Extinction thresholds and negative responses of Afrotropical ant-following birds to forest cover loss in oil palm and agroforestry landscapes

Afrotropical ant-following birds are vulnerable to forest loss and disturbance, but critical habitat thresholds regarding their abundance and species richness in human-dominated landscapes, including industrial oil palm plantations, have never been assessed. We measured forest cover through Landsat imagery and recorded species richness and relative abundance of 20 ant-following birds in 48 plots of 1-km², covering three landscapes of Southwest Cameroon: Korup National Park, smallholder agroforestry areas (with farms embedded in forest), and an industrial oil palm plantation. We evaluated differences in encounter frequency and species richness among landscapes, and the presence of critical thresholds through enhanced adaptive regression through hinges. All species were detected in Korup National Park and the agroforestry landscape, which had similar forest cover (>85%). Only nine species were found in the oil palm plantation (forest cover = 10.3 ± 3.3%). At the 1-km² scale, the number of species and bird encounters were comparable in agroforests and the protected area: mean species richness ranged from 12.2 ± 0.6 in the park and 12.2 ± 0.6 in the agroforestry matrix to 1.0 ± 0.4 in the industrial oil palm plantation; whereas encounters decreased from 34.4 ± 3.2 to 26.1 ± 2.9 and 1.3 ± 0.4, respectively. Bird encounters decreased linearly with decreasing forest cover, down to an extinction threshold identified at 24% forest cover. Species richness declined linearly by ca. one species per 7.4% forest cover lost. We identified an extinction threshold at 52% forest cover for the most sensitive species (Criniger chloronotus, Dicrurus atripennis, and Neocossyphus poensis). Our results show that substantial proportions of forests are required to sustain complete ant-following bird assemblages in Afrotropical landscapes and confirm the high sensitivity of this bird guild to deforestation after industrial oil palm development. Securing both forest biodiversity and food production in an Afrotropical production landscape may be best attained through a combination of protected areas and wildlife-friendly agroforestry.     

Assessing forest fragmentation in northeastern region (NER) of India using landscape matrices

Northeastern region (NER) of India, one of the largest reserves of forests in India has so far been studied with a view to map the distribution of species or modeling the disturbance regimes and richness analysis. The present study focuses on the importance of regional level studies where the entire NER which is under the threat of forest fragmentation and degradation, is been assessed. In the present study, six historical data sets generated from remote sensing data (1972, 1982, 1987, 1989, 1993 and 1999) are used to assess forest cover loss, shape index and entropy to the degree of forest fragmentation over a multi-decadal period. The assessments have been carried out in the open (40–10% canopy density) and close (>40% canopy density) forest cover classes. The range of shape index and deviation from the actual mean in open forest and closed forest were computed separately. The patches among two categories were further analyzed based on patch area into six classes; ranging from <1 km² to >500 km². This also indicates variability of the forest patches. It is noteworthy that patches of area within 1–10 km² and 10–50 km² have been severely fragmented. This loss could be attributed to the shifting cultivation practice where the patches of moderate size are cultivated by group of families. The present study could give an insight to the patch configuration and composition in terms of shape index and the Shannon's entropy index.     

Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS

The present study investigates the status of forest degradation in the upper catchment of the river Tons in the Uttarakhand state of India, including Govind Wildlife Sanctuary and the National Park by the same name using remote sensing and the geographic information system (GIS). The study revealed that more than 50% of the study area is covered with snow and the alpine grasslands while 8.1% area is under agriculture. Degraded forest covers maximum area (53 km²), followed by moderately (30.4 km²) and severely degraded (26.8 km²) forests. The lower and middle slopes showed higher degradation than upper slopes due to multiple uses for agriculture, horticulture, agroforestry and grazing by the local people. Over time, the degradation and deforestation, without adequate protection, have led to severe soil erosion, biodiversity and the habitat depletion for a large number of rare and endemic species including loss of livelihood to the local people.     

Forest cover dynamics analysis and prediction modeling using logistic regression model

Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β = −26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2 km² of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC = 87%).     

