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Organisations acting to conserve and protect species across large spatial scales prioritise to optimise use of resources. Spatial conservation prioritization tools typically focus on identifying areas containing species groups of interest, with few tools used to identify the best areas for single-species conservation, in particular, to conserve currently widespread but declining species.
ObjectiveA single-species prioritization framework, based on temporal and spatial patterns of occupancy and abundance, was developed to spatially prioritize conservation action for widespread species by identifying smaller areas to work within to achieve predefined conservation objectives.
MethodsWe demonstrate our approach for 29 widespread bird species in the UK, using breeding bird atlas data from two periods to define distribution, relative abundance and change in relative abundance. We selected occupied 10-km squares with abundance trends that matched species conservation objectives relating to maintaining or increasing population size or range, and then identified spatial clusters of squares for each objective using a Getis-Ord-Gi* or near neighbour analysis.
ResultsFor each species, the framework identified clusters of 20-km squares that enabled us to identify small areas in which species recovery action could be prioritized.
ConclusionsOur approach identified a proportion of species’ ranges to prioritize for species recovery. This approach is a relatively quick process that can be used to inform single-species conservation for any taxa if sufficiently fine-scale occupancy and abundance information is available for two or more time periods. This is a relatively simple first step for planning single-species focussed conservation to help optimise resource use.
相似文献- Invasions by non‐native species can compromise the conservation value of otherwise pristine headwater streams. While both developed and developing countries recognize this threat, few of the latter have suitable budgets to implement control programmes.
- This study assessed the effectiveness of a mechanical project to remove non‐native rainbow trout Oncorhynchus mykiss from a 6 km section of the upper Krom River, a small headwater stream in the Cederberg Mountains in South Africa's Cape Floristic Region (CFR).
- From October 2013 to February 2014, 354 O. mykiss were removed by angling (58%), fyke netting (28%) and gill netting (14%). This resulted in a marked reduction, but not eradication, of the O. mykiss population (fish relative abundance decreased from 0.53 ± 0.09 fish per net per night in October 2013 to 0.21 ± 0.09 fish per net per night in February 2014). Following the cessation of manual removals, the relative abundance of O. mykiss had increased to 0.56 ± 0.18 fish per net per night by March 2016, suggesting that without sustained removal effort, the population will rapidly return to its pre‐removal abundance level.
- Further work is needed to refine the methodology and test the effectiveness of mechanical removal of non‐native freshwater fish in a variety of ecological settings in the CFR. This approach holds potential for meeting the dual goals of reducing the ecological impacts of non‐native fishes and generating employment opportunities in line with the policy objectives of developing nations.
The goal of sustainable coffee production requires multiple functions from agroforestry systems. Many are difficult to quantify and data are lacking, hampering the choice of shade tree species and agronomic management. Process-based modelling may help quantify ecosystem services and disservices. We introduce and apply coffee agroforestry model CAF2021 (https://doi.org/10.5281/zenodo.5862195). The model allows for complex systems with up to three shade tree species. It simulates coffee yield, timber and fruit production by shade trees, soil loss in erosion, C-sequestration, N-fixation, -emission and -leaching. To calibrate the model, we used multivariate data from 32 different treatments applied in two long-term coffee agroforestry experiments in Costa Rica and Nicaragua. Without any further calibration, the model was then applied to agroforestry systems on 89 farms in Costa Rica and 79 in Guatemala where yields had been reported previously in farmer interviews. Despite wide variation in environmental and agronomic conditions, the model explained 36% of yield variation in Costa Rica but only 15% in Guatemala. Model analysis quantified trade-offs between yield and other ecosystem services as a function of fertilisation and shading.
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