Two paths to a suboptimal solution - once more about optimality in reserve selection

Several studies have compared the performances of exact algorithms (integer programming) and heuristic methods in the solution of conservation resource allocation problems, with the conclusion that exact methods are always preferable. Here, I summarize a potentially major deficiency in how the relationship between exact and heuristic methods has been presented: the above comparisons have all been done using relatively simple (linear) maximum coverage or minimum set models that are by definition solvable using integer programming. In contrast, heuristic or meta-heuristic algorithms can be applied to less simplified nonlinear and/or stochastic problems. The focus of this study is two kinds of suboptimality, first-stage suboptimality caused by model simplification and second-stage suboptimality caused by inexact solution. Evidence from comparisons between integer programming and heuristic solution methods suggests a suboptimality level of around 3%-10% for well-chosen heuristics, much depending on the problem and data. There is also largely anecdotal evidence from a few studies that have evaluated results from simplified conservation resource allocation problems using more complicated (nonlinear) models. These studies have found that dropping components such as habitat loss rates or connectivity effects from the model can lead to suboptimality from 5% to 50%. Consequently, I suggest that more attention should be given to two topics, first, how the performance of a conservation plan should be evaluated, and second, what are the consequences of simplifying the ideal conservation resource allocation model? Factors that may lead to relatively complicated problem formulations include connectivity and evaluation of long-term persistence, stochastic habitat loss and availability, species interactions, and distributions that shift due to climate change.     

First-best, second-best, and heuristic solutions in conservation reserve site selection

Previous studies which dealt with the conservation reserve site selection problem used either optimization methods, specifically linear integer programming (IP), or heuristic algorithms. The trade-off between computational efficiency versus optimality has been discussed in some articles and conflicting messages were signaled. Although the problem of suboptimality was acknowledged, some authors argued that heuristics may be preferable to exact optimization because IP models are computationally complex and may not be solvable when too many reserve sites are involved. On the other hand, some studies reported that fairly large problems could be solved easily. This paper shows that although the computational complexity argument can be valid for large reserve selection problems, by properly guiding the solver and exploiting the problem structure, formal optimization can deliver second-best (near-optimal) solutions that dominate the greedy heuristic solutions.     

Species specific connectivity in reserve-network design using graphs

Systematic conservation planning applications based solely on the presence/absence of a large number of species are not sufficient to guarantee their persistence in highly fragmented landscapes. Recent developments have thus incorporated much desired spatial design considerations, and reserve-network connectivity has received increased attention. Nonetheless, connectivity is often determined without regard to species-specific responses to habitat fragmentation. But species differ in their dispersal ability and habitat requirements, making proximate priority areas necessary for some species, while undesirable for others. We present a novel approach that incorporates species-specific connectivity needs in reserve-network design. Importantly, our method differs from previous approaches in that connectivity is not part of the objective function, but part of the constraints, thus avoiding typical undesirable trade-off that may result in high connectivity for some species but null connectivity for others. We use graphs to describe the dispersal pattern of each species and our goal is to identify minimum sets of reserves with connected sites for each of the species. This is not a trivial problem and we present three algorithms, one heuristic and two integer cutting algorithms that guarantee optimality, based on different 0-1 linear programming formulations. Applications to simulated data show that one of the algorithms that guarantee optimality is superior to the other, although both have limited application due to the number of sites and species they can manage. Remarkably, the heuristic can obtain very satisfactory solutions in short computational times, surpassing the limitations of the exact algorithms.     

Replacement cost: A practical measure of site value for cost-effective reserve planning

Conservation needs are often in direct competition with other forms of land-use, and therefore protection of biodiversity must be cost-efficient. While common reserve selection algorithms address this problem, quantitative planning tools often suggest an optimal set of sites that is not necessarily convenient for practical conservation. Besides cost-effective solutions we require flexibility if land-use conflicts are to be effectively resolved. We introduce a novel concept for site value in quantitative reserve planning. Replacement cost refers to the loss in solution value given that the optimal cost-efficient solution cannot be protected and alternative solutions, with particular sites forcibly included or excluded, are needed. This cost can be defined either in terms of loss of biological value or in terms of extra economic cost, and it has clear mathematical definitions in the context of benefit-function-based reserve planning. A main difference with the much-used concept of irreplaceability is that the latter tells about the likelihood of needing a site for achieving a particular conservation target. Instead, replacement cost tells us at what cost (biological or economic) can we exclude (or include) a site from the reserve network. Here, we illustrate the concept with hypothetical examples and show that replacement-cost analysis should prove useful in an interactive planning process, improving our understanding of the importance of a site for cost-efficient conservation.     