Carbon allocation in a Costa Rican dry forest derived from tree ring analysis

The rising discussion on carbon balance of tropical forests often does not consider the sequestration potential of secondary dry forests, which are becoming an increasing importance due to land use change and reforestation. We have developed an easy applicable tool for the estimation of biomass increment of tropical secondary forest stands on the base of tree ring analysis. The existence of annual rings was shown by a combination of anatomical examination and radiocarbon estimations. With tree ring analysis, forest inventories and destructive sampling the above-ground biomass increment of secondary forest stands of age between 9 and 48 years in the dry forest region of Guanacaste, Costa Rica were estimated. The above-ground biomass increment of the tree layer varies between 2.4 and 3.2 Mg/ha yr in different stands. Lianas contribute with up to 23% additional production. Differences in productivity among the stands along a chronosequence were not significant. The measured carbon allocation potential of 1.7–2.1 Mg C/ha yr lies in the range of reported values from other tropical dry forests and old growth humid forests as well.     

Ecological Footprint: Refining the carbon Footprint calculation

Within the Ecological Footprint methodology, the carbon Footprint component is defined as the regenerative forest capacity required to sequester the anthropogenic carbon dioxide emissions that is not absorbed by oceans. A key parameter of the carbon Footprint is the Average Forest Carbon Sequestration (AFCS), which is calculated from the net carbon sequestration capacity of forests ecosystems.The aim of this paper is to increase the clarity and transparency of the Ecological Footprint by reviewing the rationale and methodology behind the carbon Footprint component, and updating a key factor in its calculation, the AFCS. Multiple calculation options have been set to capture different rates of carbon sequestration depending on the degree of human management of three types of forest considered (primary forests, other naturally regenerated forests and planted forests). Carbon emissions related to forest wildfires and soil as well as harvested wood product have been included for the first time in this update of the AFCS calculation. Overall, a AFCS value range of 0.73 ± 0.37 t C ha⁻¹ yr⁻¹ has been identified. The resulting carbon Footprint and Ecological Footprint values have then been evaluated based on this value range. Results confirm that human demand for ecosystem services is beyond the biosphere's natural capacity to provide them.     

Estimating biomass and carbon mitigation of temperate coniferous forests using spectral modeling and field inventory data

Realizing the importance of forest carbon monitoring and reporting in climate change, the present study was conducted to derive spectrally modeled aboveground biomass and mitigation using Landsat data in combination with sampled field inventory data in the coniferous forests of Western Himalaya. After conducting preliminary survey in 2009, 90 quadrats (45 each for calibration and validation) of 0.1 ha were laid in six forest types for recording field inventory data viz. diameter at breast height, height, slope and aspect. Biomass carbon (Mg ha^− 1) was worked out for different forest types and crown density classes (open with 10–40% crown density and closed with > 40% crown density) using recommended volume equations, ratios and factors. Biomass carbon map (aboveground + belowground) was generated for the entire region using geospatial techniques. Normalized difference vegetation index (NDVI) was generated and spectral values were extracted to establish relation (R² = 0.72, p < 0.01) with the field inventory data. The model developed was validated (R² = 0.73, p < 0.01) with 45 sample observations not used earlier for predicting and generating biomass carbon map (2009) for the entire region. The data from field based inventory indicates highest total biomass carbon (171.40, σ ± 23.19) Mg ha^− 1 for Fir–Spruce (closed) which has relatively more mature girth classes and low tree density. This value was found to be significantly higher than other forest types. Lowest biomass carbon was observed for Blue Pine (open) (37.15, σ ± 11.82) Mg ha^− 1. The NDVI values for the entire region ranged from 0 to 0.62 and consequently the spectrally derived aboveground biomass carbon varied from 0 to 600 Mg ha^− 1. The study demonstrates the application of mapping, spectral responses and sampled field inventory for type wise assessment of carbon mitigation in temperate coniferous forests of Himalayas.     

