 minutes. The success of the amplification was checked in a 2% agarose gel and reactions of the same sample were merged. Subsequently, PCR products from all the samples were purified using Agencourt AMPure XP magnetic beads kit , quantified with the QuantiFluor™ dsDNA System and pooled in equal proportions. The pooled product was then used to prepare Illumina DNA library following TruSeq DNA library preparation protocol. Paired-end sequencing (2 × 300) was performed on the Illumina MiSeq sequencing platform  at MR DNA .Prokaryotic communities were characterized amplifying the V4-V5 hypervariable region of 16S rRNA gene using the primers new515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 909 R (5′-CCCCGYCAATTCMTTTRAGT-3′)63, with the next cycling conditions: 94 °C for 3 minutes, followed by 28 cycles of 94 °C for 30 s, 57 °C for 40 s and 72 °C for 60 s, and a final extension of 72 °C for 5 minutes. The processing of amplicons for paired-end sequencing (2 × 300) on Illumina MiSeq sequencing platform was as described for prokaryotic community.Fungal communities were analysed by amplification of ITS1 region using the ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′) primersThe raw bacterial and fungal sequences associated with this study were deposited in the GenBank SRA database under BioProject accession number PRJNA378480.17. Next, sequences from each library were processed using USEARCH pipeline and UPARSE-OTU algorithm64. Briefly, reads were separately quality-filtered allowing a maximum e-value of 1 for both libraries, trimmed (to 350-bp (base pair) and 240-bp for prokaryotic and fungal libraries, respectively), dereplicated and sorted by abundance (removing singleton sequences) prior OTU determination at 97% sequence identity. Then, chimeric representative sequences from the OTUs were detected and removed using UCHIME65. Finally, original high-quality sequences were mapped to OTUs at the 97% identity threshold obtaining one OTU table for prokaryotic community and another one for fungal community. The taxonomic affiliation of each OTU was obtained using RDP  taxonomic classifier against 16S rRNA training set 14 for prokaryotic sequences and UNITE Fungal ITS train set 07-04-2014 for fungal sequences with a 50% confidence threshold for both cases. Next, OTUs classified as Archaea were removed from prokaryotic OTU table. All the remaining prokaryotic OTUs and all the OTUs from fungal dataset could be classified as Bacteria and Fungi, respectively, and they were thus retained. These two OTU tables were used for downstream analyses.First, raw MiSeq paired-end reads from prokaryotic 16 rRNA and fungal ITS amplicons were separately assembled and reoriented using MR DNA pipeline. Subsequently, sequences were demultiplexed and formatted for processing using a Phython script66.For diversity characterization of microbial communities and statistical analyses, the number of sequences per sample was normalized to 25,000 sequences for bacteria and 40,000 sequences for fungi. The bacterial and fungal communities were characterized in terms of diversity by calculating richness (number of OTUs), Shannon index, Smith-Wilson evenness and the richness estimator indices ACE (abundance-based coverage estimation) and Chao 1 using Mothur v.1.37.067. Briefly, DNA from each sample (~1 µg) was firstly labeled with the fluorescent nucleic acid dye Cy3 using a random priming method. The labeled DNA was subsequently purified and dried before hybridization on Agilent platform-based GeoChip 5.0 arrays at 67 °C for 24 h. GeoChip microarrays were then scanned with a SureScan Microarray Scanner . Raw data were quantified and normalized as previously described67. Spots with a signal-to-noise ratio <2.0, signals <1.3 times the background or <1000 were discarded. Additionally, spots that were detected in less than two samples  were also removed. Before statistical analyses, data were: (i) normalized by dividing the signal of each spot by the total intensity of the microarray; (ii) multiplied by a constant; and (iii) transformed by the natural logarithm. Microbial functional gene diversity was characterized by calculating richness (number of detected genes), Shannon index and Smith-Wilson evenness using Mothur v.1.37.0.The small version of GeoChip 5.0  was used for functional characterization of soil microbial communities at both sites in spring and autumn. Three DNA samples per site and season were analyzed. To do this, the original six DNAs extracts from each site and season were merged two by two to give three pooled pairs of composite samples. GeoChip analyses were done as previously describedhttp://ieg2.ou.edu/MENA) and the networks were constructed using random matrix theory-based methods and default parameters. More information on theories, algorithms, pipeline structure and procedures was described by Zhou, et al.69 and Deng, et al.70. An identical similarity threshold (0.900) for both sites was chosen for network constructions to allow direct comparisons. To characterize the modularity property, each network was separated into modules by the fast greedy modularity optimization. Various indices were calculated and used to descript and compare topological properties of networks at each forest site  was applied to determine whether there were significant differences (p ≤ 0.05) between the sites M and R in spring and autumn regarding (i) soil physicochemical properties; (ii) soil temperature; (iii) bacterial and fungal abundance; (iv) bacterial, fungal and microbial functional diversity measurements; (v) relative abundance of the different taxonomic bacterial and fungal groups; (vi) relative abundance of the top most abundant bacterial and fungal OTUs; and vii) normalized signal intensities of the different functional groups