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406 results for “Forest cover”
Data from: Congruent phylogeographic patterns of eight tree species in Atlantic Central Africa provide insights on the past dynamics of forest cover
Cycles of Quaternary climate change are assumed to be major drivers of African rainforest dynamics and evolution. However, most hypotheses on past vegetation dynamics relied on palaeobotanical records, an approach lacking spatial resolution, and on current patterns of species diversity and endemism, an approach confounding history and environmental determinism. In this context, a comparative phylogeographic study of rainforest species represents a complementary approach because Pleistocene climate fluctuations may have left interpretable signatures in the patterns of genetic diversity within species. Using 1274 plastid DNA sequences from eight tree species (Afrostyrax kamerunensis, A. lepidophyllus, Erythrophleum suaveolens, Greenwayodendron suaveolens, Milicia excelsa, Santiria trimera, Scorodophloeus zenkeri, Symphonia globulifera) sampled in 50 populations of Atlantic Central Africa (ACA), we averaged divergence across species to produce the first map of the region synthesizing genetic distinctiveness and standardized divergence within and among localities. Significant congruence in divergence was detected mostly among five of the eight species and was stronger in the northern ACA. This pattern is compatible with a scenario of past forest fragmentation and recolonization whereby forests from eastern Cameroon and north-eastern Gabon would have been more affected by past climate change than those of western Cameroon (where one or more refugia would have occurred). By contrast, southern ACA (Gabon) displayed low congruence among species that may reflect less drastic past forest fragmentation or a more complex history of vegetation changes. Finally, we also highlight the potential impact of current environmental barriers on spatial genetic structures.
Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants
1. Despite time lags and non-linearity in ecological processes, the majority of our knowledge about ecosystem responses to long-term changes in climate originates from relatively short-term experiments. 2. We utilized the longest ongoing snow removal experiment in the world and an additional set of new plots at the same location in northern Sweden to simultaneously measure the effects of long-term (11 winters) and short-term (1 winter) absence of snow cover on boreal forest understorey plants, including effects on root growth and phenology. 3. Short-term absence of snow reduced vascular plant cover in the understorey by 42%, reduced fine root biomass by 16%, reduced shoot growth by up to 53%, and induced tissue damage on two common dwarf shrubs. In the long-term manipulation, more substantial effects on understorey plant cover (92% reduced) and standing fine root biomass (39% reduced) were observed, whereas other response parameters, such as tissue damage, were observed less. Fine root growth was generally reduced, and its initiation delayed by c. 3 (short-term) to 6 weeks (long-term manipulation). 4. Synthesis We show that one extreme winter with a reduced snow cover can already induce ecologically significant alterations. We also show that long-term changes were smaller than suggested by an extrapolation of short-term manipulation results (using a constant proportional decline). In addition, some of those negative responses, such as frost damage and shoot growth, were even absolutely stronger in the short-term compared to the long-term manipulation. This suggests adaptation or survival of only those individuals that are able to cope with these extreme winter conditions, and that the short-term manipulation alone would over-predict long-term impacts. These results highlight both the ecological importance of snow cover in this boreal forest, and the value of combining short- and long-term experiments side by side in climate change research.