Loss of aboveground forest biomass and landscape biomass variability in Missouri,US

Disturbance regimes and forests have changed over time in the eastern United States. We examined effects of historical disturbance (circa 1813 to 1850) compared to current disturbance (circa 2004 to 2008) on aboveground, live tree biomass (for trees with diameters ≥13 cm) and landscape variation of biomass in forests of the Ozarks and Plains landscapes in Missouri, USA. We simulated 10,000 one-hectare plots using random diameters generated from parameters of diameter distributions limited to diameters ≥13 cm and random densities generated from density estimates. Area-weighted mean biomass density (Mg/ha) for historical forests averaged 116 Mg/ha, ranging from 54 Mg/ha to 357 Mg/ha by small scale ecological subsections within Missouri landscapes. Area-weighted mean biomass density for current forests averaged 82 Mg/ha, ranging from 66 Mg/ha to 144 Mg/ha by ecological subsection for currently forested land. Biomass density of current forest was greater than historical biomass density for only 2 of 23 ecological subsections. Current carbon sequestration of 292 TgC on 7 million ha of forested land is less than half of the estimated historical total carbon sequestration of 693 TgC on 12 million ha. Cumulative tree cutting disturbances over time have produced forests that have less aboveground tree biomass and are uniform in biomass compared to estimates of historical biomass, which varied across Missouri landscapes. With continued relatively low rates of forest disturbance, current biomass per ha will likely increase to historical levels as the most competitive trees become larger in size and mean number of trees per ha decreases due to competition and self-thinning. Restoration of large diameter structure and forested extent of upland woodlands and floodplain forests could fulfill multiple conservation objectives, including carbon sequestration.     

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido,Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km²) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40 m³ ha^–1 and from 30.78 to 180.54 MgC ha^–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r² = 0.80, RMSE = 55.04 m³ ha^–1; aboveground tree carbon content: r² = 0.78, RMSE = 19.10 MgC ha^–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r² = 0.84, RMSE = 62.66 m³ ha^–1; aboveground tree carbon content: r² = 0.76, RMSE = 27.05 MgC ha^–1) or coniferous forests (volume: r² = 0.75, RMSE = 51.07 m³ ha^–1; aboveground tree carbon content: r² = 0.58, RMSE = 19.00 MgC ha^–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.     

Drivers of reptile and amphibian assemblages outside the protected areas of Western Ghats,India

Biodiversity conservation in forested landscapes outside protected areas is important to sustain populations of species with restricted ranges. However, such habitats face many anthropogenic threats, including logging, extraction of firewood and leaf-litter for mulch in plantations. In this study, we determined the effects of forest degradation on amphibians and reptiles in forests outside protected areas by measuring their species richness and community composition across a disturbance gradient from near pristine to highly degraded forests in Agumbe, Western Ghats, India. Twenty-one strip 15 m × 150 m transects were laid across the disturbance gradient and diurnal visual encounter surveys were conducted. Sampling was repeated three times per transect covering the dry, intermediate and wet seasons. Amphibian and reptile communities were affected by the decrease in canopy cover and leaf litter volume, respectively. Our results indicate that the collection of firewood and leaf-litter can severely affect amphibian and reptile populations. Structured conservation planning outside of protected areas is therefore imperative.     

Impacts of forest gaps on butterfly diversity in a Bornean peat-swamp forest

Forest degradation is leading to widespread negative impacts on biodiversity in South-east Asia. Tropical peat-swamp forests are one South-east Asian habitat in which insect communities, and the impacts of forest degradation on them, are poorly understood. To address this information deficit, we investigated the impacts of forest gaps on fruit-feeding butterflies in the Sabangau peat-swamp forest, Central Kalimantan, Indonesia. Fruit-baited traps were used to monitor butterflies for 3 months during the 2009 dry season. A network of 34 traps (n_gap = 17, n_shade = 17) was assembled in a grid covering a 35 ha area. A total of 445 capture events were recorded, comprising 384 individuals from 8 species and 2 additional species complexes classified to genera. On an inter-site scale, canopy traps captured higher species richness than understory traps; however, understory traps captured higher diversity within each site. Species richness was positively correlated with percent canopy cover and comparisons of diversity indices support these findings. Coupled with results demonstrating morphological differences in thorax volume and forewing length between species caught in closed-canopy traps vs. those in gaps, this indicates that forest degradation has a profound effect on butterfly communities in this habitat, with more generalist species being favored in disturbed conditions. Further studies are necessary to better understand the influences of macro-habitat quality and seasonal variations on butterfly diversity and community composition in South-east Asian peat-swamp forests.     

