High-throughput formation of lipid bilayer membrane arrays with an asymmetric lipid composition

Rikiya Watanabe
Department of Applied Chemistry
Graduate School of Engineering
The University of Tokyo
Bunkyo-ku113-8656TokyoJapan

PRESTO
JST
Bunkyo-ku113-8656TokyoJapan

Naoki Soga
Department of Applied Chemistry
Graduate School of Engineering
The University of Tokyo
Bunkyo-ku113-8656TokyoJapan

Tomoko Yamanaka
Department of Applied Chemistry
Graduate School of Engineering
The University of Tokyo
Bunkyo-ku113-8656TokyoJapan

Hiroyuki Noji
Department of Applied Chemistry
Graduate School of Engineering
The University of Tokyo
Bunkyo-ku113-8656TokyoJapan

High-throughput formation of lipid bilayer membrane arrays with an asymmetric lipid composition
10.1038/srep07076
We present a micro-device in which more than 10,000 asymmetric lipid bilayer membranes are formed at a time on micro-chamber arrays. The arrayed asymmetric lipid bilayers, where lipid compositions are different between the inner and outer leaflets, are formed with high efficiency of over 97% by injecting several types of liquids into a micro-device that has hydrophilic-in-hydrophobic surfaces. The lipid compositional asymmetry is an intrinsic property of bio-membranes, and therefore, this micro-device extends the versatility of artificial lipid-bilayer systems, which were previously limited to symmetric bilayer formation, and could contribute to the understanding of the role of lipid compositional asymmetry in cell physiology and also to further analytical and pharmacological applications.

M ost biological membranes possess an asymmetric transbilayer phospholipid distribution, in which endogenous enzymes expend energy to maintain the arrangement by promoting phospholipid translocation or flip-flop 1 . In particular, the plasma membrane of most eukaryotes maintains a high degree of asymmetry, which controls various cellular functions, such as signal transduction, membrane fusion, and cell apoptosis [2][3][4] . Therefore, extensive studies have been conducted to further understand the role of asymmetric distribution in cell physiology by developing in vitro platforms to form lipid bilayer membranes with an asymmetric lipid composition 2,[5][6][7] .

In general, an asymmetric lipid bilayer is formed by assembling two independently prepared lipid monolayers 5,6 or altering the distribution of specific phospholipids using chemical or mechanical perturbation, e.g., pH gradient and osmotic pressure 2 , or using molecules that promote lipid redistribution, e.g., translocase 2 . Although these techniques are powerful for qualitative analysis of the effect of asymmetric lipid bilayers on cellular functions, it remains difficult to perform quantitative analyses because it is technically challenging to prepare asymmetric lipid bilayers with high reproducibility in a high-throughput manner given the complexity of the bilayer formation process and the size heterogeneity of the formed bilayer membranes. Thus, many aspects remain unclear with respect to the physiological role of lipid compositional asymmetry.

Recently, some techniques using micro-fabricated devices, which enable the controlled handling of less than microliter volumes of fluids, have been developed to create artificial lipid bilayers with a simple procedure 8,9 . Although these devices can form lipid bilayers with a uniform size in a high-throughput manner, it remains technically difficult to control the transbilayer distribution of phospholipids because they are based on the spontaneous assembly of lipid bilayers. In this study, we address this issue by developing a method to independently prepare two lipid monolayers in a simple manner and assemble them to form asymmetric lipid bilayers with uniform size in a high-throughput manner.

Results

Fabrication of micro device. Using conventional soft lithography, we fabricated a micro-device with more than 10,000 through-hole structures (7 fL, W 5 4 mm, and h 5 0.5 mm) on a carbon-fluorine hydrophobic polymer (CYTOP, Asahi-glass, Japan) on a hydrophilic glass substrate [9][10][11][12] (Fig. 1). Because the fabrication process was well established in our previous studies [10][11][12] , we are able to efficiently fabricate the micro-device, achieving a fabrication success rate of around 100% 8 . The through-hole structures were utilized as micro-chambers, whose orifice is suitable to mechanically support the lipid membrane because of its hydrophobicity 8 . Teflon has high hydrophobicity and has thus frequently been used as a support medium for lipid membranes in previous studies 8 . For exchange of the sample solution, a flow channel was constructed from the micro-chamber array OPEN SUBJECT AREAS:
LAB-ON-A-CHIP BIOMIMETICS
patterned on a glass substrate, a spacer sheet, and a glass block, which had an access port for sample injection (Fig. 1a).

High throughput formation of asymmetric lipid bilayers. Planar lipid membranes were formed on the top orifice of the microchambers via sequential injection of liquids from the access port (Fig. 2a). This technique is similar to the conventional technique known as the ''painting technique'', which forms lipid bilayers by deposition of lipid solution across a small aperture in a hydrophobic material 12,13 . The experimental procedures developed in this study to form asymmetric planar lipid bilayers were composed of four steps. First, an aqueous solution was infused into the flow cell. After the infusion, micro-chambers were filled with the aqueous solution. Second, a lipid solution containing 20 mg mL 21 lipid (a 1550550 (w5w5w) mixture of fluorescein-DHPE, DOPE and DOPG) in chloroform was infused to flush away the first aqueous solution. After the flushing, water-in-chloroform droplets were formed in the individual chambers. The lipid solution used in this process contained a small amount of lipid, i.e., approximately one thousandth of that used in conventional procedures to form lipid bilayers (,10 mg mL 21 ) 13-15 but almost the same amount as that previously employed for lipid monolayer formation at the interface of water-in-oil droplets (,50 mg mL 21 ) 5,16 . Therefore, lipid monolayers but not multilayers were presumably formed at the interfaces of individual droplets. Third, a second lipid solution containing 2 mg mL 21 lipid (a 151 (w5w) mixture of DOPE and DOPG) in chloroform was infused. Finally, a second aqueous solution was infused to flush the second lipid solution. In this process, the hydrocarbon tails of the residual second lipids zipped with those of the first lipids to form a bilayer membrane. Uniformsized planar bilayers with an asymmetric lipid composition where fluorescein-DHPE was located only in the inner leaflet of bilayer were accordingly formed on the opened orifice of the micro-chambers (Fig. 2b), with a formation efficiency of over 97% (number of chambers sealed with lipid membranes: 10,545 versus total number of chambers: 10,784, yielding 97.8% efficiency). Here, it should be noted that in this study, we formed lipid bilayers using chloroform as a solvent for the lipid, whereas decane or hexadecane has been used in conventional approaches 8 . The prominent feature of our approach is that the lipid membranes spontaneously thin down to form bilayers without mechanical perturbation, e.g., hydraulic pressure, which is required for bilayer formation using decane or hexadecane. This mechanical perturbation is a critical issue because it breaks more than half of the lipid membranes, thus resulting in a low efficiency of less than 30% for bilayer formation in the previous approaches 16 . The thinning of the lipid membranes is driven by drainage of the organic solvent over the hydrophobic support. Chloroform has higher water solubility than decane or hexadecane and is thus more easily dissolved in an aqueous solution, i.e., it is more easily drained over a hydrophobic support, and thus, our approach does not require any mechanical perturbation, which contributes to increasing the efficiency of bilayer formation to over 97%. Bright-field observation showed circular interference patterns in the individual chambers after lipid membrane formation (Fig. S1b), whereas a clear circular pattern was not observed before membrane formation (Fig. S1a). This interference pattern is a characteristic optical feature of thin lipid bilayer formation, termed the Plateau-Gibbs border 17,18 , which separates the thin lipid bilayer and the supporting annulus. Thus, bright-field observation supported the formation of lipid bilayers in this study. Lipid bilayer formation was further confirmed from the activity of a membrane transporter, a-hemolysin 19 (see below), which is not functional unless the lipid membrane becomes as thin as the membrane-spanning structure of a-hemolysin.