detected using GeoChip 5.0. Tukey’s honest significance difference (HSD) post-hoc test was used for multiple comparisons of means at a 95% confidence interval. Normality and heteroscedasticity of data were tested by the Shapiro-Wilk and Levene tests, respectively. In case that one of those conditions was not met, the values were log transformed.72.The significance of the factors altitude and season, on the structure of bacterial, fungal and microbial functional gene communities, was tested using permutational analysis of variance (PERMANOVA) and analysis of similarity (ANOSIM) tests with Bray-Curtis similarities and 9999 permutations. Non-metric multidimensional scaling (NMDS) analysis based on Bray-Curtis similarities at OTU level was used to visualize patterns in bacterial and fungal community structures. NMDS analysis was also applied for GeoChip 5.0 dataset. All the analyses were done using “vegan” package in RMantel test, comparing Bray-Curtis similarities matrices with 9,999 permutations, was used to detect significant correlations (p ≤ 0.05) between microbial functional gene community composition and the different physicochemical properties tested, soil temperature measurements as well as bacterial and fungal abundances. Mantel test was also used to study the relationships between abundance, diversity (Shannon index) and community structure of bacterial and fungal communities and the different soil physiochemical properties and soil temperatures. “Vegan” package in R was used for Mantel tests.Supplementary Information


Nocardiopsis litoralis strain VSM 8. The main quadratic effects and interactions of the five variables on the production of bioactive metabolites were investigated. A second-order polynomial model produced a satisfied fit for experimental data with regard to the production of the bioactive metabolites. Regression analysis showed that high R2 values of all the five responses are significant and adjusted R2 values showed good agreement with the experimental and predicted values. The present model was used to evaluate the direct interaction and quadratic effects to optimize the physico-chemical parameters for the production of bioactive metabolites that inhibit the pathogenic microorganisms measured in terms of zones of inhibition (responses). Mathematical kinetic model development and estimation of kinetic parameters also showed good approximation in terms of model fitting and regression analysis.Response surface methodology-based central composite design on five variables incubation time, pH, temperature, sucrose concentration, and soya peptone concentration was employed for optimization of the production of bioactive compounds by  Microorganisms dwelling in extreme environments are prolific producers of several bioactive compounds that have evolved due to adaptation of the extreme environmental conditions in terms of metabolic biochemistry. Actinomycetes are well documented for their capacity to produce bioactive metabolites with complex diversity and diverse biological activities. The genes involved in the synthesis of secondary metabolites remain undiscovered due to absence of transcription under conventional laboratory conditions, but mathematical models predict thousands of unexplored secondary metabolites from actinomycete genomes. Actinomycetes are stable, persistent, and active component of the marine microbial communities. Discovery of novel actinobacteria, which operate unusual metabolic pathways, has the ability to produce complex chemical compounds with potential biological activities.Optimization of physiological parameters and the supplementary nutrition for the biosynthesis of bioactive metabolites can be achieved by the systemic study. The important parameters such as cultural conditions and media constituents are the significant factors influencing the high yield of antimicrobial compounds. Often the production conditions are very similar among the closely related actinomycetes, and hence, optimization of growth and production conditions is very crucial for the maximum production of potential bioactive compounds  optimization is laborious and time-consuming, and also misleading conclusions may be drawn, because individual factor interactions are ignored. Hence, establishing the optimized conditions is cumbersome, and therefore, cultural conditions need to be optimized to maximize the production of bioactive compounds with preserved biological activities .Hence, the objectives of this study include the statistical optimization of process parameters for bioactive metabolite production using RSM and to estimate the kinetic parameters of actinomycetes fermentation using N. litoralis strain VSM 8, isolated from deep sea sediment samples of Bay of Bengal, Visakhapatnam using soil dilution plate technique on actinomycetes isolation agar medium containing 2-g sodium caseinate, 0.1-g l-asparagine, 4-g sodium propionate, 0.5-g dipotassium phosphate, 0.1-g magnesium sulfate, 0.001-g ferrous sulfate, and 5 ml glycerol per 1000 ml distilled water (pH 8). The strain has been deposited in NCBI Genbank with an accession number KT901293. The strain was maintained on Yeast extract Malt Extract Dextrose (YMD) agar medium at 4 °C. An attempt was made in the study to optimize the culture conditions for enhancing the production of bioactive metabolites by the strain using RSM.As a part of our ongoing research on bioactive metabolites, one promising strain with good antimicrobial potential was identified as N. litoralis strain VSM 8 for the bioactive metabolite production under a wide range of physical conditions was performed.RSM applies the statistical and mathematical approach for modeling, designing, and analyzing the engineering problems. It is used to optimize the response surface that