A large‐scale field assessment of carbon stocks in human‐modified tropical forests

Tropical rainforests store enormous amounts of carbon, the protection of which represents a vital component of efforts to mitigate global climate change. Currently, tropical forest conservation, science, policies, and climate mitigation actions focus predominantly on reducing carbon emissions from deforestation alone. However, every year vast areas of the humid tropics are disturbed by selective logging, understory fires, and habitat fragmentation. There is an urgent need to understand the effect of such disturbances on carbon stocks, and how stocks in disturbed forests compare to those found in undisturbed primary forests as well as in regenerating secondary forests. Here, we present the results of the largest field study to date on the impacts of human disturbances on above and belowground carbon stocks in tropical forests. Live vegetation, the largest carbon pool, was extremely sensitive to disturbance: forests that experienced both selective logging and understory fires stored, on average, 40% less aboveground carbon than undisturbed forests and were structurally similar to secondary forests. Edge effects also played an important role in explaining variability in aboveground carbon stocks of disturbed forests. Results indicate a potential rapid recovery of the dead wood and litter carbon pools, while soil stocks (0–30 cm) appeared to be resistant to the effects of logging and fire. Carbon loss and subsequent emissions due to human disturbances remain largely unaccounted for in greenhouse gas inventories, but by comparing our estimates of depleted carbon stocks in disturbed forests with Brazilian government assessments of the total forest area annually disturbed in the Amazon, we show that these emissions could represent up to 40% of the carbon loss from deforestation in the region. We conclude that conservation programs aiming to ensure the long‐term permanence of forest carbon stocks, such as REDD+, will remain limited in their success unless they effectively avoid degradation as well as deforestation.     

The extent of edge effects in fragmented landscapes: Insights from satellite measurements of tree cover

Due to deforestation, intact tropical forest areas are increasingly transformed into a mixture of remaining forest patches and human modified areas. These forest fragments suffer from edge effects, which cause changes in ecological and ecosystem processes, undermining habitat quality and the offer of ecosystem services. Even though detailed and long term studies were developed on the topic of edge effects at local scale, understanding edge effect characteristics in fragmented forests on larger scales and finding indicators for its impact is crucial for predicting habitat loss and developing management options. Here we evaluate the spatial and temporal dimensions of edge effects in large areas using remote sensing. First we executed a neighborhood pixel analysis in 11 LANDSAT Tree Cover (LTC) scenes (180 × 185 km each, 8 in the tropics and 3 in temperate forested areas) using tree cover as an indicator of habitat quality and in relation to edge distance. Second, we executed a temporal analysis of LTC in a smaller area in the Brazilian Amazon forest where one larger forest fragment (25,890 ha) became completely fragmented in 5 years. Our results show that for all 11 scenes pixel neighborhood variation of LTC is much higher in the vicinity of forest edges, becoming lower towards the forest interior. This analysis suggests a maximum distance for edge effects and can indicate the location of unaffected core areas. However, LTC patterns in relation to fragment edge distance vary according to the analyzed region, and maximum edge distance may differ according to local conditions. Our temporal analysis illustrates the change in tree cover patterns after 5 years of fragmentation, becoming on average lower close to the edge (between 50 and 100 m). Although it is still unclear which are the main causes of LTC edge variability within and between regions, LANDSAT Tree Cover could be used as an accessible and efficient discriminator of edge and interior forest habitats in fragmented landscapes, and become invaluable for deriving qualitative spatial and temporal information of ecological and ecosystem processes.     