FRAP measurement. The planar lipid bilayers formed in this study were isolated and remained independent of each other, as confirmed by fluorescence recovery after photobleaching (FRAP). As mentioned above, the lipid bilayers formed in this study were composed of fluorescein-DHPE. Thus, fluorescent images of fluorescein-DHPE-containing lipid bilayers were recorded with 10-s intervals by confocal microscopy (Fig. 3). Using the laser scanning system of the confocal microscope, one of the bilayers was selectively photobleached (Fig. 3). A subsequent recording of the fluorescence intensity showed that the bleached bilayer did not recover its original fluorescence, suggesting that the fluorescein-DHPE did not laterally diffuse between bilayers within the 700-s observation period (Fig. 3). Thus, the individual lipid bilayers formed in this study were confirmed to be isolated from each other, i.e. more than 10,000 lipid bilayers were formed at a time on our micro-device.

Confirmation of lipid compositional asymmetry. To confirm the lipid compositional asymmetry between two leaflets, a pH-jump assay was conducted using fluorescein-DHPE, a pH indicator in which the fluorescence intensity decreases upon acidification (Fig. 4a). Initially, micro-chambers and an upper flow channel were filled with basic buffer (HEPES buffer, pH 8.0). Then, the buffer in the flow channel was exchanged with acidic buffer (MES buffer, pH 6.0). Before and after the buffer exchange, the fluorescent intensity of fluorescein-DHPE was stable (Fig. 4b,c), demonstrating that the hermetic seal of the micro-chamber with a lipid bilayer was sufficiently tight to prevent proton leakage, and moreover, fluorescein-DHPE was not located in the outer leaflets of the bilayers, which were exposed to the flow channel. Next, we injected an acidic buffer containing 5 mg mL 21 a-hemolysin, a toxic membrane protein that binds to lipid bilayer membranes and forms transmembrane nanopores 19 (W 5 1,2 nm) that have been used for various biomedical sensors, e.g., nanopore DNA sequencing 20 . Because a-hemolysin acts as a transporter only when the nanopore penetrates the lipid membrane, the transport activity of a-hemolysin is often used to confirm the formation of a thin lipid bilayer. Accordingly, it is highly expected that the buffer encapsulated in the micro-chambers would be exchanged with that in the flow-channel through nanopores of a-hemolysin if the lipid membranes formed in this study are thin bilayers. After injection of   the acidic buffer containing a-hemolysin, the fluorescence intensity of the entire membrane uniformly decreased toward that at pH 6 ( Fig. 4c), showing that the lipid membranes that formed in this study were thin enough to functionalize membrane protein activity, and moreover fluorescein-DHPE was uniformly located on the entire inner leaflet of bilayers. Thus, the formation of highly asymmetric bilayers was confirmed in this study.

From the pH-jump assay in the absence of a-hemolysin, we investigated the stability of the lipid compositional asymmetry over time. As shown by the gray plot in Fig. 4c, the fluorescence intensity of fluorescein-DHPE located in the inner leaflet of the bilayer remained stable for 4,000 s, which shows that flip-flop of fluorescein-DHPE in the bilayer rarely occurred for 4,000 s.

Flip-flop measurement. Instead of fluorescein-DHPE, we used NBD-DPPE to form asymmetric lipid bilayers, where NBD-DPPE is located in the inner leaflet of the bilayer. NBD-DPPE is a head-group-labelled fluorescent lipid that has been widely used as a model lipid for flip-flop measurement [21][22][23] (Fig. 5a).   To visualize the flip-flop of NBD-DPPE, we performed the NBDreduction assay using dithionite, which rapidly quenches the fluorescence of NBD 21 ( Supplementary Information). Initially, the micro-chambers and an upper flow channel were filled with buffer (MOPS buffer, pH 7.0). Then, the buffer in the flow channel was exchanged with the buffer containing 50 mM dithionite. Because dithionite does not permeate through lipid membranes on our system for 7 h (Supplementary Information), the fluorescence of NBD-DPPE will be quenched only when NBD-DPPE is exposed to upper flow channel upon flip-flop. After the buffer exchange, the fluorescent intensity of NBD-DPPE was gradually and exponentially decreased with a time constant of 4.1 h (Fig. 5b,c), which was similar to previous findings in liposomes 21 . These findings suggest that the fluidity of the lipid in our device, which affects the flip-flop rate, is almost the same as that in liposomes.

Discussions

In this study, we succeeded in demonstrating real-time flip-flop measurement in planar lipid bilayers, which has been difficult in other membrane systems because of the fast leakage of dithionite, i.e. dithionite can cross the lipid bilayer within an hour 21 . As mentioned above, our system achieves tight sealing of lipid bilayers, which prevents dithionite leakage across the bilayer for seven hours (Supplementary Information), and therefore, allows us to conduct real-time monitoring of lipid flip-flop in a single planar lipid membrane by using NBD-labelled phospholipid. Real-time monitoring of flip-flop in the single lipid membrane may have the versatile uses in various lipid and membrane protein assays, such as single molecule analysis of flippase activity, and thus increase the understanding of the lipid flip-flop mechanism.

We constructed a micro-device for high throughput formation of asymmetric planar lipid-bilayers in micro-chamber arrays. Micro-chamber arrays have recently enabled the development of highly sensitive and quantitative biological assays, such as singlemolecule enzymatic assays, digital PCR, and digital ELISA 9,10,24-26 . Thus, the micro-device developed in this study has a large variety of potential applications, including highly sensitive analysis of membrane proteins on asymmetric lipid bilayers, which would enable further understanding of the role of lipid compositional asymmetry in the function of membrane proteins, which are optimal pharmaco-logical targets. Additionally, the high-throughput feature of asymmetric bilayer formation in our micro-device may enable screening of pharmacological effects on membrane proteins located in the asymmetric lipid bilayers in vitro.

Methods

Microfabrication. We fabricated hydrophobic through-hole structures on a hydrophilic glass substrate using conventional photolithography. A carbon-fluorine Intensity (AU)   hydrophobic polymer (CYTOP, Asahi-glass, Japan) was spin-coated on a clean cover glass (32 mm 3 24 mm) at 4,000 rpm for 30 s and then baked for 1 h at 180uC. The thickness of the CYTOP layer was 0.5 mm. Photolithography was conducted using a positive photoresist (AZP4903, AZ Electronic Materials, Japan) to pattern mask structures on the CYTOP layer. The resist-patterned substrate was dry-etched with O 2 plasma using a reactive ion etching system (RIE-10NR, Samco, Japan) to expose an array of hydrophilic SiO 2 on the glass surface. The substrate was then cleaned and rinsed with acetone and ethanol to remove the photoresist layer remaining on the substrate. Figure 1 depicts a fabricated device, where the diameter of the CYTOP well is 4 mm.

Imaging. Fluorescent time-lapse recordings were taken under a confocal microscope system with a 603 objective lens and photomultiplier tubes (A1R, Nikon, Japan). A laser was used for the excitation of fluorescein-DHPE (l ex 5 488 nm). The fluorescence intensity was recorded through a 603 objective lens with photomultiplier tubes (Nikon, Japan). Analysis was performed using NIS Elements software (Nikon, Japan). Bright-field images of lipid membrane formation were taken under an optical microscope with a 1003 objective lens (IX 71, Olympus, Japan).