Dynamic economic management of soil erosion,nutrient depletion,and productivity in the north central USA

Physical scientists have presented a wealth of evidence regarding the effects of cropland soil degradation. Because soil degradation has both on‐site and off‐site effects, public policies have often tried to increase rates of conservation over privately optimal rates. Where private incentives leave off and public incentives start up is somewhat controversial, however. Physical evidence, while necessary, is not sufficient to predict conservation actions by farmers in response to the threat of degradation. This paper provides a partial explanation for why farmers may adopt differing conservation strategies, even though they share similar preferences. A model is constructed that divides soil degradation into reversible and irreversible components. We portray nutrient depletion as a reversible facet of soil degradation and soil profile depth depletion as an irreversible facet of soil degradation. Predictions of optimal management response to soil degradation are accomplished using a closed‐loop model of fertilizer applications and residue management to control future stocks of soil nutrients and soil profile depth. Our model is applied to degradation data from nine soils in the north central United States. Three principal findings result: First, due to differences in initial soil properties, susceptibility to degradation, sensitivity of yield to soil depth, and yield response to alternative management practices, dynamically optimal economic strategies cannot be inferred directly from physical results but are inferred from the associated economic implications. Second, optimal residue management is more variable with respect to soil type than to the erosion phase of the soil, implying that substantial gains to targeting are possible. Third, nutrient depletion is a more compelling motivator for adopting residue management than soil profile depth depletion. This implies that motivating residue management requires programs that pay even greater attention to reversible degradation, and therefore the overall farm management implications, rather than strictly to protect topsoil from irreversible degradation. Copyright © 2001 John Wiley & Sons, Ltd.     

Conservation cornerstones: Capitalising on the endeavours of long-term monitoring projects

Ecological monitoring is widely used to measure change through time in ecosystems. The current extinction crisis has resulted in a wealth of monitoring programs focussed on tracking the status of threatened species, and the perceived importance of monitoring has seen it become the cornerstone of many biodiversity conservation programs. However, many monitoring programs fail to produce useful outcomes due to inherent flaws. Here we use a monitoring program from south-eastern Australia as a case study to illustrate the potential of such endeavours. The threatened carnivorous marsupial, the brush-tailed phascogale (Phascogale tapoatafa), has been monitored at various locations between 2000 and 2010. We present strong evidence for a decline in relative abundance during this period, and also describe relationships with environmental variables. These results provide insights likely to be valuable in guiding future management of the species. In the absence of the monitoring program, informed management would not be possible. While early detection of population declines is important, knowledge of the processes driving such declines is required for effective intervention. We argue that monitoring programs will be most effective as a tool for enhanced conservation management if they test specific hypotheses relating to changes in population trajectories. Greater emphasis should be placed on rigorous statistical analysis of monitoring datasets in order to capitalise on the resources devoted to monitoring activities. Many datasets are likely to exist for which careful analysis of results would have benefits for determining management directions.     

基于启发式优先级规则的农机调配算法

针对目前农机调配中无科学合理调配方案的现状,该文提出了一种基于机主选择的农机调配模式,建立了以高收入低成本为目标的优化模型。针对农机调配的特点,该文分析了组合的优先级规则,设计了基于启发式优先级规则的农机调配算法。分别采用一般农机调配算法和本文提出的调配算法对农田实例进行计算并得到不同的调配方案。从服务收入、调配路程和等待时间等方面对不同调配方案进行分析和比较可知,本文算法得到的调配方案要优于一般调配算法的方案。最后分析了农田数量和并行度对本文算法性能的影响。试验结果表明该文提出的算法对于解决当前农机调配问题更为有效。     

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers’ objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives – a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved.     