Spatial modeling of deforestation processes in the Central Peruvian Amazon

This study examines the relation between primary forest loss and landscape characteristics in the Ucayali region, Peru. Seven variables (rivers, elevation, annual precipitation, soil suitability for agriculture, population density, paved roads, and unpaved roads), were identified as potential deforestation drivers. The variables were converted into spatially explicit layers of continuous data and divided into a 9 km² grid. A multiple regression analysis was conducted to determine variable significance. Distance to paved and unpaved roads were strongly associated with deforestation, followed by distance to rivers, annual precipitation and elevation. All significant variables were negatively correlated with deforestation. Variables excluded from the model were population density and soil suitability for agriculture, suggesting that the influence of population density on forest clearing across the study area was not significant, and that deforestation activities were undertaken regardless whether soils are suitable for agriculture or not. Based on the linear regression analysis, the significant variables were selected and added to the Land Change Modeler in order to project primary forest coverage by 2025. The modeling results predict extensive deforestation along the Aguaytia River and at the forest/non-forest interface along the paved highway. The rate of primary forest removal is expected to increase from 4783 ha y⁻¹ (for the 2007–2014 period) to 5086 ha y⁻¹ (for the 2015–2025 period). A preliminary survey questionnaire conducted to explore deforestation intentions by farmers in the region, partly confirmed the overall deforestation trends as projected by the model.     

Carbon Pools and Emissions from Deforestation in Extra-Tropical Forests of Northern Argentina Between 1900 and 2005

We estimated carbon pools and emissions from deforestation in northern Argentine forests between 1900 and 2005, based on forest inventories, deforestation estimates from satellite images and historical data on forests and agriculture. Carbon fluxes were calculated using a book-keeping model. We ran 1000 simulations for a 105-year period with different combinations of values of carbon stocks (Mg C ha⁻¹), soil carbon in the top 0.2 m, and annual deforestation series. The 1000 combinations of parameters were performed as a sensitivity analysis that for each run, randomly selected the values of each variable within a predefined range of values and probability distributions. Using the simulation outputs, we calculated the accumulated C emissions due to deforestation from 1900 to 2005 and the annual emission as the average of the 1000 simulations, and uncertainties of our estimates as the standard deviation. We found that northern Argentine forests contain an estimated 4.54 Pg C (2.312 Pg C in biomass and 2.233 Pg C in soil). Between 1900 and 2005 approximately 30% of the forests were deforested, yielding carbon emissions of 0.945 (SD = 0.270) Pg C. Estimated average annual carbon emissions between 1996 and 2005, mostly from deforestation of the Chaco dry forests, were 20,875 (SD = 6,156) Gg C y⁻¹ (1 Gg = 10⁻⁶ Pg). These values represent the largest source of carbon from land-cover change in the extra-tropical southern hemisphere, between 0.9 and 2.7% of the global carbon emissions from deforestation, and approximately 10% of carbon emissions from the Brazilian Amazon. Deforestation, which has accelerated during the last decades as a result of modern agriculture expansion, represents a major national source of greenhouse gases and the second emission source, after fossil fuel consumption by fixed sources. We conclude that Argentine forests are an important carbon pool and emission source that need more attention for accurate global estimates, and seasonally dry forest deforestation is a key component of the Argentine carbon cycle. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

Cultivation impacts soil microbial dynamics in dry tropical forest ecosystem in India