Photobleaching assay. The flow cell and micro-chambers were filled with buffer A (50 mM HEPES [pH 8.0]). Using the laser-scanning system of the confocal microscope, one of the chambers was photobleached for a few seconds. The fluorescent images of fluorescein-DHPE were recorded at 10-s intervals.

pH-jump assay. The flow cell and microchambers were filled with buffer A. After lipid membrane formation, the buffer in the flow cell was exchanged to buffer B (50 mM MES [pH 6.0]). After a certain period had elapsed, the buffer in the flow cell was exchanged to buffer B containing 5 mg mL 21 a-hemolysin (Sigma Aldrich, USA). Fluorescence images of fluorescein-DHPE composing lipid bilayers were recorded at 60-s intervals.

NBD-reduction assay. The flow cell and microchambers were filled with buffer C (500 mM MOPS [pH7.0]). After lipid membrane formation, the buffer in the flow cell was exchanged to buffer C containing 50 mM dithionite. Fluorescence images of NBD-DPPE composing lipid bilayers were recorded at 5-min intervals.

Figure 1 |
1Micro-chamber arrays for asymmetric lipid bilayer formation.

micro-device where the substrate of hydrophilic-inhydrophobic structures is assembled with cover glass that has an access port for sample injection. (b) Bright field image of the through-hole structures of the carbon-fluorine hydrophobic polymer on a hydrophilic glass substrate. (c) Illustration of a fabricated structure (diameter, 4 mm; thickness, 500 nm).

Figure 2 |
2Formation of lipid bilayer membrane with an asymmetric lipid composition. (a) Schematic illustration of asymmetric lipid bilayer formation.First, an aqueous solution is injected into the device through an access port. Second, the lipid solution containing fluorescent-labeled lipids (fluorescein-DHPE) is injected. Third, another lipid solution without fluorescein-DHPE is injected. Finally, a second aqueous solution is injected to flush the second lipid solution. In these processes, asymmetric lipid bilayers are formed on the orifices of micro-chambers. (b) Fluorescent image of fluorescein-DHPE composing asymmetric lipid bilayers.

Figure 3 |
3FRAP measurement of fluorescein-DHPE. Representative time courses of the fluorescent intensity of fluorescein-DHPE-containing lipid bilayers (gray) and their average (green). The fluorescent intensity was normalized against that at 0 s. The upper panels show fluorescent images of fluorescein-DHPE before and after photobleaching.

Figure 4 |
4Demonstration of lipid compositional asymmetry in the two leaflets. (a) Fluorescence intensity of fluorescein-DHPE versus pH. (b) Schematic illustration of pH-jump assay. First, micro-chambers and an upper flow channel are filled with basic buffer (pH 8). Second, the buffer in the flow channel is exchanged with an acidic buffer (pH 6). Third, the a-hemolysin dissolved in acidic buffer is injected into the flow channel. During these processes, we measured the fluorescence intensity of fluorescein-DHPE located in the bilayers. (c) Time course of fluorescence intensity of fluorescein-DHPE in bilayers. Green or grey plots represent the fluorescence intensity in the presence or absence of a-hemolysin after 2000 s. Insets show fluorescent images of fluorescein-DHPE in the representative micro-chamber.www.nature.com/scientificreports SCIENTIFIC REPORTS | 4 : 7076 | DOI: 10.1038/srep07076

Figure 5 |
5Flip-flop measurement using NBD-DPPE. (a) Quenching of NBD-DPPE resulting from reduction by dithionite. (b) Schematic illustration of NBD-reduction assay. First, micro-chambers and an upper flow channel are filled with buffer (pH 7). Second, the buffer in the flow channel is exchanged with buffer containing 50 mM dithionite. During these processes, we measured the fluorescence intensity of NBD-DPPE located in the bilayers. (c) Time course of fluorescence intensity of NBD-DPPE in bilayers. Green or grey plots represent the fluorescence intensity in the presence or absence of 50 mM dithionite after 40 min, respectively. The orange line represents the fitting curve based on a single-order reaction scheme with a time constant of 4.1 h. Insets show fluorescent images of NBD-DPPE in the representative micro-chamber.

www.nature.com/scientificreports SCIENTIFIC REPORTS | 4 : 7076 | DOI: 10.1038/srep07076

www.nature.com/scientificreports SCIENTIFIC REPORTS | 4 : 7076 | DOI: 10.1038/srep07076
SCIENTIFIC REPORTS | 4 : 7076 | DOI: 10.1038/srep07076
AcknowledgmentsWe thank all members of the Noji Laboratory. This work was supported by Grant-in-Aid for Scientific Research no. 18074005 to H.N. and no. 30540108 to R.W. from the Ministry of Education, Culture, Sports, Science and Technology, Japan, and Precursory Research for Embryonic Science to R.W. from the Japan Science and Technology Agency.Author contributionsR.W. designed and performed experiments and analyzed data; N.S. and T.Y. gave technical supports; H.N. designed experiments, built whole story and wrote papers with R.W.Additional informationSupplementary information accompanies this paper at http://www.nature.com/ scientificreports Competing financial interests: The authors declare no competing financial interests.
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Can public report cards affect consumer's private learning of quality? An evaluation of the impact of Florida hospital report cards on acute myocardial infarction patient admission patterns

Vivian Y Wu firstname.lastname@example.com
Sol Price School of Public Policy
Schaeffer Center for Health Policy and Economics
University of Southern California
Los Ange-lesUSA

Pai Liu
Palo Alto Research Center
Palo AltoUSA

Can public report cards affect consumer's private learning of quality? An evaluation of the impact of Florida hospital report cards on acute myocardial infarction patient admission patterns
C5E5368C1E829D7A829C2B46622AE5ED10.4236/health.2013.512297Received 9 November 2013; revised 15 December 2013; accepted 21 December 2013Hospital Report CardsHospital Admission PatternPrivate Learning
The empirical literature on hospital report cards typically assumes that report card cannot interact with consumer's private learning.This study examines the impact of the implementation of FL hospital quality reporting system in late 2004 on hospital admission patterns using a pre-post difference-in-difference design.The estimation model allows for the possibility that report-card learning may interact with non-report-card learning.The study sample is comprised of all patients admitted to any FL hospital between 2000 and 2008 with a principal diagnosis of acute myocardial infarction (AMI).We find that hospital admission patterns for AMI patients did not respond to report card information.However, we find evidence consistent with the possibility that the implementation of a report card system may stimulate consumers (either patients or physicians) to seek higher quality hospitals through private information channels.

INTRODUCTION

For more than two decades, there have been interests to provide comparative provider quality information to consumers to aid consumer choices based on quality of care.To date, the US federal government, more than 30 states, and many private organizations have initiated some forms of report cards that publicly disclose selected financial, process, volume, and outcomes data on health plans, hospitals, and physicians.In principle, report cards can improve market performance in two ways.First, report cards can generate patient (demand) responses, by bringing critical information to patients to identify and switch to high quality providers.Assuming that patients would "vote with their feet," providers are incentivized to improve quality in order to subsequently attract higher demand.The second mechanism is provider responses.Even when there are no demand responses, report cards may still generate incentives for providers to improve quality if providers care about their performance relative to the peers [1].

Empirically, there is a large volume of literature examining the effect of releasing provider report cards on patient volume or provider choices.The main challenge of the literature is that most studies did not distinguish between consumer's learning from report cards and that from non-report-card mechanisms that would occur independently of report cards [2][3][4].While many other studies did either control for the effect of private learning or estimate the magnitude of it [5][6][7][8][9][10][11][12], the main issue with this set of studies is that they commonly assume that consumer learning from public report cards is independent of that from private channels, which may be not plausible.

