Where are Europe's last primary forests? Sabatini, Francesco Maria; Burrascano, Sabina; Keeton, William S. ...
Diversity & distributions,
October 2018, Volume:
24, Issue:
9/10
Journal Article
Peer reviewed
Open access
Aim: Primary forests have high conservation value but are rare in Europe due to historic land use. Yet many primary forest patches remain unmapped, and it is unclear to what extent they are ...effectively protected. Our aim was to (1) compile the most comprehensive European-scale map of currently known primary forests, (2) analyse the spatial determinants characterizing their location and (3) locate areas where so far unmapped primary forests likely occur. Location: Europe. Methods: We aggregated data from a literature review, online questionnaires and 32 datasets of primary forests. We used boosted regression trees to explore which biophysical, socio-economic and forest-related variables explain the current distribution of primary forests. Finally, we predicted and mapped the relative likelihood of primary forest occurrence at a 1-km resolution across Europe. Results: Data on primary forests were frequently incomplete or inconsistent among countries. Known primary forests covered 1.4 Mha in 32 countries (0.7% of Europe's forest area). Most of these forests were protected (89%), but only 46% of them strictly. Primary forests mostly occurred in mountain and boreal areas and were unevenly distributed across countries, biogeographical regions and forest types. Unmapped primary forests likely occur in the least accessible and populated areas, where forests cover a greater share of land, but wood demand historically has been low. Main conclusions: Despite their outstanding conservation value, primary forests are rare and their current distribution is the result of centuries of land use and forest management. The conservation outlook for primary forests is uncertain as many are not strictly protected and most are small and fragmented, making them prone to extinction debt and human disturbance. Predicting where unmapped primary forests likely occur could guide conservation efforts, especially in Eastern Europe where large areas of primary forest still exist but are being lost at an alarming pace.
Commodity agriculture continues to spread into tropical dry forests globally, eroding their social-ecological integrity. Understanding where deforestation frontiers expand, and which impacts this ...process triggers, is thus important for sustainability planning. We reconstructed past land-system change (1985–2015) and simulated alternative land-system futures (2015–2045) for the Gran Chaco, a 1.1 million km² global deforestation hotspot with high biological and cultural diversity. We co-developed nine plausible future land-system scenarios, consisting of three contrasting policy narratives (Agribusiness, Ecomodernism, and Integration) and three agricultural expansion rates (high, medium, and low). We assessed the social-ecological impacts of our scenarios by comparing them with current biodiversity, carbon density, and areas used by forest-dependent people. Our analyses revealed four major insights. First, intensified agriculture and mosaics of agriculture and remaining natural vegetation have replaced large swaths of woodland since 1985. Second, simulated land-system futures until 2045 revealed potential hotspots of natural vegetation loss (e.g. western and southern Argentinian Chaco, western Paraguayan Chaco), both due to the continued expansion of existing agricultural frontiers and the emergence of new ones. Third, the strongest social-ecological impacts were consistently connected to the Agribusiness scenarios, while impacts were lower for the Ecomodernism and Integration scenarios. Scenarios based on our Integration narrative led to lower social impacts, while Ecomodernism had lower ecological impacts. Fourth, comparing recent land change with our simulations showed that 10% of the Chaco is on a pathway consistent with our Agribusiness narrative, associated with adverse social-ecological impacts. Our results highlight that much is still at stake in the Chaco. Stricter land-use and conservation planning are urgently needed to avoid adverse social-ecological outcomes, and our results charting the option space of plausible land-system futures can support such planning.
Land-use change is a root cause of the extinction crisis, but links between habitat change and biodiversity loss are not fully understood. While there is evidence that habitat loss is an important ...extinction driver, the relevance of habitat fragmentation remains debated. Moreover, while time delays of biodiversity responses to habitat transformation are well-documented, time-delayed effects have been ignored in the habitat loss versus fragmentation debate. Here, using a hierarchical Bayesian multi-species occupancy framework, we systematically tested for time-delayed responses of bird and mammal communities to habitat loss and to habitat fragmentation. We focused on the Argentine Chaco, where deforestation has been widespread recently. We used an extensive field dataset on birds and mammals, along with a time series of annual woodland maps from 1985 to 2016 covering recent and historical habitat transformations. Contemporary habitat amount explained bird and mammal occupancy better than past habitat amount. However, occupancy was affected more by the past rather than recent fragmentation, indicating a time-delayed response to fragmentation. Considering past landscape patterns is therefore crucial for understanding current biodiversity patterns. Not accounting for land-use history ignores the possibility of extinction debt and can thus obscure impacts of fragmentation, potentially explaining contrasting findings of habitat loss versus fragmentation studies.
•Disturbances have long-lasting impacts on forest biomass and structure.•Post-disturbance trajectories differed among disturbance agents.•Anthropogenic disturbances lead to lower woody cover and ...biomass.•Fairly slow structural recovery after logging and fires.
