Search

The rapid expansion of avocado cultivation in Michoacán, Mexico, is one of the drivers of deforestation. We assessed the degree of fragmentation and functional connectivity of the remaining temperate forest within the Avocado Belt and prioritized patches that contribute the most to connectivity using a network-based approach and modelling different seed and pollen dispersal scenarios, including two types of patch attributes (size and degree of conservation). As landscape transformation in the region is rapid and ongoing, we updated the land-use and land-cover maps through a supervised classification of Sentinel-2 imagery, improving the reliability of our analyses. Temperate forest is highly fragmented within the region: most patches are small (<30 ha), have a reduced core-area (28%), and irregular shapes. The degree of connectivity is very low (0.06), dropping to 0.019 when the degree of conservation of patches was considered. The top 100 ranked patches of forest that support the connectivity of seeds and pollen have different characteristics (i.e., size and topology) that may be considered for implementing conservation and management strategies. Seed dispersal seems to be more threatened by fragmentation than pollen dispersal, and patches that are important for maintaining seed connectivity are embedded in the denser zone of avocado orchards. [ABSTRACT FROM AUTHOR]

Honey bee decline is currently one of the world's most serious environmental issues, and scientists, governments, and producers have generated interest in understanding its causes and consequences in honey production and food supply. Mexico is one of the world's top honey producers, however, the honey bee population's status has not been documented to date. Based on 32 years of data from beekeeping, we make a country-level assessment of honey bee colony trends in Mexico. We use generalized additive mixed models to measure the associations between the percent change in honey bee hives and the percent change in honey yield per hive in relation to land-use, climate, and socioeconomic conditions. Despite the fact that the average annual yield per hive increased from 1980 to 2012, we detected a significant decline in the percent change in the number of honey bee hives across the time period studied. We also found a relationship between climatic conditions and agricultural land use, with agriculture increases and high temperatures producing a decrease in the percent change in honey yield. We found a relationship between a reduction in the temperature range (the difference between maximum and minimum temperatures) and a decrease in the percent change in the number of hives, while socioeconomic factors related to poverty levels have an impact on the number of hives and honey yields. Although long-term declines in hive numbers are not correlated with poverty levels, socioeconomic factors in states with high and medium poverty levels limit the increase in honey yield per hive. These results provide evidence that land-use changes, unfavorable climatic conditions, political, and socioeconomic factors are partially responsible for the reductions in the percent change in honey bee hives in Mexico. [ABSTRACT FROM AUTHOR]

Deforestation is a major factor reducing natural habitats, leading to tropical ecosystems and biodiversity loss worldwide. The Lacandona region in southern Mexico holds one of the largest fragments of tropical rainforest in North America. We evaluated the deforestation of the Lacandona region harmonizing concepts and methodologies. An international (FAO definition), governmental (national definition), and regional definition of deforestation with applications at different scales were analyzed and harmonized with two classification methods (likelihood and spectral angle mapper (SAM)). We used 2015 and 2018 Landsat 8 images, and likelihood and SAM classifications were applied for FAO and regional definitions of deforestation. Overall, the best evaluated classifier in quantity was likelihood for 2015 and 2018 (kappa: 0.87 and 0.70, overall accuracy: 91.8 and 80.4%, and quantity disagreement: 4.1 and 10 %, respectively). The allocation disagreement only showed exchange between classes. Nevertheless, they did not show differences between classifiers, although 2015 had less disagreement than 2018: exchange, 4.1% for likelihood and SAM; shift: 0% for likelihood and SAM. Maps based on the regional definition of deforestation showed that the likelihood classification detected 11,441 ha less deforestation than SAM (40,538 and 51,979 ha, respectively). The FAO definition of deforestation showed that likelihood classification detected 11,914 ha less deforestation than SAM classification (37,152 and 49,066 ha, respectively). Further, the likelihood classification showed 3387 ha more of deforestation according to the regional definition than the FAO definition of deforestation (40,538 and 37,152 ha, respectively). SAM classification showed that the regional definition showed 2913 ha more deforestation than the FAO definition (51,979 and 49,066, respectively). We concluded that implementation of governmental programs in the Lacandona region requires estimations based on a careful selection of deforestation definitions and methods. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and CLM LSMs in the Weather Research and Forecasting Model (WRF) over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna LULC category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate signals. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near surface temperature and precipitation. However in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation climate signals. Differences in thermal climate signals between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions, and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates. [ABSTRACT FROM AUTHOR]

Copyright of Historia Agraria is the property of Historia Agraria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current biodiversity loss is mostly caused by anthropogenic habitat loss and fragmentation, climate change, and resource exploitation. Measuring the balance of species loss and gain in remaining fragmented landscapes throughout time entails a central research challenge. We resurveyed in 2013 plant species richness in the same plots of a previous sampling conducted in 2003 across 18 forest fragments of different sizes of the Chaco Serrano forest in Argentina. While the area of these forest remnants was kept constant, their surrounding forest cover changed over this time period. We compared plant species richness of both sampling years and calculated the proportion of species loss and gain at forest edges and interiors. As in 2003, we found a positive relationship between fragment area and plant richness in 2013 and both years showed a similar slope. However, we detected a net decrease of 24% of species’ richness across all forest fragments, implying an unprecedentedly high rate and magnitude of species loss driven mainly by non-woody, short-lived species. There was a higher proportion of lost and gained species at forest edges than in forest interiors. Importantly, fragment area interacted with percent change in surrounding forest cover to explain the proportion of species lost. Small forest fragments showed a relatively constant proportion of species loss regardless of any changes in surrounding forest cover, whereas in larger fragments the proportion of species lost increased when surrounding forest cover decreased. We show that despite preserving fragment area, habitat quality and availability in the surroundings is of fundamental importance in shaping extinction and immigration dynamics of plant species at any given forest remnant. Because the Chaco Serrano forest has already lost 94% of its original cover, we argue that plant extinctions will continue through the coming decades unless active management actions are taken to increase native forest areas. [ABSTRACT FROM AUTHOR]

We applied annual Moderate Resolution Imaging Spectroradiometer (MODIS) product Vegetation Continuous Fields (VCF) data for the detection of forest cover change (FCC) in Mexico over the period 2000-2010. We excluded the pixels with uncertain information and applied a moving average and low-pass filter to smooth the multi-temporal data to reduce the fluctuations in the forest cover for each pixel. We applied a linear regression model and created two scenarios based on the coefficient of determination and slope to determine whether a pixel had changed its land cover over the study period. This model was able to label detected changes as deforestation, degradation, reforestation, and regrowth, based on the initial and final values of forest cover. The results showed that there has been more forest gain (reforestation and regrowth) than forest loss (deforestation and degradation) during the study period. We verified these results by comparing with the biomass data derived from the Mexican National Forest and Soil Inventory (Inventario Nacional Forestal y de Suelos, abbreviated to INFyS). Our model provides an efficient method to assess FCC at national level, which can contribute to the development of a reference level of greenhouse gas emission as necessary for the implementation of the international policy for reduction of emissions from deforestation and forest degradation (REDD+). [ABSTRACT FROM AUTHOR]