This study focuses on testing whether report cards may interact with consumer's private learning of quality, instead of assuming that they are independent from each other.Conceptually, it is unclear in which direction private learning should be affected by the release of report cards.For example, report cards may decrease the effec-tiveness of private learning, if report cards provide information that people would otherwise have to rely on private channels prior to the release of report cards.Assuming that public information is less costly, report cards thus may substitute away the need and usefulness of private information.On the other hand, report cards may increase private learning of provider quality.For example, the publicity that came with the implementation of report cards can increase public awareness and demand for provider quality.If the demand for quality increases, while critical information is not completely released on the report cards, consumers may turn to seek more information from private channels, or doctors may be pressured to better respond to quality in their referral decisions.This will result in an increase in the effect of private quality information on provider choice.Lastly, it is still possible that report cards may have no effect on private learning, if report cards simply do not affect consumer knowledge or behavior in any dimension.In any case, it would be problematic to assume the private learning is unaffected by report cards without testing it directly.

Our study addresses this gap in the literature by examining, specifically, whether consumer learning of hospital quality may be substituted away or enhanced by the release of report cards in the hospital setting.In this study, we term the learning of all possible channels other than the information from report cards as "non-reportcard learning".To our knowledge, only one existing study, while not its main focus, allows non-report-card learning to interact with report card learning in the fertility clinic setting [13].In that study, the 2 quality measures for quality, reported and non-reported, were highly correlated at 0.59, because one is the 3-year lag of another, and thus the estimates may not be stable.We improve the prior study by using 2 different quality measures to better separate out report-card vs non-report-card learning and testing them explicitly in a different hospital setting.The second contribution of our study is that we analyze the effect of report cards on physicians' admission patterns.Taking advantage of the study data that allow us to track the same physician over time, we examine how a physician's referral pattern across hospitals changes before and after the release of report cards using a difference-in-difference estimate at the physician level.Lastly, we analyze the effect of an aggregated 3-category quality ranking, rather than detailed numeric quality measures.The prior reporting method is of particular importance, as many states and the Center of Medicare and Medicaid Services that adopted hospital report cards use the 3-category reporting-"better than expected/above average," "as expected/same as the average," and "worse than expected/below average".It is unclear whether the highly simplified information is useful to patients and there are relatively less empirical studies testing the effect of the simplified reporting.

METHODS

The study uses a pre-post difference-in-difference model to estimate separate effect of quality information on physician admission patterns using the unique information contained in the FL report card system.The state of Florida passed the Affordable Health Care for Floridians Act in 2004, requiring the Agency for Health Care Administration (AHCA) to "offer information about health care services, costs, and quality of care, including hospitals, nursing homes, ambulatory surgery centers, pharmaceuticals, health plans and physicians".Beginning in November of 2004, the agency launched a new website, FloridaCompareCare.gov, to publish the cost and process indicators for over 150 medical conditions, surgeries, and procedures.For hospitals, the data also includes infection/complication rates and mortality rates for several common diseases/conditions.The implementation of the reporting system was a major undertaking at the AHCA with widely disseminated publicity.

The critical information that enables the present study is 2 distinct hospital quality measures available in the system: one aggregated 3-catogory quality ranking that was made available to public by report cards, and another detailed hospital-level actual risk-adjusted quality score that was never disclosed to the consumers but available to researchers before and after the report cards.This unique information allows us not only to use the reported quality ranking to estimate demand responses due to report cards, but also to control and estimate the change in demand response to non-report-card learning as a result of report cards.

The empirical difference-in-difference model is specified as in the following:
                   (1)

Dependent Variable

We use the percent distribution of a physician's AMI patients across hospitals to describe a physician's admission pattern.In other words, the dependent variable measures the percent AMI patients coming from a physician at a hospital in a given year.For example, if a physician has a total of 50 AMI patients in a year, and sends 20 patients to Hospital A and 30 to Hospital B. The dependent variable will record 40% for Hospital A and 60% for Hospital B for this particular physician.We use the percentage term instead of the absolute number of cases to adjust for the scale differences between physicians, and within a physician over time (which can be changing at different rates).Analyzing volumes at the physician-hospital level provides another advantage that the well-known volume-outcome endogeneity will be less of a concern at the physician-hospital level.Higher hospital quality may attract physicians to send more patients to that hospital, but a single physician's volume at a hospital is less likely to be large enough to drive a hospital's overall quality outcomes (with few extremely highvolume exceptions).

Key Independent Variables

The key independent variables of interest are "better/ worse" and "risk-adjusted mortality"."Better" is an indicator variable which takes on the value of 1 if a hospital was ranked as "better than expected" in the prior year's report card, and "Worse" indicates those hospitals that ranked "worse than expected," respectively.Coefficient α indicatesa change in hospital admissions to hospital h per physician p due to a change in hospital h's ranking in the report card to be "better" than expected, and coefficient β measures the effect of a "worse" report card ranking.The reference group is hospitals whose AMI mortality rate was "as expected."Each quarter beginning the third quarter of 2004, the Florida AHCA calculated a hospital's expected risk-adjusted mortality rateusing the AHRQ's IQI tools with FL hospital discharge data.It then ranked hospitals into three categories: 1) better than expected, if the actual mortality rate is lower than expected, 2) as expected, if the actual mortality rate is within the expected range, and 3) worse than expected, if the actual mortality rate is higher than expected, and disclose only the 3 categories in their public reporting.Hospitals with too few cases in the condition will not receive a ranking.We obtain the actual reported data (three categories) from the agency directly between 2004 (4 th quarter) and 2008 yearly and these are entered as 1-year lag in the regressions to avoid volume-outcome endogeneity.

γ and  are the other 2 coefficients of interests.The variable "risk adjusted mortality" was the calculated but unreported hospital-specific risk-adjusted AMI mortality serving as a proxy of a hospital's underlying quality.2001-2004 was defined as the pre-report card period, and 2005-2008 as the post-report card period.Coefficient γ identifies the relationship between private knowledge of hospital quality (observable to consumers but unobservable to researchers) and admission patterns in the prereport card period, and coefficient  captures whether this relationship changed in the post report card period.The critical assumption is that because report cards never report the detailed, hospital-level quality score, any learning about the hospital-level quality has to come from non-report-card learning.We reproduce AMI risk-adjusted mortality rates using the same public-available AHRQ IQI tool 1 , because the Agency was unable to share their calculated risk-adjusted mortality rates.AMI risk-adjusted mortality rates were also entered as 1-year lag.

Control Variables

Distance is calculated by vincenty method, which calculates the distance between two points on the surface of a spheroid using zip codes.We included 2 distance measures.The first is the distance between the referring physician's office to the hospital where an admission occurs, which measures the preference/convenience of the referring physician.The second measure is the conventional travel distance between a patient's residence and the admitted hospital, which measures a patient's willingness to travel.It is worth noting that we cannot decompose changes in travel distance as pure physician's or patient's response.Patients could be switching to physicians who are willing to admit them to desired hospitals, or changes could come from physicians redirecting patients to certain hospitals.In this study throughout we treat any observed volume change as the result of joint physicianpatient responses.

Control variables also include several sets of fixed effects (FE).It can be argued that some hospital factors not observed by the researchers may be observable to physicians/patients.For example, a physician can learn from experience the true productivity level of a hospital that is not directly observed by hospital characteristics.If this underlying productivity is correlated with patient outcomes, then the estimates on the effect of "better" or "worse" are biased.We use a stronger hospital FE specification that controls for all fixed hospital characters that may be correlated with the level or change in hospital quality.Lastly, year fixed effects are used to control for year-specific time trends that affect all hospitals/providers commonly.

