Identification of policies for a sustainable legal trade in rhinoceros horn based on population projection and socioeconomic models

Between 1990 and 2007, 15 southern white (Ceratotherium simum simum) and black (Diceros bicornis) rhinoceroses on average were killed illegally every year in South Africa. Since 2007 illegal killing of southern white rhinoceros for their horn has escalated to >950 individuals/year in 2013. We conducted an ecological–economic analysis to determine whether a legal trade in southern white rhinoceros horn could facilitate rhinoceros protection. Generalized linear models were used to examine the socioeconomic drivers of poaching, based on data collected from 1990 to 2013, and to project the total number of rhinoceroses likely to be illegally killed from 2014 to 2023. Rhinoceros population dynamics were then modeled under 8 different policy scenarios that could be implemented to control poaching. We also estimated the economic costs and benefits of each scenario under enhanced enforcement only and a legal trade in rhinoceros horn and used a decision support framework to rank the scenarios with the objective of maintaining the rhinoceros population above its current size while generating profit for local stakeholders. The southern white rhinoceros population was predicted to go extinct in the wild <20 years under present management. The optimal scenario to maintain the rhinoceros population above its current size was to provide a medium increase in antipoaching effort and to increase the monetary fine on conviction. Without legalizing the trade, implementing such a scenario would require covering costs equal to approximately $147,000,000/year. With a legal trade in rhinoceros horn, the conservation enterprise could potentially make a profit of $717,000,000/year. We believe the 35‐year‐old ban on rhinoceros horn products should not be lifted unless the money generated from trade is reinvested in improved protection of the rhinoceros population. Because current protection efforts seem to be failing, it is time to evaluate, discuss, and test alternatives to the present policy.     

Kernel-based home range method for data with irregular sampling intervals

Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations.     

The role of power line rights-of-way as an alternative habitat for declined mire butterflies

Habitat loss is one of the greatest threats for biodiversity. In Finland, two thirds of natural mires have been drained for silviculture, which transforms open wetlands into dense forests. However, vegetation management of power line rights-of-way (ROW) maintain the drained mires as open areas. The aim of this study was to determine the effect of the power line ROW vegetation management on butterfly abundance, species richness and community structure by comparing the managed power line ROWs to unmanaged drained control sites and to natural mires. The species richness or abundance of mire butterflies did not differ between the power line ROWs and natural mires. In contrast, both species richness and abundance of butterflies was low on the unmanaged control sites. Tree canopy cover had a negative effect on mire butterflies and this is most likely related to changes in microclimate. The results indicate that the active vegetation removal in the power line ROWs maintain alternative habitats for mire butterflies; yet, the power line ROWs cannot substitute the natural mires.     

量化沿海海域中背景营养盐的富集:波罗的海城市港湾的实例研究

对受到破坏的海岸生态系统进行成功的管理需要它们过去状况的可靠的科学根据.我们证明,生物指标的沉积物记录可用于在监测记录所经历的较短时间跨度之前对营养盐的浓度进行定量重建.我们为总溶解氮建立了一个具有0.09g/L(对数log10的单位)预测精度的借助于硅藻的、加权平均的、部分最小二乘传递函数模型.该模型被用于处理采自拉亚拉赫蒂(Laajalahti)湾的沉积岩心数据,拉亚拉赫蒂湾是位于芬兰赫尔辛基的一个城市港湾,在该港湾中,该模型的性能在30年的水质数据记录和该流域中已知土地使用变化的基础上得以证实.尽管该模型低估了受到严重影响的该港湾中的高营养盐浓度,但它仍成功跟踪了该营养盐记录中的各种趋势.实际值与预测值之间存在的总体一致性显示出该方法在对沿海水域中的背景营养盐浓度进行评估方面具有的巨大潜力.     

Planning for robust reserve networks using uncertainty analysis

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence–absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data—erroneous species presence–absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.     

Priority areas for conservation of Old World vultures

The prosperity and well-being of human societies relies on healthy ecosystems and the services they provide. However, the biodiversity crisis is undermining ecosystems services and functions. Vultures are among the most imperiled taxonomic groups on Earth, yet they have a fundamental ecosystem function. These obligate scavengers rapidly consume large amounts of carrion and human waste, a service that may aid in both disease prevention and control of mammalian scavengers, including feral dogs, which in turn threaten humans. We combined information about the distribution of all 15 vulture species found in Europe, Asia, and Africa with their threats and used detailed expert knowledge on threat intensity to prioritize critical areas for conserving vultures in Africa and Eurasia. Threats we identified included poisoning, mortality due to collision with wind energy infrastructures, and other anthropogenic activities related to human land use and influence. Areas important for vulture conservation were concentrated in southern and eastern Africa, South Asia, and the Iberian Peninsula, and over 80% of these areas were unprotected. Some vulture species required larger areas for protection than others. Finally, countries that had the largest share of all identified important priority areas for vulture conservation were those with the largest expenditures related to rabies burden (e.g., India, China, and Myanmar). Vulture populations have declined markedly in most of these countries. Restoring healthy vulture populations through targeted actions in the priority areas we identified may help restore the ecosystem services vultures provide, including sanitation and potentially prevention of diseases, such as rabies, a heavy burden afflicting fragile societies. Our findings may guide stakeholders to prioritize actions where they are needed most in order to achieve international goals for biodiversity conservation and sustainable development.     