Accounting for habitat loss rates in sequential reserve selection: Simple methods for large problems

We develop reserve selection methods for maximizing either species retention in the landscape or species representation in reserve areas. These methods are developed in the context of sequential reserve selection, where site acquisition is done over a number of years, yearly budgets are limited and habitat loss may cause some sites to become unavailable during the planning period. The main methodological development of this study is what we call a site-ordering algorithm, which maximizes representation within selected sites at the end of the planning period, while accounting for habitat loss rates in optimization. Like stochastic dynamic programming, which is an approach that guarantees a globally optimal solution, the ordering algorithm generates a sequence in which sites are ideally acquired. As a distinction from stochastic dynamic programming, the ordering is generated via a relatively fast approximate process, which involves hierarchic application of the principle of maximization of marginal gain. In our comparisons, the ordering algorithm emerges a clear winner, it does well in terms of retention and is superior to simple heuristics in terms of representation within reserves. Unlike stochastic dynamic programming, the ordering algorithm is applicable to relatively large problem sizes, with reasonable computation times expected for problems involving thousands of sites.     

Microbial community response to transition from conventional to conservation tillage in cotton fields

In the southeastern United States, conservation tillage techniques are used to conserve soil nutrients and structure, providing habitat and substrate for biota, which are largely responsible for the mineralization of nutrients in the soil. A deterrent for growers considering the transition to conservation tillage is the delay in soil response (e.g. increased soil carbon, efficient nutrient cycling, impacts on yield) associated with the equilibration of the soil food web. The objective of this study was to determine if the microbial community composition and biomass changed with transition to conservation tillage. Soils sampled from five sites, representing a chronosequence of conservation tillage (from conventionally tilled to 30 year no-till), were collected for fatty acid analysis. Microbial communities were significantly different among sites. Fungi, characterized by 18:2ω6, 18:1ω9, and 18:3ω6c fatty acids, were typically lowest in the conventionally tilled soil, probably due to repeated disruption of the fungal hyphae associated with tillage. In all soils, soil nutrient concentrations, moisture and microarthropod abundance were correlated with microbial structure. Plots in conservation tillage were significantly different from the conventionally tilled plots, but did not exhibit a clear linear pattern across the chronosequence. This evidence that belowground food webs can respond quickly to a cessation in tillage suggests that the delay in soil response may be due more to the time required to build organic matter than to a slow response by the biota.     

陕北黄土高原月降雨理论分布的初步研究

利用陕北两个典型气象站点多年降雨强度数据,使用数学方法描述月雨强分布的形状,从而满足水文模型、土壤侵蚀模型等对降雨强度数据的需求。通过探讨小时雨强在月时间尺度上应符合的理论分布函数,并采用统计学参数估计方法进行参数拟合,来选择合适的数学方法对月雨强分布进行描述。使用最大似然法对常见的7种理论分布函数进行拟合的结果表明,LOGN、GAMA、WBL等理论分布函数都可以较好地再现月降雨强度分布,90%以上的月份COE值大于0.7,一半以上的月份COE值大于0.9。另外,WBL分布对降雨强度峰值亦有良好的表现能力,因此推荐在陕北黄土高原降雨分布的模拟过程中使用。     

Optimization of irrigation management and environmental assessment using the water cycle algorithm

Obtaining high crop yields with limited water consumption requires optimal irrigation strategies based on comprehensive studies of the parameters of plant–environment interactions. Here, we used a structural equation modelling (SEM) to assess the relationships among input irrigation factors and moderate factors to find an optimum water use efficiency (WUE) response factor, for outdoor and greenhouse cultivation of eggplant (Solanum melongena). The input irrigation factors (including irrigation interval, water salinity and environment) and the moderate factors (evapotranspiration, soil salinity, plant parameters, fruit parameters and crop yield) were used in water cycle algorithm (WCA) and genetic algorithm (GA) methods to optimize the water use efficiency. The optimization process included finding the best combination of irrigation factors and optimized eggplant cultivation. The structural equation modelling results indicate that irrigation interval negatively affected water use efficiency with a more dominant effect on plant parameters. Water salinity negatively affected the water use efficiency with a more dominant effect on soil salinity, crop yield and fruit parameters. Low salinity water was more effective than full irrigation to optimize the water use efficiency. The water cycle algorithm revealed that for outdoor cultivation, the optimal range of irrigation interval was 2–5 days and water salinity in the range of 0.8–2.2 ds/m. These factors optimized evapotranspiration (346.23–738.19 mm), soil salinity (4.16–9.45 ds/m), fruit parameters (33.81–35.12 cm) and crop yield (1715.7–2190.8 g/plant), as well as increasing the water use efficiency (3.08–4.89 g/(plant-mm)). Both the water cycle algorithm and genetic algorithm yielded very close to optimal values. Two years of repeated experiments and the closeness of the optimal values using the algorithms confirmed that the optimal amounts are reliable.     