influences the various parameters and relationship between the input parameters and the obtained response quantitatively Parmjit . Relatio5 full factorial CCD for five variables comprising 32 factorial points, 10 axial points, and 8 replicates:N is total number of experimental runs to be performed, n is number of variables (factors), and nc is number of replicates at center points.To design minimum number of experimental runs, a full factorial central composite face-centered design (CCFD) with five independent variables and their combinations was used to optimize the response with the region of three-dimensional observation spaces. Design Expert software  was used to design the experiments for bioactive metabolites production. Using CCD of RSM, the most significant variables  at their optimum levels were identified for maximal response in terms of antimicrobial activity of bioactive metabolites measured as zone of inhibition. Five independent variables selected in this study include A-Incubation time, B-pH, C-Temperature, D-Sucrose, and E-Soya peptone. A total of 50 experiments were obtained using following equation that have 2The central coded value of all the variables was considered as zero. Low and high ranges of all the variables used in RSM and the complete experimental plan with values in actual and coded form are presented in Table R2), adjusted determination coefficient (Adjusted-R2), root mean square error (RMSE), and absolute average deviation (AAD) were carried out between experimental and predicted data. The data obtained were subjected to graphical and regression analysis using Design Expert software. The experimental errors and reproducibility of the data were determined by the central points. To minimize the effect of the uncontrolled factors, the experimental sequence was randomized. The quadratic regression equation was used with each variable to develop an empirical model which correlated the response (bioactive metabolite production) to five variables, as per the following equation . To analyze the fit and prediction accuracy of the model constructed, correlation coefficients . The data acquired from the models were used to calculate the specific cell growth rate (µmax), day−1, specific production rate of bioactive metabolite, day−1.The growth of halophilic marine actinomycete with limiting carbon substrates influences the bioactive metabolite production. Basic mathematical and unstructured kinetic models quantitatively describe the substrate utilization and growth-associated production formation kinetics in a batch system, and the similar equations were also developed by many researchers  (X) , in a batch fermentation, is best described as logistic function -type model equation that relates hyperbolic growth of cell:Bioactive metabolite production can be obtained from growth limiting substrate (optimized media constituents) and the substrate utilization kinetics can be taken from Modified Leudeking–Piret (MLP) equation:On integration, the above equation results logistic incorporated modified Leudeking–Piret (LIMLP) equation:η, in above equation can be calculated from stationary phase data formation occurs in late-logarithmic phase of cell growth and bioactive metabolite formation kinetics follows Leudeking–Piret equation (Luedeking and Piret Logistic incorporated Leudeking–Piret (LILP) equation derived from integration of the above equation results:β, can be determined from stationary phase data , an, an6), aN. litoralis strain VSM 8 is represented in mm) was experimentally found to be 21 mm (Staphylococcus aureus), 20 mm (Bacillus subtilis), 22.9 mm (X. campestris), 19.9 mm (Pseudomonas aeruginosa), and 17.9 mm  that was obtained from cultural conditions of the strain grown in a medium containing 2 % sucrose, 1 % soya peptone with pH 8 incubated at 25 °C for 11 days.Influence of different physico-chemical parameters on bioactive metabolite production and their effect on the response (measured as inhibition zones) were investigated and optimized as per the model designed by CCD of RSM. The effect of independent variable optimization on the responses was identified by complete five factors and three-level factorial experiment designs with eight replications of central point and ten axial points and thirty-two factorial points for bioactive metabolite production by the strain. The maximum production of the bioactive compound and its effect on the responses , model summary statistics  for the best outcome quadratic model  for responses Staphylococcus aureus, Bacillus subtilis, Xanthomonas campestris, Pseudomonas aeruginosa, and Candida albicans (inhibition of the growth was represented in mm). The lack of fit test values of quadratic models identified earlier as the accepted model for all the responses was found to be insignificant lack of fit which indicate that the model is highly significant. The second-order quadratic model equations and regression coefficients (%) of all responses  implies that the model is significant. Model terms are said to be significant if the values of p (Prob < F) is less than 0.0500. ANOVA reveals that most of the significant factors affecting the growth inhibition of pathogenic microorganisms (five responses) by the bioactive compounds produced by VSM 8 for the variables: incubation time (days), pH, temperature, concentration of sucrose, and concentration of soya peptone. Tables The model was found to be statistically significant with confidence level of 99.9 % since the Prob > R2) values of the five responses and high value of R2 indicate that the full quadratic model equation was capable of representing that the system under a given experimental domain is significant and adjusted coefficient of determination , pH, temperature, concentration of sucrose, and