The overall accuracy for mapped deforestation over the study period was 91%. The producer’s accuracy for both stable forest and deforestation were 90% and 92% respectively. While the user’s accuracy for stable forest was 98%, the user’s accuracy for deforestation was 68%. This latter result reflects small errors of omission, likely attributable to clouds obscuring deforestation, or underestimation of deforestation.

The temporal trends in deforestation and fires highlighted annual peaks that aligned with political events. In Madagascar, fire is used to both celebrate and protest, and fire incidence is generally higher during socio-political crises or conflicts [ 57 – 58 ]. National elections in Madagascar occurred in December 2006 and December 2013 and in the months following these elections, deforestation increased substantially while fires showed modest increases. These trends likely reflected how in months preceding elections, officials often do not fully enforce land use policies to gain favor with local people, and administrators are often too busy campaigning to enforce regulations. Additionally, following elections, a new administration often results in staff turnover, which can also lead to lags in law enforcement [ 49 ]. 2007 was the highest year for deforestation, this essentially preceded REDD+ investment as the REDD+ activities started in December of 2007. Another large increase in both deforestation and the number of fire detections was evident beginning with the coup in 2009. From 2009 to 2012, large areas of land across Madagascar were seized and deforested without oversight or governance due to the lack of recognized government [ 49 ]. Further, many investors withdrew funding from Madagascar immediately following the 2009 coup until 2014 [ 65 ], as evidenced by the reduction in total number of investments during these years in Fig 4 .

The number of investments between 2007–2014 in CAZ starting in each year are shown with the blue bars along with the total USD invested each year shown with orange triangle markers (USD values on the secondary y-axis). The total number of cumulative investments in any given year is shown in light blue.

The number of investments and the cumulative number of investments varied significantly across the 8 year study period ( Fig 4 ). The number of investments is a count of new investments each year and the cumulative number of investments is a count of all on-going investments in each year. 2007, 2011, and 2014 had the highest number of new investments with 106, 102, and 133, respectively. Cumulative investments over the time period averaged 173 investments (SD of ± 38 investments) per year. Cumulative investments peaked in 2008 (208), 2011 (203), and 2014 (243). 2013 had the lowest number of cumulative investments (120).

We assessed temporal trends in deforestation, fires, and investments for the entire CAZ from 2007–2014. Trends in deforestation and fires were similar for many (but not all) years ( Fig 3 ). In fact, the two datasets had a low correlation (r = 0.19) for the entire annual time series from 2007–2014. When we excluded 2007 (the year when the two datasets differ the most), the correlation of the annual 2008–2014 time series increased to r = 0.61. The discrepancy in 2007 may be due to cloud persistence in the region. A year of cloud-free observations may include deforestation obscured by clouds in previous years. Deforestation and fire detections both decreased sharply in 2012 and then increased again in 2013.

A total of nine fokontany on the western border of our study area experienced deforestation but no fires were detected. However, the deforestation experienced was very small in area, less than 5 ha, and the 1 km MODIS-derived fire data were most likely too coarse to detect these small clearings. Conversely, five fokontany located along the edge of the southern patch of CAZ forest, experienced fires but no deforestation. This could also be attributed to the coarse resolution of the MODIS data, as fires detected in a 1 km 2 area are assigned to the centroid of the 1 km pixel. It is possible, therefore, that fires occurring just outside the study area were attributed to a pixel centroid within the study area. It is also possible that the presence of fires but no deforestation in these five fokontany resulted from errors of omission in the forest loss data.

Of the 88 fokontany in the study area, ten did not experience fires or deforestation during the study period. These fokontany were located along the study area perimeter and all had very little forest cover. The only exception was Ankailava, a forested fokontany, on the western boundary of the study area where communities generally practice irrigated rice production instead of swidden agriculture [ 34 ]. Further, the communities in this fokontany, are separated from the forest by a large valley which makes the forest less accessible.

High rates of deforestation and high densities of fire detections were also found in fokontany along the eastern boundary of our study area where fokontany contained less forest area than the interior of CAZ. Deforestation from swidden agriculture for rain-fed rice or maize production occurred more in this area due to accessibility and the presence of lower elevation forests [ 34 ] (Figs 1 , 2c & 2d ). In contrast, deforestation rates and fire detection densities were lower on the western boundary of our study area where the population is more sedentary and the main source of agricultural production is from irrigated rice fields, rather than swidden agriculture ( Fig 2c & 2d ). While fokontany with low rates of deforestation and low densities of fires were also situated in the middle of CAZ, it is noteworthy that deforestation and fire activity still occurred in these more remote areas. Some of this activity in the middle of CAZ may have resulted from the boom in artisanal gold mining between 2006–2009 [ 34 ] where the newly arrived miners cleared forests to plant rice and other crops to sustain their families.