We use three specifications for regression model.The first is the hospital fixed effects model as described above.In the second specification, we also include physician FE in addition to the hospital FE to control for specific time-invariant provider characteristics that may influence a physician's choice of hospital.This specification uses the change in "better" or "worse" ranking to identify its effect on the change in physician level AMI volume.In the third and strongest specification, we re- 1 The old V.3.0 version.place hospital FE and physician FE with physician-hospital pair FE.The physician-hospital pair FE essentially removes all the possible confounding variations between hospitals for a given physician, and uses only withinphysician and within-hospital variation over time to identify the effect of a change in report card ranking on a physician's admission to that hospital.

Alternative Model

To better understand the timing of the effect of report cards, we replace the post period dummy in the main regression model with year FE and interact the unreported AMI risk-adjusted mortality with year FE:
                    (2)
where "RsikAdjusted Mortality" again are the calculated but unreported risk-adjusted AMI mortality of a hospital in the previous year, which is interacted with the year FE from year 2002 to 2008, where 2001 is the reference year.The set of coefficients γ k will inform whether there is any systematic change between unobserved hospital quality and admission patterns over time, and how these changes may be correlated with the timing of the release of report cards.The regression model in Equation ( 2) is identical to the main model in Equation ( 1), except for the change of time period dummy in the interaction terms.We also used three specifications for Equation (2) as did for Equation (1)-one used hospital FE only, another used hospital FE and physician FE, and a third one used physician-hospital pair FE.

DATA

We obtain FL hospital discharge data from FL AHCA between 2000 and 2008.This data set contains the whole universe of inpatient discharges occurred during 2000 to 2008 at any hospital in FL.Data on hospital ranking as appeared on the report cards, starting from the fourth quarter of 2004 to the last quarter of 2008 was merged to the main discharge data, by 1-year lag.Hospital characteristics are obtained from the American Hospital Association Annual Surveys.Additional information on physician office address comes from Medical Quality Assurance Services of Florida Department of Health, and is linked to the master data by physician license number.Hospital-level risk-adjusted mortality rates are computed using the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators (IQI) tool, which is the tool FL AHCA uses to generate the 3-category of AMI mortality ranking (higher than expected, as expected, and lower than expected).

Overall, there are 336,142 AMI discharges occurring at about 200 hospitals between 2001 and 2008.We limit the study period to 2001 and 2008 due to the use of one-year lag variables.There may be concerns that some doctors may not routinely admit AMI patients, and thus whose admitting patterns can skew the estimates.Hence, we restrict the sample further to physicians who specialize in Internal Medicine, Family Medicine, or Cardiac Surgeons.This exclusion eliminates about 19% of the total hospital-year observations.In addition, AMI patients do not travel far.We exclude admissions that have long travel distances, measured as the distance between the zip code of admitted hospital or admitting physician's office and patient's home being greater than 50 miles or the admissions with any of zip code information missing.This drops out 20% observations.Furthermore, we exclude hospitals with less than 20 AMI cases per year and physicians who have only one-year records.These procedures together exclude another 2%.Our final sample includes 206,515 AMI admissions in 2001-2008 for the regression analysis.

RESULTS

Table 1 showed the descriptive statistics of the study sample.The number of AMI patients in FL was on a steady decline, from 27,370 in 2001 to 24,209 in 2008.Risk-adjusted mortality rates were declining as well.Interestingly, the number of physicians treating AMI patients also declined over time.The statistics in the last 2 columns indicated that the number of hospitals used by a physician declined from an average of 1.56 hospitals in 2001 to 1.36 hospitals in 2008, suggesting that physicians were narrowing their hospital referral network for AMI patients.

Table 2 reported the results using the main regression model in Equation ( 1) to examine the effect of report card information vs non-report-card information on a physician's admission pattern.The first column controlled the hospital FE, the second column controlled the hospital FE and physician FE, and the third column controlled the physician-hospital pair FE.All the three columns have year FE.The results of the three columns reported a consistent finding.The coefficients of "better" and "worse" than expected mortality were not statistically significant, indicating that there was no direct effect of report card ranking on physicians' admission patterns.The coefficients of "risk-adjusted mortality" in the prereport period, in years 2001-2004, were all positive and insignificant in Columns 1 and 3.In Column 2, there was evidence that hospitals with higher AMI mortality would actually had greater admissions from a physician.This relationship was reversed in the post report-card period when a physician's referral patterns began to respond to quality.Specifically, in Column 1, 1 percentage point decrease in AMI risk-adjusted mortality in prior year lead to 45 percentage points increase in the percent of AMI admissions received from a physician.When we removed variation between physicians in Column 2 to identify the relationship using the variation from changes within a physician over time, the magnitude reduces to 36.6 percentage points.In Column 3, our preferred specification, where we removed all the variation between physician-hospital pairs, the magnitude further reduces to 27 percentage points but remained statistically significant.Overall, there results across the columns provided consistent evidence that, coincidental with the implementation of the FL report card, hospitals with higher quality began to be associated with more AMI admissions from physicians.Because the hospital-level detailed quality information could not have been learned from the report cards themselves, the change had to be coming from nonreport-card learning.Table 3 presented the results of identical models as Table 2, except for that the unreported risk-adjusted mortality rates were interacted with a dummy variable for each year between 2002 and 2008, using the first year, 2001, as the reference year.The FE specifications for each column were the same as those in Table 2. Consistent with the results in Table 2, the coefficients of "better" and "worse" than expected mortality were still not significant.The coefficients of yearly risk-adjusted mortality rate further supported that report cards may have an effect on non-report-card learning.Before 2005 when the report cards were unavailable, the unreported risk-adjusted mortality had no statistically significant relation with physician admission patterns.In year 2004 when the report card system was launched (in November), physician admissions for AMI patients began to respond to the undisclosed AMI risk-adjusted mortality.The magnitude of change was strongest in 2005, the first year after the full implementation of FL public reporting system, and the effect persisted in the following 2 years.However, the effect deteriorated over time, and by 2008 the effect was gone.The fact that the timing of responses was so closely associated with the timing of the implementation of report cards suggested that the changes in hospital admission patterns were likely caused by the release of report cards.

Sensitivity Analysis

We conducted several sensitivity analyses to examine stability of the findings.First, we hypothesize that if the change in AMI admission patterns were due to the re-lease of AMI quality reports, the change in AMI admission patterns should only be related to AMI quality but not other measures.We test the hypothesis by choosing several unreported process/outcomes measures for conditions that are irrelevant to AMI.Specifically, we regress physicians' AMI admission patterns on risk-adjusted mortality rates for GI hemorrhage, pancreatic cancer, and pneumonia, and delivery rate for cesarean section.These conditions were chosen because they have sufficient observations across hospitals and can be calculated using the same AHRQ IQI tools.The results (not presented, but available upon request) showed no clear association between AMI admissions and these other process/outcomes measure, supporting our hypothesis.

Secondly, we vary the sample exclusion criteria to be more/less restrictive.Restricting traveling distance to 30 -50 miles provided qualitative similar findings.We also included all physicians who had valid zip code to compute distance but did not specialize in Internal Medicine, Family Medicine, or Cardiac Surgeon; the results were consistent while the magnitude of effect was smaller.Overall, the findings are not sensitive to these changes in sample exclusion criteria.

DISCUSSION

Our study provides 2 important empirical findings.First, we find that aggregated reporting, i.e. ranking a hospital's quality (mortality) in "better", "as expected", or "worse" 3 categories, does not appear to affect hospital admission patterns significantly.This finding is consistent with most existing studies which found limited or no demand response to disclosed quality information.While this study does not test the underlying reason(s) directly, we think that the lack of a potential effect could be related to the usefulness of the disclosed information.Aggregated reporting may ease the mental demand for consumers to understand and compare quality across hospitals than the more complicated hospital-level information.However, the downside is that while the 3 categories may help consumers to identify the best and worst, such information may not be useful in terms of actually helping consumers to decide which hospital to go to.Furthermore, if consumers already have proxies for the best and worst hospitals, such as the teaching status, size, location, and reputation of a hospital, aggregated ranking may provide little new information to consumers and thus do not affect behavior.