We studied for two years the seasonal changes in plant available nitrate and ammonium nitrogen (N), nitrification, N-mineralization, microbial biomass carbon (MBC), nitrogen (MBN) and phosphorus (MBP) in two forest and three cropland sites, derived from a tropical forest ecosystem of India. Results indicated that seasonal values of nitrate N, ammonium N and phosphate P ranged from 7.33–12.99, 5.1–10.22 and 4.0–7.8 μg g^?1 in forest and 4.13–9.26, 9.35–14.46 and 2.8–5.8 μg g^?1 in cropland ecosystems, respectively, with maximum values in summer and minimum in rainy seasons. Nitrification and N-mineralization values varied from 6–28 and 4–26 μg g^?1 mo^?1 in forest and 3–14 μg g^?1 mo^?1 and 4–17 μg g^?1 mo^?1 in cropland, with maximum values in rainy season and minimum in summer season.MBC, MBN MBP ranged from 393–753, 34–80 and 16–36 μg g^?1 in forests and 186–414, 21–41 and 11–22 μg g^?1 in croplands, being maximum in summer and minimum in rainy seasons. There was gradual increase in the values of inorganic N, nitrification, N-mineralization and MBC, MBN and MBP along the age of cropland. Analysis of variance indicated significant difference in the concentration of inorganic N, nitrification and N-mineralization and MBC, MBN and MBP due to sites and seasons.Cultivation caused decline in the mean annual organic C, N and P by 42%, 29% and 13%. The values of nitrate N were decreased by 23–38%, while ammonium N was increased by 39–74%. Nitrification and N-mineralization values were reduced by 39–63% and 40–60%, respectively. Microbial C, N and P were reduced by 44–54%, 41–50% and 28–44%, respectively. Nonetheless, the contribution of soil microbial biomass reflected in total N was enhanced from 4.76% in forest to 5.03% in cropland ecosystem. Enhancement of plant available ammonium-N and microbial contribution in total N are an indicator of natural conserving mechanism to check the nitrogen loss from the nutrient poor agro-ecosystem.     

Seasonal abundance and activity of pill millipedes (Arthrosphaera magna) in mixed plantation and semi-evergreen forest of southern India

Seasonal occurrence and activity of endemic pill millipedes (Arthrosphaera magna) were examined in organically managed mixed plantation and semi-evergreen forest reserve in southwest India between November 1996 and September 1998. Abundance and biomass of millipedes were highest in both habitats during monsoon season. Soil moisture, conductivity, organic carbon, phosphate, potassium, calcium and magnesium were higher in plantation than in forest. Millipede abundance and biomass were about 12 and 7 times higher in plantation than in forest, respectively (P < 0.001). Their biomass increased during post-monsoon, summer and monsoon in the plantation (P < 0.001), but not in forest (P > 0.05). Millipede abundance and biomass were positively correlated with rainfall (P = 0.01). Besides rainfall, millipedes in plantation were positively correlated with soil moisture as well as temperature (P = 0.001). Among the associated fauna with pill millipedes, earthworms rank first followed by soil bugs in both habitats. Since pill millipedes are sensitive to narrow ecological changes, the organic farming strategies followed in mixed plantation and commonly practiced in South India seem not deleterious for the endangered pill millipedes Arthrosphaera and reduce the risk of local extinctions.     

Post-fire dynamics of woody vegetation in seasonally flooded forests (impucas) in the Cerrado-Amazonian Forest transition zone

Fire disturbance alters the structural complexity of forests, above-ground biomass stocks and patterns of growth, recruitment and mortality that determine temporal dynamics of communities. These changes may also alter forest species composition, richness, and diversity. We compared changes in plant recruitment, mortality, and turnover time over three years between burned and unburned sites of two seasonally flooded natural forest patches in a predominantly savanna landscape (regionally called ‘impucas’) in order to determine how fire alters forest dynamics and species composition. Within each impuca, 50 permanent plots (20 m × 10 m) were established and all individuals ≥5 cm diameter at breast height (DBH) identified and measured in two censuses, the first in 2007 and the second in 2010. Unplanned fires burned 30 plots in impuca 1 and 35 in impuca 2 after the first census, which enabled thereafter the comparison between burned and unburned sites. The highest mortality (8.0 and 24.3% year⁻¹ for impuca 1 and 2) and turnover time (69 and 121.5 years) were observed in the burned sites, compared to 3.7 and 5.2% year⁻¹ (mortality), and 28.4 and 40.9 years (turnover), respectively, for the unburned sites. Although these seasonally flooded impuca forests are embedded in a fire-adapted savanna landscape, the impucas vegetation appears to be sensitive to fire, with burned areas having higher mortality and turnover than unburned areas. This indicates that these forest islands are potentially at risk if regional fire frequency increases.