concentration of soya peptone on the responses, are insignificant, but exhibited significant interactions with other parameters. The significant interactive effects of the variables  are represented in 3D plots. The RSM generated 3D plots are used to analyze the effect of the interactions of the variables, and the plots are generated with response on the nt Figs. , 3, 4, 5N. litoralis strain VSM 8 extended from 5th to 11th day. The maximum yield of the bioactive metabolite was recorded on the 11 day.Effect of the different variables with linear square and quadratic coefficients was obtained at significant terms. As single, the factors like concentration of sucrose and soya peptone had high coefficient value which showed their high linear significant effect on growth inhibition of pathogenic microorganisms by the bioactive compound produced by the strain. Direct effect of the five variables, time of incubation, pH, temperature, concentration of sucrose, and concentration of soya peptone on the growth inhibition of the five response pathogens by the bioactive metabolites produced by the strain, is represented by zone of inhibition (mm). The stationary phase of the strain Amycolatopsis alba var. nov. DVR D4 strain produced maximum bioactive compound after 4 days of incubation. However, Abd-Elnaby et al.  of the best carbon source selected for the growth and antimicrobial metabolite production was also investigated. The result is in conformity with the results reported by Uddin et al.  which is represented as zone of inhibition. Significant interactions between the five variables were observed and analyzed from the 3D surface plots. Application of RSM with CCD statistical experimental design to optimize the selected factors for maximum production is an efficient method that tests the effect of interactions among the variables with minimum number of experiments. Regression equations were derived for both selectivity and total flux using the experimental data together with the statistical software package Design Expert 7.1.3, yielding predicted values in good agreement with observed values.RSM, a collection of statistical and mathematical method, is used in conjunction with central composite design to optimize the different variables at different levels for the production of bioactive metabolite by N. litoralis strain VSM 8 (KT901293), limiting substrate utilization results obtained from shake-flask experiments and model kinetics, were compared in Fig. S. aureus, B. subtilis, X. campestris, P. aeruginosa, and C. albicans strains over the time, is shown in Fig. R2) values obtained by fitting logistic (L), logistic incorporated Leudeking–Piret (LILP), and logistic incorporated modified Leudeking–Piret (LIMLP) models to the experimental data were found to be high, thus revealing good precision of the models.The growth profiles of M 8 KT90193, limitµmax, X0 and Xmax were calculated from N. litoralis strain VSM 8 (KT901293) growth kinetic profile using logistic (L) model. Values of growth and non-growth-associated product parameters, α and β, were estimated using LILP model. Higher α value than β confirmed that bioactive metabolite production by N. litoralis strain VSM 8 is more growth-associated than non-growth associated in shake flask. The simulated parameters, γ and η, of LIMLP model are also in good agreement with the experimental values, which implies that this model is more appropriate to represent limiting substrate utilization kinetics in bioactive metabolite production by N. litoralis strain VSM 8. Furthermore, zones of inhibition from agar diffusion tests are much similar to model predicted values . The experimental results showed that the maximum zone of inhibition against all the five responses was found to be 21 mm (Staphylococcus aureus), 19.8 mm (Bacillus subtilis), 22.9 mm (Xanthomonas campestris), 19.9 mm (Pseudomonas aeruginosa), and 17.9 mm . The highest production of the bioactive metabolite produced by VSM 8 represented in terms of antimicrobial activity was obtained when the strain was grown in a medium containing 2 % sucrose, 1 % soya peptone with pH 8 incubated at 25 °C for 11 days. However, the range of time of incubation, pH, temperature, concentration of sucrose, and soya peptone above or below the central point reported less zone of inhibition.The effect of different variables, including incubation time, pH, temperature, sucrose, and soya peptone concentration on production of bioactive metabolites by R2 values. RSM with CCD described the production of the bioactive compounds by N. litoralis strain VSM 8. Furthermore, the optimum conditions and the effect of bioactive compound against five responses are well induced by 3D plots.A response surface experimental methodology, based on three levels central composite design of experiment, was successfully employed in this optimization study, accounting for the effects of the main variables. The quadratic models developed and subsequent ANOVA test, the concentration of sucrose and soya peptone dosage was found to be the most influential variables for the bioactive metabolite production along with the other significant variables. The model fitted very well to the experimental data, as confirmed by high N. litoralis strain VSM 8 growth, and limiting substrate utilization and bioactive metabolite production  showed good regression squares. Thus, the unstructured models provided a better approximation of kinetic profiles of bioactive metabolite production by N. litoralis strain VSM 8 in submerged shake-flask fermentations. To the best of our knowledge, this is the first ever report on the kinetic modeling for bioactive metabolite production measured in terms of zones of inhibition by N. litoralis strain VSM 8.The estimated kinetic parameters for the