Pocono mesic till barrens in retreat: topography,fire and forest contagion effects

The Pocono mesic till barrens (PMTB) are a unique assemblage of fire-maintained shrub communities that support numerous rare species. Historically these barrens covered a large area in the vicinity of Long Pond, Pennsylvania, USA. However, due largely to regional fire suppression instituted in the early 1960s, over 70% of the area covered by barrens succeeded to fire-intolerant forest that does not support the rare species. We investigated the influence of forest proximity on barrens succession across three geomorphic types during periods of high fire frequency and fire suppression, testing the hypothesis that forest processes such as seed rain, shading, and detrital enrichment of soils enhances barrens succession through a contagion effect. Evidence of a forest contagion effect should be shown by increased rates of barrens succession with increasing proximity to the nearest forest edge. In order to detect a forest contagion effect, barrens persistence and barrens succession were modeled in proximity zones of 0-50 m, 50-100 m, 100-200 m, and greater than 200 m from the nearest forest edge. We used existing GIS data layers for fire, geomorphology, and vegetation distribution in 1938, 1963, and 1992. The layers were modified and overlain using ArcView software to determine persistence and succession rates for each unique combination of layers in each proximity zone from 1938 to 1963 (pre-fire suppression) and 1963 to 1992 (post-fire suppression). ANCOVA results indicate that proximity to the nearest forest edge significantly affected barrens persistence rates in both time periods, but succession rates were significantly affected in 1938 to 1963 only. Twenty-eight percent of the 1938 barrens succeeded to forest by 1963; 56% of the 1963 barrens became forest by 1992. Results support previous findings that barrens persistence is enhanced by increased fire frequency, and that barrens persist longer where they overlie flat glacial till than on other geomorphology types.     

Opioid requirements after locoregional anaesthesia in dogs undergoing tibial plateau levelling osteotomy: a pilot study

ObjectiveTo determine the intraoperative and early postoperative opioid requirement after ultrasound-guided sciatic and/or femoral nerve block or epidural anaesthesia in dogs undergoing tibial plateau levelling osteotomy (TPLO).Study designProspective, masked, pilot, randomized, clinical trial.AnimalsA total of 40 client-owned dogs undergoing TPLO.MethodsEach dog was randomly assigned to group SF (combined sciatic and femoral nerve block), group S (sciatic nerve block), group F (femoral nerve block) or group E (epidural anaesthesia). A total of 0.3 mL kg^–1 of ropivacaine 0.5% was administered to each nerve or in the epidural space. Intraoperatively, fentanyl (2 μg kg^–1) was administered intravenously when heart rate, mean arterial pressure or respiratory rate increased by >30% compared with baseline values. Postoperatively, a visual analogue scale (VAS) and a modified German version of the French pain scale (4AVet) were used to assess pain every 30 minutes for 150 minutes and again once the morning after surgery. Methadone (0.1 mg kg^–1) was administered intravenously if the VAS was ≥ 4 cm [maximal value 10 cm; median (interquartile range)] or the composite pain score was ≥5 [maximal value 15; median (interquartile range)]. Significance was defined as p ≤ 0.05.ResultsGroups SF and E required less total intraoperative and early postoperative opioid doses compared with groups S and F (p = 0.031). No dogs in group SF had a block failure or required postoperative methadone. A reduced methadone requirement was found in group SF compared with all the other groups up to 150 minutes after recovery (p = 0.041).Conclusions and clinical relevanceCombined sciatic and femoral nerve block and epidural anaesthesia lead to less cumulative consumption of perioperative opioids than single nerve blockade. Sciatic or femoral nerve block alone might be insufficient to control nociception and early postoperative pain in dogs undergoing TPLO.     

Assessment of the socio-economic impacts associated with the arrival of apple snail (Pomacea canaliculata) in Mwea irrigation scheme,Kenya

Background

In Kenya, rice (Oryza sativa L.) is mainly produced under irrigation by small-scale farmers. Mwea irrigation scheme (MIS) in Kirinyaga County accounts for 80–88% of rice production. Here, rice is the main source of livelihood and a source of revenue generation for the county. However, a recently established invasive freshwater snail, Pomacea canaliculata (Lamarck) (family: Ampullariidae), a species of apple snail, presents a serious threat to rice production.

Results

Household surveys, focus group discussions and key informant interviews highlight apple snail as a serious problem in MIS. Households that observed at least a moderate level of infestation (>20% of cultivated area) experienced significant reductions in rice yield (~14%) and net rice income (~60%). Farmers reported increased use of chemical pesticides for management of apple snail. In addition, the cost of hired labor for physical removal of egg masses and snails is resulting in substantial negative effects on net income. Farmer age, area of land owned, responsibility for decision-making, receipt of extension advice, training, and membership of a farmer organization, were all statistically significant variables to explain farmers awareness of the need for area-wide apple snail management.

Conclusion

Strategies to limit the spread of apple snail are urgently needed. A Multi-Institutional Technical Team (MITT) has been established to spearhead management efforts and consolidate advice to farmers on how to manage apple snail. However, without action to mitigate spread, the consequences could be disastrous for rice production and food security in Kenya, and for other rice growing regions across Africa. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Conservation planning with spatially explicit models: a case for horseshoe bats in complex mountain landscapes

Context

Context Bats are considered as an ecological indicator of habitat quality due to their sensitivity to human-induced ecosystem changes. Hence, we will focus the study on two indicator species of bats as a proxy to evaluate structure and composition of the landscape to analyze anthropic pressures driving changes in patterns.

Objectives

This study develops a spatially-explicit model to highlight key habitat nodes and corridors which are integral for maintaining functional landscape connectivity for bat movement. We focus on a complex mountain landscape and two bat species: greater (Rhinolophus ferrumequinum) and lesser (Rhinolophus hipposideros) horseshoe bats which are known to be sensitive to landscape composition and configuration.

Methods

Species distribution models are used to delineate high-quality foraging habitat for each species using opportunistic ultrasonic bat data. We then performed connectivity analysis combining (modelled) suitable foraging habitat and (known) roost sites. We use graph-theory and the deviation in the probability of connectivity to quantify resilience of the landscape connectivity to perturbations.

Results

Both species were confined to lowlands (<1000 m elevation) and avoided areas with high road densities. Greater horseshoe bats were more generalist than lesser horseshoe bats which tended to be associated with broadleaved and mixed forests.

Conclusions

The spatially-explicit models obtained were proven crucial for prioritizing foraging habitats, roost sites and key corridors for conservation. Hence, our results are being used by key stakeholders to help integrate conservation measures into forest management and conservation planning at the regional level. The approach used can be integrated into conservation initiatives elsewhere.

     

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition,landscape configuration,and bioenergy crop area

Context

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees.

Objectives

Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations.

Methods

Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7, 23.5 and 28.8% of agricultural land converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape.

Results

Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale.

Conclusions

Strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.

     

Segmentation of 3D images of plant tissues at multiple scales using the level set method

Background

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

Results

We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue.

Conclusion

The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.