Our second and the most interesting finding is that patients and physicians do have private information channel, and that non-report-card learning may be affected by the implementation of report cards.We found that physicians' admission patterns responded to hospital quality after the implementation of the FL reporting system, at the detailed hospital-level, but such detailed information was never available in the public reporting.In addition, the response was strongest within the first 3 years after the release of report cards.This finding supports what emphasized in several studies that there is a substantial amount of private learning [5,11].But our study provides new findings consistent with the possibility that non-report-card learning can be facilitated by report cards, although the effect lasted only for a few years.

We think that the responses to quality could be coming from patients and/or physicians.Non-report-card learning about hospital quality is improved if patients are better educated about the variation in quality among hospitals, and the importance to seek and select quality hospitals as a result of report cards.Demand response to undisclosed quality can be enhanced due to change in physicians' behavior as well.Doctors presumably are much more knowledgeable about a hospital's true quality [14].However, they may not always admit patients to hospitals of the best quality due to a contractual arrangement, convenience to the physicians, or other factors as found in the literature [15].With the fact that report cards' facilitating increased public awareness and scrutiny about provider quality, physicians may be pressured to start to admit patients according to quality more than other factors at the margin.Our present setting does not allow us to distinguish whether the non-report-card responses came from the patients or physicians, and we leave this as an important area for future research.Our findings also suggest that consumers may not have problems comprehending the more detailed hospital-level quality information.

To get a sense of the magnitude of the demand response, we calculate how a one standard deviation decrease in average risk-adjusted mortality, or by 0.03 percentage points, will affect the number of AMI cases at an average hospital.Using our most conservative estimate in Table 2 Column 3, a 0.03 percentage point decrease in mortality will lead to a 0.81 (0.81 = 0.03 × 27) percentage point increase in AMI patient distribution per physician, or about 0.6 AMI patient.While this number may seem small, this amount to an increase of total 19 AMI cases for an average hospital that has 32 admitting physicians, implies an 11% (11% = 19/174 average number of AMI cases in 2004) increase in total AMI patient volume.This calculation is meant to illustrate an important demand response due to report cards that are often not considered or captured correctly in most existing studies.When non-report-card learning is not controlled in addition to public report card effect, the estimated demand response is biased.When non-report-card learning is controlled but its interaction with report card is not allowed, the results are biased as well.

There are several limitations of the study.First, our study is limited to just one state and one condition.Second, the lack of findings in responses to reported ranking may be related to data limitation.Only 8% -10% hospitals were identified as "better" or "worse" each year and the movement between the categories was limited, so that it is harder to precisely identify an effect with the fixed effect model.Third, our results could be caused by some co-temporal factors that occurred around 2005 and were not controlled in the model.However, we are unaware of any special event in 2005 that would significantly affect AMI patient's hospital admissions other than the FL report card implementation.Forth, while our results suggest a strong correlation that non-report-card learning can be enhanced by report cards, our study does not provide evidence with regard to the actual mechanism that how report cards change various channels of private learning.With these caveats in mind, our study still points to an important aspect that future models of consumer learning should consider the interaction between informational channels.

Table 1 .
1
Distribution and concentration of AMI patients across physicians and hospitals.
YearAMI CasesRisk-Adjusted AMI Mortality Rate (t-1)Number of PhysiciansNumber of HospitalsMean # of Hospitals Used per Physician200127,3700.1238871621.56200227,0300.1239591581.50200327,3810.1139951611.47200426,6470.1139981531.42200526,4740.1039101601.40200624,7940.0938441551.37200724,6920.1037181551.35200824,2090.0935301561.36

Table 2 .
2
Relationship between report card better/worse ranking and hospital admission patterns, 2001-2008.
Dependent Variable: Percent of AMI Patients at a Hospital(1)(2)(3)Better than Expected (1-Year Lag)1.658 (1.526)−0.447 (0.970)0.336 (0.861)Worse than Expected (1-Year Lag)0.555 (1.467)−0.675 (0.909)−0.365 (0.798)Risk-Adjusted Mortality (1-Year Lag)13.101 (11.876)19.432 * (7.555)12.288 (6.825)Risk-Adjusted Mortality (1-Year Lag) * 2005-2008−45.336 ** (16.745)−36.609 ** (11.252)−27.247 ** (10.128)Distance between Physician and Hospital−0.794 ** (0.127)−1.215 ** (0.123)Distance between Physician and Hospital * 2005-2008−0.010 (0.114)−0.018 (0.076)−0.063 (0.069)Distance between Patient and Hospital0.0210.055 **0.021 * (0.009)(0.038)(0.017)Distance between Patient and Hospital * 2005-2008−0.024 (0.037)−0.054 ** (0.021)−0.018 (0.014)Year FEYesYesYesHospital FEYesYes-Physician FE-Yes-Physician-Hospital Pair FE--YesObservations206,515206,515206,515R-Squared0.270.630.81
Robust standard errors clustered at physician, hospital and physician-hospital pair level in each column respectively pair in parentheses.* Significant at 5%; ** Significant at 1%.

Table 3 .
3
Relationship between report card better/worse ranking and hospital admission patterns, 2001-2008.
Dependent Variable: Percent of AMI Patients at a Hospital(1)(2)(3)Better than Expected (1-Year Lag)1.679 (1.604)−0.482 (1.015)0.460 (0.899)Worse than Expected (1-Year Lag)0.391 (1.477)−0.729 (0.925)−0.515 (0.811)Risk-Adjusted Mortality (1-Year Lag)14.545 (20.639)25.125 * (12.583)23.732 * (11.299)Risk-Adjusted Mortality (1-Year Lag) * 2002−8.413 (20.305)5.283 (14.687)−2.929 (13.649)Risk-Adjusted Mortality (1-Year Lag) * 2003−12.299 (23.570)−11.814 (15.393)−15.017 (13.876)Risk-Adjusted Mortality (1-Year Lag) * 200415.803 (25.272)−17.960 (17.036)−30.856* (14.961)Risk-Adjusted Mortality (1-Year Lag) * 2005−51.660 * (24.142)−49.134** (17.246)−46.335** (15.272)Risk-Adjusted Mortality (1-Year Lag) * 2006−41.240 (29.194)−34.142 (17.768)−39.218 * (15.804)Risk-Adjusted Mortality (1-Year Lag) * 2007−53.953 (28.215)−45.000 * (20.132)−40.091 * (17.874)Risk-Adjusted Mortality (1-Year Lag) * 2008−34.963 (33.677)−39.640 (20.907)−25.537 (18.846)Distance between Physician Hospital−0.795 ** (0.127)−1.216 ** (0.123)Distance between Physician and Hospital * 2005-2008−0.009 (0.113)−0.017 (0.076)−0.061 (0.069)Distance between Patient and Hospital0.021 (0.038)0.055 ** (0.017)0.020 * (0.009)Distance between Patient and Hospital * 2005-2008−0.023 (0.036)−0.052 * (0.021)−0.017 (0.014)Year FEYesYesYesHospital FEYesYes-Physician FE-Yes-Physician-Hospital Pair FE--YesObservations206,515206,515206,515R-Squared0.270.630.81
Robust standard errors clustered at physician, hospital and physician-hospital pair level in each column respectively pair in parentheses.* Significant at 5%; ** Significant at 1%.

