Striking the balance: Challenges and perspectives for the protected areas network in northeastern European Russia

Increasing anthropogenic pressure on the largest remaining tracts of old-growth boreal forest in Europe necessitates additional conservation of ecosystems and biodiversity in northeastern European Russia. In a regional network comprising 8 % of the Nenets Autonomous District and 13.5 % of the Komi Republic, 248 areas have varying protected statuses as state nature reserves (zapovedniks), national parks, reserves/sanctuaries (zakazniks), or natural monuments. Due to increased natural resource extraction in this relatively pristine area, designation of additional protected areas is critical for the protection of key ecological sites. The history of ecological preservation in these regions is herein described, and recent recommendations for incorporating additional ecologically representative areas into the regional network are presented. If the protected area network can be expanded, the overall environmental stability in these globally significant ecosystems may remain intact, and can help Russia meet the 2020 Aichi conservation targets, as set forth by the Convention of Biological Diversity.     

The New Zealand experience of varroa invasion highlights research opportunities for Australia

The Varroa mite (Varroa destructor) is implicated as a major disease factor in honey bee (Apis mellifera) populations worldwide. Honey bees are extensively relied upon for pollination services, and in countries such as New Zealand and Australia where honey bees have been introduced specifically for commercial pollinator services, the economic effects of any decline in honey bee numbers are predicted to be profound. V. destructor established in New Zealand in 2000 but as yet, Australia remains Varroa-free. Here we analyze the history of V. destructor invasion and spread in New Zealand and discuss the likely long-term impacts. When the mite was discovered in New Zealand, it was considered too well established for eradication to be feasible. Despite control efforts, V. destructor has since spread throughout the country. Today, assessing the impacts of the arrival of V. destructor in this country is compromised by a paucity of data on pollinator communities as they existed prior to invasion. Australia’s Varroa-free status provides a rare and likely brief window of opportunity for the global bee research community to gain understanding of honey bee-native pollinator community dynamics prior to Varroa invasion.     

A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Specific Features of Wild Boar,Sus scrofa,Rooting Activity at the Northern Boundary of Its Range in Western Siberia

Russian Journal of Ecology -     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

Spatial population structure of rare orchid species in rich fens in the central part of Murmansk oblast

The spatial structure of 10 populations of rare orchid species (Dactylorhiza incarnata, D. traunsteineri, Listera ovata) has been studied in rich fens sporadically occurring in Murmansk oblast. Two levels of plant aggregation within populations have been distinguished: (1) clusters of individuals and (2) isolated population subsets consisting of clusters. General spatial demographic features of orchid populations in the fens are small area (77 m²) and low ecological density (0.009 ind./m²). Characteristics of the population subsets (their number, area, composition, and distance to the neighboring subset) are species-specific and reflect the degree of species rarity in plant communities.     

Assessing relative vulnerability to sea-level rise in the western part of the Mekong River Delta in Vietnam

The Mekong River Delta in Vietnam plays a crucial role for the region in terms of food security and socioeconomic development; however, it is one of the most low-lying and densely populated areas in the world. It is vulnerable to seawater incursion, flood risk, and shoreline change, exacerbated as a consequence of sea-level rise (SLR) related to climate change. This study examined the Kien Giang coast in the western part of the delta, comprising seven coastal districts (namely Ha Tien, Kien Luong, Hon Dat, Rach Gia, Chau Thanh, An Bien, and An Minh), the economy of which is important in terms of agriculture and aquaculture. The analytical hierarchical process (AHP) method of multi-criteria decision making was integrated directly into geographic information systems (GIS) to derive a composite vulnerability index that indicated areas most likely to be vulnerable to SLR. The hierarchical structure comprised three key components: exposure (E), sensitivity (S), and adaptive capacity (A), at level 1. At the next level, 8 sub-components were mapped: seawater incursion, flood risk, shoreline change, population characteristics, land use/land cover, and socioeconomic, infrastructure, and technological capability, beyond which a further 22 variables (level 3) and 24 sub-variables (level 4) related to vulnerability were also mapped. Variables were assigned weights for incorporation into AHP pairwise comparisons after discussion with stakeholders. Maps were generated to visualise areas where the relative vulnerability was very low, low, moderate, high, and very high. Societal data were generally only available at district level; however, several regional patterns emerged. Relatively high exposure to flooding and inundation, salinity, and moderate loss of mangroves occurred along the coastal fringe of each district. This western section of the delta, which is low-lying and remote from the distributaries that carry sediment to the coast, appears to be particularly vulnerable. The most sensitive areas tended to be ethnic households engaged in rice cultivation and with moderate population density. The least adaptable areas consisted of high numbers of poor households, with low income, and moderate densities of transport, irrigation and drainage systems. Most coastal districts were determined to be moderately to relatively highly vulnerable, with scattered hotspots along the coast.