Forest cover at landscape scales increases male and female gametic diversity of palm seedlings
<p>Genetic diversity shapes the evolutionary potential of plant populations. For outcrossing plants, genetic diversity is influenced by effective population size and by dispersal, first of paternal gametes through pollen, and then of paternal and maternal gametes through seeds. Forest loss often reduces genetic diversity, but the degree to which it differentially impacts the paternal and maternal contributions to genetic diversity and the spatial scale at which these impacts are most pronounced are poorly understood. To address these questions, we genotyped 504 seedlings of the animal-dispersed palm <i>Oenocarpus bataua </i>collected from 29 widely distributed sites across Ecuador and decomposed the contribution of paternal and maternal gametes to overall genetic diversity. The amount of forest cover at a landscape scale (> 10 km radius) had an equally significant positive association with both male and female gametic diversity. In addition, there was a significant positive association between forest cover and effective population size. Stronger fine-scale spatial genetic structure for female versus male gametes was observed at sites with low forest cover, but this did not scale up to differences in male vs female gametic diversity. These findings show that reductions in forest cover at spatial scales much larger than those typically evaluated in ecological studies lead to significant, and equivalent, decreases of diversity in both male and female gametes, and that this association between landscape level forest loss and genetic diversity may be driven directly by reductions in effective population size of <i>O. bataua</i>, rather than by indirect disruptions to local dispersal processes. </p>
Data from: Which landscape size best predicts the influence of forest cover on restoration success? – A global meta-analysis on the scale of effect
Landscape context is a strong predictor of species persistence, abundance and distribution, yet its influence on the success of ecological restoration remains unclear. Thus, a primary question arises: which landscape size best predicts the effects of forest cover on restoration success? To answer this question, we conducted a global meta-analysis for biodiversity (mammals, birds, invertebrates, herpetofauna and plants) and measures of vegetation structure (cover, density, height, biomass and litter). Response ratios were calculated for comparisons between reference (e.g. old-growth forest) and disturbed sites (degraded or restored). Using an information-theoretic approach, mean response ratio (restoration success) and response ratio variance (restoration predictability) within each study landscape were regressed against the percentage of overall (summed forest cover) and contiguous (summed pixels of ≥60% forest cover) forest within eight different buffer sizes of radius 5–200 km (at 1-km resolution). We included 247 studies encompassing 196 study landscapes and 4360 quantitative comparisons. The best buffer (landscape) size varied for the following: (i) overall and contiguous forest cover, (ii) biodiversity and vegetation structure and (iii) mean response ratio and response ratio variance. Only plant biodiversity was influenced by overall forest cover (buffer size of 5, 10 and 200 km radii), while plants (10 and 200 km radii), mammals (5, 10 and 50–200 km radii), invertebrates (5 and 10 km radii), cover (5 km radii), height (5 km radii) and litter (100 km radii) were influenced by contiguous forest cover. Overall, mean response ratio and response ratio variance were positively and negatively nonlinearly related with both overall and contiguous forest cover, respectively. We reveal for the first time a clear pattern of increasing restoration success and decreasing uncertainty as contiguous forest cover increases. We also indicate preliminary recommended buffer sizes for investigating landscape restoration effects on biodiversity and vegetation structure. However, the coarse grain and variability in the data mean the optimal landscape size may not have been detected; thus, further research is needed. Synthesis and applications. When setting targets for ecological restoration, policymakers and restoration practitioners should account for the following: (i) the landscape context, particularly the amount of contiguous habitat up to 10 km around a disturbed site, and (ii) the uncertainty in restoration success, as it increases when contiguous forest cover falls below about 50%.
A sampling dataset of canopy cover across Daxing'anling forests at 30 m using UAV visible imagery
<p>The sampling dataset of canopy cover across Daxing’anling forests for the year 2018 contained 77 sampling sites, and each of samplings was 720 m * 720 m with 30 m resolutions. The dataset was produced based on high-resolution UAV visible images. First, the two types of backgrounds, i.e., shaded gaps and sunlit gaps, was detected using the spectral information of DOM and structural analysis of DSM, respectively; Secondly, the DSM was inverted and segmented by watershed method, and tree crowns was mosaics of segmentations excluding the identified two types of backgrounds and other gap pixels located within segmentations, and the canopy cover was the ratio of tree crowns to that of a forest stand.</p>
Distribution. SE Brazil (Sao Paulo State), along the N (right) margin of the Rio Paranapanema, W as far as the Rio Parana, and between the upper rios Paranapanema and Tieté; today, known only from eleven widely separated forest patches covering c.444 km". in Callitrichiade
Distribution. SE Brazil (Sao Paulo State), along the N (right) margin of the Rio Paranapanema, W as far as the Rio Parana, and between the upper rios Paranapanema and Tieté; today, known only from eleven widely separated forest patches covering c.444 km".