Conservation policies and planning under climate change

Biodiversity conservation policies focus on securing the survival of species and habitats according to their current distribution. This basic premise may be inappropriate for halting biodiversity decline under the dynamic changes caused by climate change. This study explores a dynamic spatial conservation prioritization problem where climate change gradually changes the future habitat suitability of a site’ current species. This has implications for survival probability, as well as for species that potentially immigrate to the site. The problem is explored using a set of heuristics for both of two policy objectives focusing on (1) the protection on current (native) species, and (2) all species, including immigrating species. The trade-offs between the protection of native species versus all species is illustrated. The study shows that the development of prediction models of future species distributions as the basis of decision rules can be crucial for ensuring the effectiveness of conservation plans. Finally, it is discussed how more adaptive strategies, that allow for the redirection of resources from protected sites to privately-owned sites, may increase the effectiveness of the conservation networks. Climate change induced shifts in the suitability of habitats for species may increase the value of such adaptive strategies, the benefit decreasing with increasing migration probabilities and species distribution dynamics.     

自寻优最近邻算法估算有限气象数据区潜在蒸散量

FAO-56 Penman-Monteith估算ET0方法被广泛使用,但计算时需要输入多个气象数据。开发一种替代方法,在使用尽可能少的气象数据情况下,仍可以提供准确的或至少接近FAO-56 Penman-Monteith的ET0估算值是该领域研究热点之一。该文结合典型相关分析（canonical correlation analysis,CCA）和k最近邻算法（k-nearest neighbor,k-NN）,提出自寻优最近邻算法的潜在蒸散量计算方法（CCA-k-NN）,利用较少气象数据实现潜在蒸散量的估算。核心思想是用CCA算法寻找与潜在蒸散量最相关的气象数据,实现后续估算ET0时的气象数据降维,然后利用k-NN算法估算ET0。选择西北地区为例,将该区域气象数据分别从时间和空间尺度,分为训练数据集,验证数据集和测试数据集,分别在3类数据集上用该文方法估算ET0,并以FAO-56 Penman-Monteith作为参照,评估了该文CCA-k-NN方法的估算精度和适用性。结果表明,CCA-k-NN方法与FAO-56 Penman-Monteith保持了较高的相关性（相关系数大于0.9）,有好的估算精度,均方根误差和平均绝对误差均小于1 mm/d,空间尺度上算法纳什效率系数均大于0.5,时间尺度上纳什效率系数均大于0.8,在时空尺度均适用。同时,相对于其他替代方法该文算法具有低的时间复杂度,在计算大量数据时可有效降低时间成本。     

Habitat-based conservation priorities for carabid beetles within the Picos de Europa National Park, northern Spain

Quick biodiversity studies on poorly studied taxa and areas are increasingly popular for setting conservation priorities over a wide range of spatial scales. However, the implementation of such studies is complicated by the variable extent to which the different criteria used in prioritisation are correlated to each other. Using methods of constrained ordination, we examined the species-habitat relationships of carabid beetles based on ground beetle assemblages from 22 sites in the Picos de Europa National Park, northern Spain. We found characteristic species assemblages for subalpine meadows, Genista shrublands, and pastures, whereas mown meadows, heathlands, beech and riparian woodlands were occupied by more habitat generalist species. Species associated with subalpine meadows and Genista shrublands tended to be mostly brachypterous and to have geographic ranges restricted to northern Spain. In contrast, we found no relationship between the degree of species' association with pastures and geographic range-wing size type. Although the species richness was higher in riparian woodlands and mown meadows, we suggest a higher conservation value for subalpine meadows and Genista shrublands across the landscape because they sustain characteristic assemblages dominated by species with restricted ranges and reduced powers of dispersal. Our study suggests that preserving areas in the landscape supporting higher biodiversity will not necessarily preserve those species potentially more susceptible to habitat loss and fragmentation. It also supports the feasibility of biodiversity studies based on multivariate techniques for setting conservation priorities over complex landscapes.     