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Forest Biomass Management Challenges in Commercially Exotic Tree Plantation Areas: A Case Study from the Rungwe Volcanic Province (Southern Highlands of Tanzania)
2017

Benard Mwakisunga
Department of Environmental Health Sciences
Ruaha Catholic University
IringaTanzania

Forest Biomass Management Challenges in Commercially Exotic Tree Plantation Areas: A Case Study from the Rungwe Volcanic Province (Southern Highlands of Tanzania)

Journal of Geoscience and Environment Protection
5201710.4236/gep.2017.53006Received: December 22, 2016 Accepted: February 17, 2017This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).Forest ReserveIndigenous TreesExotic TreesBiomass VariationHuman Encroachment
The carbon (C) stored in the living biomass of trees is typically the largest C pool of the forest ecosystem which is directly impacted by deforestation and degradation. With the global efforts to combat climate change through a forestation and reforestation to reduce carbon emissions in the atmosphere, natural forests conservation and tree planting programmes are highly emphasized in developing countries for both commercial purposes and carbon emissions reduction. Though most of the trees planted are exotic trees especially Pinus patula, Eucalyptus saligina and partly Cypress sp. with some fruit and shade How to cite this paper: Mwakisunga, B. (2017) Forest Biomass Management Challenges in Commercially Exotic Tree Plantation Areas: A Case Study from the Rungwe Volcanic Province (Southern Highlands of Tanzania). Journal of Geoscience and Environment Protection, 5, 67-75.

trees in towns. When comparing survival and carbon content of exotic and indigenous trees grown, exotic trees have high initial biomass and growth rate while indigenous trees perform better in survival rates; also indigenous forest sequesters more above ground biomass (C) and soil carbon than exotic plantations. Hence embarking on clearance of forest's indigenous trees for plantation of commercial exotic trees slightly slows down the efforts of carbon emission reduction and mitigation of climate change through enhancement of carbon sequesters. Therefore, this calls for the need to assess how individual tree planting programs lead into natural forest's encroachment. The study was conducted at Mbeya One ward lying between Mporoto and Rungwe forest reserves in Mbeya rural district, in the Southern highlands of Tanzania. The main objective was 1) to assess the indigenous tree biomass variation between Mporoto and Rungwe forest reserves; 2) to assess the exotic tree biomass variation between the two forest reserves; and 3) to assess the human implication on aboveground biomass variation between the two forest reserves. The findings indicated significant decrease in indigenous trees biomass in residential and crop land areas with a hasty increase in biomass when just reaching Mporoto forest reserve indicating little human encroachment in the forest re-serve. There was the same trend towards Rungwe forest reserve; however, there was a slight increase in indigenous tree biomass when reaching forest reserve signing presence of human encroachment in the forest reserve. The main human activities observed in the reserve were: timber harvesting and commercial exotic trees planting (especially the commercial trees, Pinus patula). The trend was opposite for the exotic trees especially for Pinus patula and Eucalyptus sp. in the study area. Hence the study concludes that there is a significant variation between indigenous and exotic trees in the study area, hence the variation in the tree biomass. There is also a massive human encroachment for indigenous trees clearance in expense of exotic trees plantations towards and in Rungwe forest reserve. Therefore, the study would like to call for an urgent intervention especially in the east side of the study area (Rungwe forest reserve) stopping exotic tree plantation penetrating into the forest reserve which intensifies cutting down of indigenous trees in the forest reserves plummeting above ground biomass and escalating carbon emissions in the atmosphere while jeopardizing the natural forest ecosystem services to the communities. Conservation education should be emphasized in the study area to local communities, exotic trees plantations owners and other relevant stakeholders.

Introduction

Most of the tropical natural forests have been for centuries playing key roles in traditional, socio-economic and ecological wellbeing of most of the communities living around forests and the country at large, through provision of livelihoods and ecological services [1]. However the actions of accruing these services augmented with increase in population have resulted in forest deforestation and degradation. Forest deforestation and degradation have mainly been caused by tree harvesting for timber, logging, agricultural expansion and firewood collection with its negative impacts on species diversity and forest tree composition and carbon quantities.

The carbon (C) stored in the living biomass of trees is typically the largest C pool of the forest ecosystem which is directly impacted by deforestation and degradation [2]. The relationships between diversity, biomass and C stocks at varied altitudes can have crucial implications for the management and conservation of C sinks [abid]. Natural tropical forests have been for centuries playing a key role in carbon sequestration from the atmosphere hence functioning as carbon sinks and helping to balance the concentration of CO 2 in the atmosphere. But with the expansion in trade, industries and agriculture, most of the forest reserves have been facing deforestation through illegal logging, timber harvesting, plantations of exotic trees and expansion of agriculture in natural intact fo-rests. Making deforestation the second largest human induced source of carbon dioxide in the atmosphere [3] [4].

Global instruments to reward countries embarking on reduced deforestation and degradation (REDD) and application of sustainable management of forest services (REDD+) to enhance carbon sequestration recognize the significance of indigenous tropical forest trees in carbon emissions sequestration capacity and mitigation of climate change [5] [6]. However the amount of biomass and carbon on site is largely determined by human activities and management mechanisms. [5] [6] showed that tree biomass and soil carbon densities in indigenous forests were not consistently greater than in plantations of exotics, depending on the plantations age and species. Although Meta analysis studies have shown that replacing native forests with agriculture and plantations (when less than 40 years of age) generally reduces soil carbon stock [7] [8] [9]. This indicates how plantations of commercial exotic trees have little capacity in storing carbon in its ecosystem and hence are less liable for carbon sequestration and climate change mitigation because in the study area, it was observed that Pinus patula was ready for timber harvest (when 10 and above years age). While studies show that, a typical Tree Plantation investment could generate few profits in 20 years for wood pellets, veneer in year 25 and timber after year 30 [10] [11].

The study was conducted at Mbeya One ward between two forest reserves (Rungwe forest reserve in the east and Mporoto forest reserve in the west) in Mbeya rural district, Mbeya region. The main objective was to assess the influence of human activities on biomass variation of indigenous village and forest reserve trees between two forest reserves and the specific objectives were: 1) to assess the indigenous tree biomass variation between Mporoto and Rungwe forest reserves; 2) to assess the exotic tree biomass variation between the two forest reserves; and 3) to assess the human implication on the variation of tree's biomass between the two forest reserves.

Methodology

Description the Study Area

Rungwe Mountain Forest Reserve is located in Rungwe District of Mbeya Region in the south-western part of Tanzania. It is found between latitudes 9˚02' and 9˚12'S and longitudes 33˚35' and 33˚45'E. To the east, the forest is bordered with the Kitulo National Park and the Mporoto Ridge Forest Reserve that has an impressive crater of Lake Ngozi bordering the forest to the northwestern parts [12]. Geographically the crater is part of the Mporoto Ridge forest reserve with Ngozi peak at 2620 m the highest point of the ridge.

Rungwe Mountain is the southern Tanzania's second highest peak rising up to 2960 m above sea level; it is composed of 10 or more dormant volcanic craters and domes, the mountain is surrounded by the catchments forest reserve that gazetted in 1949, which incorporates montane forest, upper montane forest and montane grassland, with lesser amounts of bushland and heath at the upper elevations found in low bushes along streams and at the edges of montane forest.

The southeastern slopes of these mountains receive up to 3000 mm of rainfall a year, the highest rainfall in Tanzania. The mountain has spiritual significance to the Wanyakyusa and Wasafwa tribe for medicine and praying [13].

The forest is home to a variety of significant forest flora and fauna, including the threatened Abbot's Duiker and it is regarded as important bird. There are also two new species of primate (the Kipunji monkey and the Rungwe Galago) and over 530 species of orchid. In 2003 a new species of monkey was discovered on the volcano. It is called the highland mangabey (Rungwecebus kipunji). Fewer than a thousand highland mangabey exist. Scientists have assigned it to a new genus, Rungwecebus, named after Rungwe volcano, where it is found [12].