Over the entire study period (2007–2014), the total deforestation and number of fire detections were highly spatially correlated (r = 0.97) indicating fires and deforestation coincided geographically in CAZ. The total hectares of deforestation and total number of fire detections in CAZ by fokontany were highest in the northern and southern extents of CAZ; and in fokontany with relatively large areas of forest remaining ( Fig 2a & 2b ). In 2012, the northern CAZ fokontany experienced an influx of migrants due to artisanal sapphire mining of river beds [ 64 ], and this influx of migrants may have resulted in additional clearing of forest for rice production. In contrast, the southern end of CAZ had high deforestation rates due to accessibility—this area is readily accessible from the national paved road.

Analysis of investment effects on deforestation and forest fires

Characterizing locational bias in investments. We first compared the 61 fokontany that received investments with the 27 fokontany that did not receive any investments to determine if there were any key differences in which fokontany received investments or not. The two groups of fokontany were similar in several characteristics that could be correlated with deforestation or fires (Table 2). These included: population density, percent of strict conservation zones, distance to unpaved roads or cart tracks, and distance to populated places (all p-values >0.10). The fokontany that received investments, however, had larger forest areas in 2007, higher elevations, and were located farther from paved roads, compared to those fokontany that did not receive any investments (p-values <0.05; Table 2). This suggests that remaining forested areas in CAZ were found in more remote areas and, not surprisingly, REDD+ investments tended to target areas with more forest area to protect. Similarly, of those fokontany that received investments, the fokontany with more total forest cover received, on average, more investments and larger dollar amounts of investments than those with less remaining forest. Fokontany that did not receive any investments were typically along the western side of the study area. These fokontany were located closer to major roads and contained, on average, only one third as much forest as the fokontany that received investments. PPT PowerPoint slide

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Investments and deforestation outcomes. The relationship between receiving any investment (i.e., binary variable) and deforestation rates over the 2007–2014 period was not statistically significant (Table 3). However, when we omitted 2007 from the analysis, there was a significant relationship at the 90% level between any investment and reduced deforestation for the full sample of fokontany and when only fokontany that ever received an investment were included. This correlation went away, however, when fokontany with investments mapped to the commune-level were omitted. The marginal effect of investments was around -0.1 when statistically significant and since we took the natural log of deforestation this translates to an approximate 10% decrease in percent deforestation from having an investment. Since average percent deforestation in the sample was ~1%, this is a decrease in percent deforestation of 0.1% attributable to the presence of an investment. We did not find any significant relationship between having an investment and deforestation when we used lags of one to three years. PPT PowerPoint slide

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larger image TIFF original image Download: Table 3. The effect of investments on percent deforestation at the fokontany-level. https://doi.org/10.1371/journal.pone.0190119.t003 When we examined the effect of presence of an investment by individual years, we found investments in 2010 and 2011 were statistically significant in reducing deforestation. In 2010, investments on average were associated with a 27% reduction in percent deforestation (p-value<0.10); this translates to a decrease in percent deforestation of 0.3%. In 2011, fokontany that received investments had, on average, a 35% lower rate of deforestation (p-value <0.05) than those that did not receive investments, or a decrease in percent deforestation of 0.4%. When we only included investments mapped to the fokontany-level, or fokontany that ever received an investment in our regressions, we found that 2011 remained statistically significant and of similar magnitude, but that 2010 was not statistically significant. These two years had some of the highest deforestation rates in CAZ; only 2007 was higher. In 2010 average deforestation was 2.4% and in 2011 it was 2.3%. The results suggest that, in these years of crisis, the fokontany that had some ongoing support and investment experienced lower deforestation rates than those fokontany where no investments were made. When we used total dollars invested in the fokontany as the independent variable and no lags, we found no statistically significant relationship with deforestation (Table 3). When we tested the effect of lagged amounts of dollars invested we found a jointly significant effect with lags of two time periods (probability>0.04–0.1 depending on sample). This suggests that dollars invested affected deforestation when there were contemporaneous investments and previous investments over the last two years. The marginal effect was between 0.04–0.05 and statistically significant at the 95% level. Since both the dependent and independent variables were log-transformed in this regression, a 1% change in dollars invested (the independent variable) is associated with a 0.04–0.05% decrease in percent deforested. A 1% change in mean dollars spent in these fokontany is about $42. While it is hard to know the exact reason investments did not have a consistently statistically significant effect on decreasing deforestation over all samples and tests (Table 3), there are several potential explanations. First, it could be attributable to the dynamic deforestation landscape and volatile political environment. Second, it could reflect an inability to capture and account for all investments or other types of investments that might have affected deforestation positively or negatively in the study area. Third, it could be the result of errors in the deforestation data set. Fourth, it could be missing data on other drivers of deforestation in the area that varied over space and time, such as booms in artisanal mining that led to change in population and agriculture pressure. Deforestation, especially at the fokontany-level, is likely associated with a much larger set of drivers that could be masking the effect of investments without finer-scale data on the specific location where investments took place at a village or community level. It does appear, however, that multiple years of investment are more effective (i.e., 2-year lag in Table 3) at reducing deforestation than one-off investments, and that investments may provide resilience to deforestation pressures during times of political instability.