The Quantitative Incidence Function Model and Persistence of an Endangered Butterfly Metapopulation

The incidence function model is derived from a linear first-order Markov chain of the presence or absence of a species in a habitat patch. The model can be parameterized with "snapshot" presence/absence data from a patch network. Using the estimated parameter values the Markov chain can be iterated in the same or in some other patch network to generate quantitative predictions about transient metapopulation dynamics and the stochastic steady state. We tested the ability of the incidence function model to predict patch occupancy using extensive data on an endangered butterfly, the Glanville fritillary ( Melitaea cinxia ) Parameter values were estimated with data collected from a 50-patch network in 1991. In 1993 we surveyed the entire geographic range of the species in Finland, within an area of 50 × 70 km², with 1502 habitat patches (dry meadows) of which 536 were occupied. Model predictions were generated for the 1502 patches and were compared with the observed pattern of occupancy in 1993. The model predicted patch occupancy well in more than half of the study area, but prediction was poor for one quarter of the area, probably because of regional variation in habitat quality and because metapopulations may have been perturbed away from the steady state. The incidence function model provides a practical tool for making quantitative predictions about metapopulation dynamics of species living in fragmented landscapes.     

Dispersal ecology of deadwood organisms and connectivity conservation

Limited knowledge of dispersal for most organisms hampers effective connectivity conservation in fragmented landscapes. In forest ecosystems, deadwood‐dependent organisms (i.e., saproxylics) are negatively affected by forest management and degradation globally. We reviewed empirically established dispersal ecology of saproxylic insects and fungi. We focused on direct studies (e.g., mark‐recapture, radiotelemetry), field experiments, and population genetic analyses. We found 2 somewhat opposite results. Based on direct methods and experiments, dispersal is limited to within a few kilometers, whereas genetic studies showed little genetic structure over tens of kilometers, which indicates long‐distance dispersal. The extent of direct dispersal studies and field experiments was small and thus these studies could not have detected long‐distance dispersal. Particularly for fungi, more studies at management‐relevant scales (1–10 km) are needed. Genetic researchers used outdated markers, investigated few loci, and faced the inherent difficulties of inferring dispersal from genetic population structure. Although there were systematic and species‐specific differences in dispersal ability (fungi are better dispersers than insects), it seems that for both groups colonization and establishment, not dispersal per se, are limiting their occurrence at management‐relevant scales. Because most studies were on forest landscapes in Europe, particularly the boreal region, more data are needed from nonforested landscapes in which fragmentation effects are likely to be more pronounced. Given the potential for long‐distance dispersal and the logical necessity of habitat area being a more fundamental landscape attribute than the spatial arrangement of habitat patches (i.e., connectivity sensu strict), retaining high‐quality deadwood habitat is more important for saproxylic insects and fungi than explicit connectivity conservation in many cases.     

Exposing ecological and economic costs of the research‐implementation gap and compromises in decision making

The frequently discussed gap between conservation science and practice is manifest in the gap between spatial conservation prioritization plans and their implementation. We analyzed the research‐implementation gap of one zoning case by comparing results of a spatial prioritization analysis aimed at avoiding ecological impact of peat mining in a regional zoning process with the final zoning plan. We examined the relatively complex planning process to determine the gaps among research, zoning, and decision making. We quantified the ecological costs of the differing trade‐offs between ecological and socioeconomic factors included in the different zoning suggestions by comparing the landscape‐level loss of ecological features (species occurrences, habitat area, etc.) between the different solutions for spatial allocation of peat mining. We also discussed with the scientists and planners the reasons for differing zoning suggestions. The implemented plan differed from the scientists suggestion in that its focus was individual ecological features rather than all the ecological features for which there were data; planners and decision makers considered effects of peat mining on areas not included in the prioritization analysis; zoning was not truly seen as a resource‐allocation process and not emphasized in general minimizing ecological losses while satisfying economic needs (peat‐mining potential); and decision makers based their prioritization of sites on site‐level information showing high ecological value and on single legislative factors instead of finding a cost‐effective landscape‐level solution. We believe that if the zoning and decision‐making processes are very complex, then the usefulness of science‐based prioritization tools is likely to be reduced. Nevertheless, we found that high‐end tools were useful in clearly exposing trade‐offs between conservation and resource utilization.     

Incorporating consumer–resource spatial interactions in reserve design

The persistence of species in reserves depends in large part on the persistence of functional ecological interactions. Despite their importance, however, ecological interactions have not yet been explicitly incorporated into conservation prioritization methods. We develop here a general method for incorporating consumer–resource interactions into spatial reserve design. This method protects spatial consumer–resource interactions by protecting areas that maintain the connectivity between the distribution of consumers and resources. We illustrate our method with a conservation planning case study of a mammalian predator, American marten (Martes americana), and its two primary prey species, Red-backed vole (Clethrionomys rutilus) and Deer mouse (Peromyscus maniculatus). The conservation goal was to identify a reserve for marten that comprised 12% of a forest management unit in the boreal forest in Québec, Canada. We compared reserves developed using analysis variants that utilized different levels of information about predator and prey habitat distributions, species-specific connectivity requirements, and interaction connectivity requirements. The inclusion of consumer–resource interactions in reserve-selection resulted in spatially aggregated reserves that maintained local habitat quality for the species. This spatial aggregation was not induced by applying a qualitative penalty for the boundary length of the reserve, but rather was a direct consequence of modelling the spatial needs of the interacting consumer and resources. Our method for maintaining connectivity between consumers and their resources within reserves can be applied even under the most extreme cases of either complete spatial overlap or complete spatial segregation of consumer–resource distributions. The method has been made available via public software.