Mt. Rungwe is the most important protected area in Rungwe district and probably within Mbeya region; it marks the junction between eastern and western arms of Africa's Great Rift Valley. It is renowned internationally as an important center of endemism. Common trees in these montane forests include Aphloia theiformis, Ficalhoa laurifolia, Maesa lanceolata, Trichocladus ellipticus, Albizia gummifera and Bersama abyssinica. There are extensive stands of bamboo in some forests and a belt of heath land on Mount Rungwe above 2600 m. Associated with variety of exotic plants grown by communities in residential areas.

Data Collection Techniques

Data collection carried out by establishing a transect lying between Mporoto and Rungwe forest reserves of approximately 7200 m (7.2 km) distance between ( Figure 1). Plots of 20 × 20 m were established in the forest and diameter at breast height of trees with Dbh > 10 cm were measured. While in the crop land and residential areas, plots of 50 × 50 m were established in which diameter at breast height of trees were recorded for biomass calculation.

Data Analysis

To determine the above ground biomass and volume of each tree, the allometric equations developed by Malimbwi et al. (2003) cited by [14] was used. According to Malimbwi (2003), both above ground biomass and volume are the function of tree height and diameter regardless of the species type.

The Tree Biomass Equations used are given below.

where; D is the diameter at breast height in centimeters (cm), H is the height in meters (m) of each tree. Tree abundance in both sides of the study area (i.e. Mporoto forest reserve side and Rungwe forest reserve side) was compared by using Shannon Weiver Species richness index (H) [15] ( )

H piLnPi

= − ∑

where: Pi is the proportion of species i relative to the total number of species Ln pi the natural logarithm of this proportion pi.

Results

A total of eighteen (18) plots were established of which two (2) plots were established in each forest reserve, having a total of four (4) plots in the forests and 14 plots in the crop and residential areas. Plots were established at various altitudes ranged between 1448 m to 2034 m a.s.l.

Variation of Indigenous Tree Biomass along the Transect

Findings shows that, the biomass of indigenous trees decreased along the transect as one moves from one forest reserve to the crop land and residential areas then began increasing when approaching the next forest reserve, this indicates that establishment of field crops involved clearance of indigenous trees, this was also observed in exotic tree plantations where there were still some indigenous trees left in the plantations. From Figure 2, gentle and undulating sloping of the graph (at 0 -1000 m) indicated the presence of little or no forest encroachment through tree cutting in Mporoto forest reserve when compared with Rungwe forest reserve where the forest is intact at around 7000 m along the transect but with a sharp decline at 6000 m. This trend shown that, there were human encroachments for tree cutting on the side of Rungwe forest reserve.

Variation of Exotic Tree Biomass along the Transect

The main commercial exotic trees in the study area were Eucalyptus saligna and Pinus patula followed with scarcely distributed residential fruit trees like Persia americana, Prunus americana and Mussa sapientum. There was an interchanging trend of biomass variation between indigenous and exotic trees in the study area. As one moves from Mporoto forest reserve in the west, exotic tree biomass was very small though began to increase when entering crop lands and near residential areas. However as in Figure 3, exotic trees biomass increased rapidly near the Rungwe forest reserve boundary and after the boundary (Figure 3, around 5000 -7000 m along the transect) with a gentle decrease at 500 m along the transect where there were indigenous trees, this indicated that human encroachment into the forest has replaced the indigenous trees especially through clearing indigenous trees replacing with exotic trees.

Variation of Indigenous Tree Richness along the Transect

There was high variation in tree species richness towards Mporoto and Rungwe

Variation of Exotic tree's Biomass along the Transect

the highway road the two forest reserves were connected and human being encroached the forests through tree clearance for crop cultivation and residents, a trend which is still going on though in a slow pace toward Rungwe forest reserve.

Variation of Exotic Tree Abundance along the Transect

The trend of exotic trees abundance along the transect corresponds positively with the trend of indigenous trees but in opposite pattern (Figure 4). From Mporoto forest reserve, exotic trees began planted just after the forest reserve boundary followed by Irish potatoes and maize fields and the trend was increasing towards residential areas. The trend was contrary on the other side of the highway towards Rungwe forest reserve where plantation of exotic trees began in the forest reserve and went down all the way to crop land and to the residential area. This indicates that on the side of Mporoto forest reserve, exotic trees has been planted in the people's own fields while in the side of Rungwe forest reserve there is some sort of forest reserve encroachment for exotic trees plantation ( Figure 5). The number of exotic tree species on the side of Mporoto forest reserve was higher compared to Rungwe forest reserve side, this may be due to the observed fact that the dominating exotic tree species on the Rungwe forest side is Pinus

Exotic trees variation along Transect

patula which is basically for timber business while on Mporoto side two exotic tree species dominated including Eucaryptus saligina and Pinus patula which were for timber and poles business. Therefore Mporoto side was more diverse (H = 1.1831) when compared to Rungwe forest side (H = 0.7415).

Conclusion and Recommendation

Conclusion

There is a clear interchanging trend of both indigenous and exotic tree's biomass variation along the transect in the study area, indicating that exotic tree plantation has replaced indigenous trees in the area while in the crop lands most of indigenous trees have been cleared for crop cultivation, which is still going on though slowly; if not intervened it will result in conversion of much of the forest land into exotic trees plantations and crop cultivation especially a commercial crop Irish potatoes. This will lead to loss of a potential carbon sink as most of the indigenous trees store more carbon when compared with exotic trees which grow at fast rate and start being harvested in short life time (8 -10 years) for timber production. While in a long run, this trend leads to depletion of the potential of natural forests in ecological and social services like losing water conserving trees in water sources hence lowering water reserve and availability not only in the surrounding communities in villages but also in towns and cities where the main water supply is from rivers draining down from mountain forests. The trend will also have a negative impact on the wild animals, some endemic and threatened dwelling in the forest reserves and the aesthetic nature of the area to tourists.

Recommendation

There should be an urgent intervention especially in the east side of the study area (towards Rungwe forest reserve) stopping exotic tree plantation penetrating into the forest reserve, timber harvesting and Irish potatoes cultivation, plummeting aboveground biomass and escalating carbon emissions in the atmosphere while jeopardizing the natural forest ecosystem services to the communities. Conservation education should be emphasized in the study area to local communities, exotic trees plantations owners and other relevant stakeholders.

Figure 1 .
1Google Earth Image indicating transect connecting Rungwe Forest Reserve and Mp-oroto Forest Reserve. Modified form Google earth, 2015 by insertion of study plots coordinates. Key: Mbeya-Malawi Highway road, Transect, Sampling plots.

Figure 2 .
2forest reserve (Shannon index, H = 1.9677 and 1.37593 respectively), with dominating indigenous tree species in crop lands and plantations in both sides were Hagenia abyssinica, Olea europaea and Halleria lucida. The indigenous tree abundance varied in an undulating pattern (Figure 4) with main tree speciesHagenia abyssinica found in all land use types while Halleria lucida were mainly found in crop lands. Presence of the some common indigenous trees in cropland and residential areas on both sides of highway which separates the study area, indicates that may be before invasion of people into the land and construction of Indigenous tree biomass along the transect.

Figure 3 .
3Indigenous tree biomass along the transect. Mporoto to Rungwe Forset reserve (m)

Figure 4 .
4Variation of indigenous tree abundance along the Transect.

Figure 5 .
5Variation of indigenous tree abundance along the Transect.
AcknowledgementsI would like to express my sincere appreciation to RESON project administrators in Rungwe Volcanic Province for the chance and support provided to this work during data collection.
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