Investments and fire outcomes. We found more consistent and stronger statistically significant relationships between measures of investments and the reduction of fires (Table 4). For investment as a binary measure, we found that having an investment reduced the probability of a fire by 0.14–0.16 when all years were included and about 0.18 when 2007 was excluded (p-value<0.05). After converting these marginal effects to account for the log-transformation of our dependent variable, this represented a 14–18% decrease in fire detections due to presence of an investment. There was a stronger relationship between an investment and fires when a one-year time lag was included in the regression. When two subsequent years of investments occurred, investments reduced the probability fire detections by 26–28%. When we examined the relationship between investments and fires for individual years we found statistically significant relationships in 2007, 2008, 2009, and 2012; but there was not consistency across years and samples. PPT PowerPoint slide

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larger image TIFF original image Download: Table 4. The effect of investments on fire detections at the fokontany-level. https://doi.org/10.1371/journal.pone.0190119.t004 When we used total dollars invested in the fokontany as the independent variable and fire as the dependent variable, we found a marginal effect between 0.013 and 0.019 depending on the sample used (Table 4). When we tested the effect of lagged amounts of dollars invested we found a jointly significant effect with a lag of one time period (probability>0.03–0.07 depending on sample). The marginal effect on investments in the lagged regression was between 0.021 and 0.028. This suggests that dollars invested affected the probability of a fire occurrence. With one year of investments (no lag), the effect of a 1% change in dollars invested (about $42 USD) was associated with a 0.01–0.02% decrease in fire detections, but if there was a previous year of dollars invested (1-year lag), then investments were associated with a 0.02–0.03% decrease in fire detections. Investments did appear to reduce fire occurrence (Table 4). Similar to deforestation, having multiple years of investment had a larger effect than just one year of investment. Many of the REDD+ investments were specifically aimed at providing alternative livelihood practices and reducing dependency on swidden agriculture, which may explain the stronger correlation between investments and reduction in fire occurrence. While deforestation and fire are generally strongly correlated, in our study area the year-to-year correlation of these two data sets was only 0.2 when 2007 was included and 0.6 when 2007 was excluded. Thus, fire occurrence data may have captured burning on existing agricultural land or young forest, in addition to burning after clearing mature forest.

Strengths and limitations of analysis. Our analysis is the most rigorous to date for CAZ given the large number of investments documented, the long time frame covered, and the use of two response variables (deforestation and fires) to test for conservation outcomes. While our study generated a robust database based on an exhaustive process and thorough analysis, some caveats should be considered. First, while the fixed effects method controlled for time-invariant unobservable differences in fokontany, any differences that varied over time and by fokontany were not controlled for and could have biased our regression results if the omitted variable was correlated with both investments and the outcome variable (i.e., deforestation rate or fire). For example, we did not have data on some time-varying drivers of deforestation, such as mining pressures, which accelerated in the latter years of our study period. Second, we may have missed some other types of investments that occurred in CAZ, such as rural development aid, but for which information was not available. This omission could have also biased our regressions if such investments were made and were targeted to the same fokontany as those by conservation groups, and such investments had an effect on deforestation rates or fire occurrence. Third, we did not have any data on investments prior to 2007 and these earlier investments could have influenced the impacts on conservation outcomes detected during our study period. Finally, our investment dataset was not mapped to communities or point locations, only to the fokontany-level. Despite the limitations mentioned above, our analysis suggests that REDD+ investments can be linked to decreases in forest fires, and to a lesser extent, reductions in deforestation. It also shows that these relationships vary across years, particularly in response to larger factors driving deforestation, such as political uncertainty. Overall, we feel confident that the relationships detected indicate positive conservation outcomes from REDD+ investments in CAZ.