Tropical dry forests are widespread, harbour vast amounts of carbon and unique biodiversity, and underpin the livelihoods of millions. A variety of natural and anthropogenic disturbances affect tropical dry forest canopy, yet our understanding of how these disturbances impact on forest structure and ecosystem functioning, and how forests develop after different disturbances, is partial. This translates into knowledge gaps regarding long-term outcomes of disturbances on forest structure as well as which of these outcomes signify recovery vs forest degradation. Here, we use a rich dataset of remotely-sensed, high-resolution forest indicators in a multilevel Bayesian regression framework to understand the effect of different disturbance agents (partial clearing, fire, logging, drought and riparian changes) on aboveground biomass, and woody cover in the Argentine Dry Chaco. Our models show that post-disturbance trajectories of forest structural indicators differ markedly among different disturbance agents. For example, riparian changes affected biomass most strongly but had the fastest recovery, whereas logging had a generally lower impact and mostly affected tree cover, but recovery was slow or never occurred. Importantly, even three decades after the disturbance event, woody cover and biomass exhibited higher values for natural disturbances compared to anthropogenic disturbances. Furthermore, anthropogenic disturbances had slower recovery rates than natural disturbances. Overall, our approach shows the potential of remote-sensing indicators and space-for-time substitution to unravel the diverse vegetation response of different disturbance agents. Given the high and rising human pressure on dry forests in the Chaco and globally, our findings also show the long-lasting effects that anthropogenic disturbances have on these valuable forests.
Tropical dry forests harbor major carbon stocks but are disappearing rapidly across the globe as agriculture expands into them. Unfortunately, carbon emissions from deforestation in dry forests ...remain poorly understood as high spatial-temporal and vertical heterogeneity complicate biomass mapping. Here, we use a novel Gradient Boosted Regression framework to test the relative gains of combining optical (MODIS) and radar (Sentinel 1) time series, as well as lidar-based (GEDI) canopy-height information, to map biomass in tropical dry forests. We apply our approach across the entire Dry Chaco ecoregion (about 800,000 km2), using an extensive ground dataset of forest inventory plots for training and validation, to map above-ground biomass (AGB) for the year 2019. Our best AGB model had an r2 of 0.89 (RMSE = 15.1 t/ha) with an estimated AGB in remaining natural vegetation of 4.65 Gt (+/− 0.9 Gt). Seasonal metrics from EVI time-series, combined with seasonal Sentinel 1 metrics, had the highest predictive power, while adding GEDI-based canopy height did not improve models. Our resulting AGB maps had a much higher level of agreement with independent ground-data than global AGB products (agreements between r2 = 0.07–0.41), which all suffer from a huge, up to 14-fold, underestimation of AGB in the Chaco. Most of the remaining AGB stored in Chaco woodlands is found in Argentina (2.4 Gt AGB), followed by Paraguay (1.13 Gt AGB) and Bolivia (1.11 Gt AGB). Our results also highlight that 71% of the remaining AGB is located outside protected areas, and around half of the remaining AGB occurs on land utilized by traditional communities. Together, our analyses reveal substantial risk of continued high carbon emissions should agricultural expansion progress. Considerable co-benefits appear to exist between protecting traditional livelihoods and carbon stocks. Our map, the most accurate and fine-scale AGB map for this global deforestation hotspot, can serve as a basis for land-use and conservation planning aimed at leveraging such co-benefits. More broadly, our analyses reveal the considerable potential of combining time series of optical and radar data for a more reliable mapping of above-ground biomass in tropical dry forests and savannas.
•Ecoregion-wide AGB map for the dry Chaco.•New AGB map reveals up to 14-fold underestimation of AGB in existing datasets.•Optical and radar together performed best in mapping AGB.•Only 19% of the remaining AGB stocks in the Chaco are under protection.
Aim: Large and ecologically functioning steppe complexes have been lost historically across the globe, but recent land-use changes may allow the reversal of this trend in some regions. We aimed to ...develop and map indicators of changing human influence using satellite imagery and historical maps, and to use these indicators to identify areas for broad-scale steppe rewilding. Location: Eurasian steppes of Kazakhstan. Methods: We mapped decreasing human influence indicated by cropland abandonment, declining grazing pressure and rural outmigration in the steppes of northern Kazakhstan. We did this by processing 5,500 Landsat scenes to map changes in cropland between 1990 and 2015, and by digitizing Soviet topographic maps and examining recent high-resolution satellite imagery to assess the degree of abandonment of >2,000 settlements and >1,300 livestock stations. We combined this information into a human influence index (HI), mapped changes in HI to highlight where rewilding might take place and assessed how this affected the connectivity of steppe habitat. Results: Across our study area, about 6.2 million ha of cropland were abandoned (30.5\%), 14\% of all settlements were fully and 81\% partly abandoned, and 76\% of livestock stations were completely dismantled between 1990 and 2015, suggesting substantially decreasing human pressure across vast areas. This resulted in increased connectivity of steppe habitat. Main conclusions: The steppes of Eurasia are experiencing massively declining human influence, suggesting large-scale passive rewilding is taking place. Many of these areas are now important for the connectivity of the wider steppe landscape and can provide habitat for endangered megafauna such as the critically endangered saiga antelope. Yet, this window of opportunity may soon close, as recultivation of abandoned cropland is gaining momentum. Our aggregate human influence index captures key components of rewilding and can help to devise strategies for fostering large, connected networks of protected areas in the steppe.
In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large‐scale ecosystem assessments, while maintaining local relevance and ...accuracy. In heterogeneous landscapes, however, the integration of field‐collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 × 5 m2) remote sensing data as auxiliary data for upscaling field‐sampled aboveground carbon data to target (meso‐scale, i.e., 30 × 30 m2) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data—surface method, which uses a weighting layer, and regression method, which applies a regression model—with one method without auxiliary data—cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field‐based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non‐sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi‐scale remote sensing data.
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