Forest cover change in China (Version1, from 2000 to 2016)
<p><strong>(1)change class code:</strong></p> <p> 1:"gain"<br> 2:"loss"<br> 3:"gain then loss": afforested areas are eventually destroyed back to non-forest land<br> 4:"loss then gain":forest areas recovered or replanted after disturbances</p> <p><strong>(2)Citation</strong></p> <p><strong> </strong>Please cite the dataset including version number and the following paper when using this forest change result: </p> <p>Jing Guo, Peng Gong, Iryna Dronova & Zhiliang Zhu (2022) Forest cover change in China from 2000 to 2016, International Journal of Remote Sensing, 43:2, 593-606, DOI: <a href="https://doi.org/10.1080/01431161.2021.2022804">10.1080/01431161.2021.2022804</a></p> <p><strong>(3)Notes</strong></p> <p>1. We encourage people to send us feedback if you found some mistakes while using this data via email.</p> <p>2. Welcome discussions around potential collaborations. 【You can email Jing (guoj15@tsinghua.org.cn).】</p> <p> </p>
Training and validation sample datasets for Qinghai-Tibet Plateau Forest Cover Map 2021
<p>The training and validation sample datasets are label by the value of "0" and "1", when "0" means the non-forest samples and "1" means the forest samples. </p>
Winter soil temperature at the snow cover manipulation experiment in boreal forest
<p>The study was conducted in a spruce forest near Syktyvkar, taiga zone of northwestern Russia (N 61.650429, E50.731707). The mean annual air temperature is 0.5 C, with an annual precipitation of about 620 mm. Snow cover duration is averages 6 months (November-May). The stand is dominated by Norway spruce (Picea abies), but other species including Betula pubescens and Populus tremula are interspersed. There are sparse shrubs of rowan (Sorbus aucuparia) and dog rose (Rosa canina). The herbaceous layer is dominated by Oxalis acetosella and Vaccinium uliginosum. Less abundant herb species are Maianthemum bifolium, Pyrola rotundifolia, and mosses Hylocomium splendens, Pleurozium schreberi, Rhytidiadelphus triquetrus. In November 2018, three experimental plots (3 × 6 m) were established. The distance between the plots was at least 100 m. Each plot was divided into two sub-plots (3 × 3 m); each sub-plots corresponded to one option. The first option provided for the absence of snow cover in winter, which was achieved by the construction of sheds (a wooden frame covered with polyethylene film). The height of the sheds was 1 m. The fallen snow was regularly removed from the sheds to prevent their destruction. The second option was the control and did not involve any manipulations. The soil temperature was recorded eight time a day from November 2018 to May 2019 using a HOBO U12-008, ONSET, which was installed 5 cm below the soil surface at each sub-plot.</p>
The environmental drivers of tree cover and forest-savanna mosaics in Southeast Asia
<p>Forest-savanna mosaics exist across all major tropical regions. Yet, the influence of environmental factors on the distribution of these mosaics is not well explored, limiting our understanding of the environmental constraints on savannas especially in Southeast Asia, where most savannas exist in mosaics. Despite clear structural and functional characteristics indicative of savannas, most SE Asian savannas continue to be classified as forest. This designation is problematic because SE Asian savannas are threatened by both fragmentation and forest-centric management practices. By studying forest-savanna mosaics across SE Asia, we aimed to parse out how landscape mosaics of forest and savanna may be constrained by fire, climate, and soil characteristics. We used remotely sensed data to characterize the distribution of tree cover and forest-savanna mosaics. Using regression models, we quantified the relative effects of precipitation, fire frequency, seasonality, and soil characteristics on average tree cover and landscape patchiness. We found that low tree cover, indicative of savannas, occurs in drier, seasonal subregions that experience frequent fire. Further, our results demonstrate that fire and precipitation strongly shape landscape patchiness. Landscapes were patchiest in subregions with low precipitation and intermediate fire frequency. These results demonstrate that the environmental factors important in delineating the distribution of savannas globally shape the distribution of tree cover and landscape patchiness across SE Asia. Fire especially drives patterns of tree cover across scales. In a region where fire suppression is a common management strategy, our results suggest that further research studying vegetation response to fire and fire suppression is needed to improve management and conservation of these mosaic landscapes. More broadly, this work demonstrates a useful approach for studying the environmental drivers that influence the distribution of forest-savanna mosaics.</p>
Data covering Q10 and soil physicochemical properties of China's forest ecosystems