Detection of Total Phosphorus Concentrations of Turbid Inland Waters Using a Remote Sensing Method

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009?~?2011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1 % for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.     

Incorporating connectivity into conservation planning: A multi-criteria case study from central Mexico

The interdigitation of the Nearartic and Neotropical biogeographic zones in the Transvolcanic Belt (TVB) of central Mexico provides the region with high faunal richness and endemicity. Biodiversity conservation in the TVB must accommodate the region’s human population of more than 40 million. The current study presents conservation plans for the TVB intended to protect 99 non-volant mammal species while minimizing the impact on the human population. A rarity-complementarity algorithm was used to select a conservation area network (CAN) from sites with untransformed vegetation to represent 10% of each species’ habitat. In addition, a new method was developed for augmenting the connectivity of CANs using graph theory. External sites were assigned quality scores based on the frequency with which they were selected at different targets of representation for species. Graph algorithms identified the highest-quality sites needed to link all conservation areas in an economical manner. These connectivity areas can facilitate migration or egress of biota in the event of local environmental stress. The network initialized with existing protected areas occupied 9.13% of the TVB, whereas the network built from scratch occupied 6.02%. In both cases, an additional area of only about 1.5% of the region was required to link all conservation areas in the network. Finally, a multiple criterion synchronization technique was used to select those connected networks which minimized both total area and human population impact.     

Incorporating connectivity into reserve selection procedures

Methods for selecting sites to be included in reserve networks generally neglect the spatial location of sites, often resulting in highly fragmented networks. This restricts the possibility of dispersal between sites, which for many species may be essential for long-term persistence. Here I describe iterative reserve selection algorithms which incorporate considerations of reserve connectivity and evaluate their performance using a data set for macroinvertebrates in ponds. Methods where spatial criteria were only invoked when ties between sites occurred did not perform significantly better than a simple greedy algorithm in terms of reserve connectivity. An algorithm based on a composite measure of species added and changes in reserve connectivity produced a reserve network with higher connectivity, but needed more sites to represent all species. A trade-off between connectivity and efficiency may be inevitable, but the costs in terms of efficiency may be justified if long-term persistence of species is more likely.     

Three new algorithms to calculate the irreplaceability index for presence/absence data

The irreplaceability index is used for determining the priority of sites for conservation. We propose three new and simple algorithms for calculating it from presence/absence data without any statistical assumptions when the representative combination size is the minimum. The rare species algorithm gives accurate results but takes the longest calculation time. The species richness algorithm gives approximated results and takes the shortest calculation time. The combined algorithm is intermediate in accuracy and calculation time. We applied the combined algorithm to real data of butterflies in the Kanto district, central Japan, and derived irreplaceability values.     

Spatial optimization of soil and water conservation practices using coupled SWAT model and evolutionary algorithm

Soil and water conservation practices have been extensively used in effective watershed management. The impact of each conservation practice is site specific and dependent on the implementation site in the watershed. In order to select cost effective placement of conservation practices with high impact, a large number of spatial combinations is needed to be compared. In this study, an optimization model framework is presented to find cost effective solutions for sediment yield and runoff control in the Fariman dam watershed in the Northeast of Iran. This was accomplished by integrating soil and water assessment tool (SWAT) for simulation of watershed hydrology and multi objective genetic algorithm (NSGA-II) for spatial optimization of soil and water conservation practices. The optimized solutions provided a trade-off between the two objective functions. The final Pareto-optimal shows that the impact of soil and water conservation practices on sediment yield is more than stream flow. The trade-offs between the objective functions show that the implementation of the median cost can lead to a significant decrease of 22.1% in the amount of sediment yield, and 10% in stream flow. Also, percent change achieved through median cost is very close to percent reduction with the highest cost. Results of low cost solution show that the vegetative practices are a suitable economic scenario for soil and water conservation. The introduced framework can be adapted as a suitable tool for selecting cost effective conservation practices in different regions.