<p><span>Data including experiment description, site geographic location, climate variables, initial edaphic variables, and factors related to substrate quality, substrate availability, and microbial properties both before and after the treatments.</span></p>
Datasets and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China"
<p>We have provided the data and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China" for reference and further reading. These data can be used to replicate the analyses presented in the paper. If you wish to use the data for other purposes, please contact the authors for permission. Thank you.</p>
Random Forest fused MODIS and Landsat snow cover from spectral mixture analysis in the Sierra Nevada, USA
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS and well as a 2-stage random forest model to fuse the 2 datasets for improved temporal/spatial resolution. There are 170 scenes in 2001 to 2012. It was used in the a publication for Remote Sensing of the Environment titled: Multi-sensor fusion using random forests for daily fractional snow cover at 30 m, doi: to be assigned.</p> <p><strong>Inputs</strong>: [YYYYMMDD is year month day of month, $num is 5 or 7 for Landsat platform, $sens is sensor TM or ETM+]</p> <p>Landsat.zip:</p> <p>Snow cover from Landsat: SSN.p042r034_YYYYMMDD.Landsat$num-$sens.canopyadjusted_mask.v01.tif </p> <p> </p> <p>MODIS.zip:</p> <p>Snow cover from MODIS: SSN.SN_W$YYYYMMDD_$YYYYMMDD.Terra-MODIS.snow_cover_percent.v01.tif</p> <p> </p> <p>Predictors.zip<strong> </strong></p> <p>Static predictors (see RSE publication Table 2): SouthernSierraNevada*.tif [* here is the variable name]</p> <p><strong>Outputs [</strong> [YYYYMMDD is year month day of month]</p> <p>ProbabilityNot0Not100.zip</p> <p>SSN.prob.btwn.YYYYMMDD.v3.tif - from classification random forest, probability of being between 0 and 100</p> <p> </p> <p>Probability100fSCA</p> <p>SSN.pro.hundred.YYYYMMDD.v3.tif - from classification random forest, probability of being 100</p> <p> </p> <p>RegressionResult.zip</p> <p>SSN.regression.YYYYMMDD.v3.tif - from prediction random forest</p> <p> </p> <p>Final_Downscaled.zip</p> <p>SSN.downscaled.YYYYMMDD.v3.3e+05.tif - final product (combination of classification and prediction)</p>
Positive forest cover effects on coffee yields are consistent across regions
<p class="PargrafodaLista1CxSpFirst"><span>1. Enhancing biodiversity-based ecosystem services can generate win-win opportunities for conservation and agricultural production. Pollination and pest control are two essential agricultural services provided by mobile organisms, many depending on native vegetation networks beyond the farm scale. Many studies have evaluated the effects of landscape changes on such services at small scales. However, several landscape management policies (e.g., selection of conservation sites) and associated funding allocation occur at much larger spatial scales (e.g., state or regional level). Therefore, it is essential to understand whether the links between landscape, ecosystem services, and crop yields are robust across broad and heterogeneous regional conditions.</span></p> <p class="PargrafodaLista1CxSpMiddle"><span>2. Here, we used data from 610 Brazilian municipalities within the Atlantic forest region (~50 Mha) and show that forest is a crucial factor affecting coffee yields, regardless of regional variations in soil, climate and management practices. We found forest cover surrounding coffee fields was better at predicting coffee yields than forest cover at the municipality level. Moreover, the positive effect of forest cover on coffee yields was stronger for <i>Coffea canephora</i>, the species with higher pollinator dependence, than for <i>C. arabica</i>. Overall, coffee yields were highest when coffee fields were near to forest fragments, mostly in landscapes with intermediate to high forest cover (> 20%), above the biodiversity extinction threshold. </span></p> <p class="PargrafodaLista1CxSpMiddle"><span>3. Coffee cover was the most relevant management practice associated with coffee yield prediction. An increase in crop area was associated with a higher yield, but mostly in high forest covers municipalities. Other localized management practices like irrigation, pesticide use, organic manure, and honey-bee density had little importance in predicting coffee yields than landscape structure parameters. Neither the climatic or topographic variables were as relevant as forest cover at predicting coffee yields. </span></p> <p class="PargrafodaLista1CxSpLast"><span><a>4. <i>Synthesis</i></a><i> and application. </i>Our work provides evidence that landscape relationships with ecosystem service provision are consistent across regions with different agricultural practices and environmental conditions. These results provide a way in which landscape management can articulate small landscape management with regional conservation goals. Policies directed towards increasing landscape interspersion of coffee fields with forest remnants favor spillover process, and can thus benefit the provision of biodiversity-based ecosystem services, increasing agricultural productivity. Such interventions can generate win-win situations favoring biodiversity conservation and increased crop yields across large <a>regions.</a></span></p>
Upper thermal limits predict herpetofauna responses to forest edge and cover
<p>Amphibians and reptiles are sensitive to changes in the thermal environment, which varies considerably in human-modified landscapes. Although it is known that thermal traits of species influence their distribution in modified landscapes, how herpetofauna respond specifically to shifts in ambient temperature along forest edges remains unclear. This may be because most studies focus on local-scale metrics of edge exposure, which only account for a single edge or habitat patch. We predicted that accounting for the combined effect of multiple habitat edges in a landscape would best explain herpetofaunal response to thermally-mediated edge effects. We (1) surveyed herpetofauna at two lowland, fragmented forest sites in central Colombia, (2) measured the critical thermal maximum (CTmax) of the species sampled, (3) measured their edge exposure at both local and landscape scales, and (4) created a thermal profile of the landscape itself. We found that species with low CTmax occurred both further from forest edges and in areas of denser vegetation, but were unaffected by the landscape-scale configuration of habitat edges. Variation in the thermal landscape was driven primarily by changes in vegetation density. Our results suggest that amphibians and reptiles with low CTmax are limited by both canopy gaps and proximity to edge, making them especially vulnerable to human modification of tropical forest.</p>
Dataset from "How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? Insights from observations and modeling in eastern Canada"
<p>The dataset presented below is described in the publication “<em>How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? An observational study in eastern Canada.</em>” from Bouchard et al. (submitted) in the journal Hydrology and Earth System Science.</p> <p>The original dataset includes <strong>monitoring data</strong> collected at Montmorency Forest (47.29°N, 71.17°W) from 15 October 2020 to 15 June 2021 (W20-21) and from 15 October 2021 to 15 June 2021 (W21-22) in a medium-size gap, the small-size gap and under the canopy. The study site is a balsam fir – whit birch stand on a 12° slope of north-east aspect. In the monitoring dataset you can find at the hourly timestep:</p> <ul> <li>Snow depth (cm)</li> <li>Soil temperature at 20 cm, 10 cm and 5 cm below ground surface (°C)</li> <li>Soil-snow interface temperature (°C)</li> <li>Snow temperature every 15 cm from the ground surface (°C)</li> <li>Snow surface temperature (°C)</li> <li>Air temperature (°C)</li> <li>Relative humidity (%)</li> <li>Soil volumetric water content at 15 cm below the ground surface (0 – 1)</li> </ul> <p>The dataset also includes <strong>snow pit observations</strong> taken at Montmorency Forest during W20-21 and during W21-22. Each winter, four (4) snow pits were dug inside medium-size gaps, small-size gaps and at subcanopy locations. Snow pit measurement dates are presented in Bouchard et al. (submitted). Each snow pit includes the vertical profile of:</p> <ul> <li>Snow stratigraphy</li> <li>Snow temperature</li> <li>Snow density</li> <li>Snow specific surface area (SSA)</li> </ul> <p> </p> <p>The snow pit height corresponds to the upper boundary of the topmost snow layer in the stratigraphy profile. For density measurements, the height value corresponds to the center of the 3-cm thick box cutter. For the SSA, the value is measured optically at the top of the sample. This value is representative of the top 1 cm of the snow sample, as this is the typical e-folding depth of 1310 nm radiation in snow. Grain type codes for the snowpack stratigraphy correspond to the <em>International Classification for Seasonal Snow </em>(Fierz et al., 2009):</p> <ul> <li>PP: precipitation particles </li> <li>DF: decomposed and fragmented precipitation particles</li> <li>RG: rounded grains</li> <li>FC: faceted crystals</li> <li>FCxr: rounding faceted particles</li> <li>DH: depth hoar</li> <li>MFpc: melt forms – rounded polycrystals</li> <li>MF: melt forms – clustered rounded grains</li> <li>MFcr: melt forms – melt-freeze crusts</li> <li>IF: ice formations</li> </ul>
Data from: Climate severity and land-cover transformation determine plant community attributes in Colombian dry forests
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Data from: Forest cover mediates genetic connectivity of northwestern cougars
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Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada
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Data from: Congruent phylogeographic patterns of eight tree species in Atlantic Central Africa provide insights on the past dynamics of forest cover
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