Impact of atmospheric pollutants on the current and future status of protected habitats.

环境署有法定责任确保工业作业不会对《1992年欧盟栖息地指令》和《2010年栖息地和物种保护条例》保护的栖息地和物种产生不可接受的影响。集约化畜牧单位是一种特殊类型的工业过程，需要根据《环境许可条例》（英格兰和威尔士，2010年）向环境局申请许可。在这个过程中，英国自然资源部、威尔士自然资源部和环境保护局都是咨询方。在实验和空间调查中，许多研究将农业和化石燃料的氮沉积对生态系统的破坏与生物多样性、生态系统服务、氮饱和度和淡水浸出的影响联系起来（Phoenix et al . 2012; Stevens et al . 2010）。目前，大约60%的英国受保护地点预计将超过其临界负荷（RoTAP 2012），但在许多地方，n的直接影响的明显迹象有限。这种科学与现场证据之间的矛盾给监管机构带来了立法问题，混淆了植被与污染之间的剂量-反应关系。基于当代污染的模型显示出曲线线性（Field et al . 2014）、线性（Maskill et al . 2010）和阈值响应（Tipping et al . 2013）。然而，氮沉降的影响被认为是随着时间的推移而累积的（Phoenix et al 2012），当空间调查的物种丰富度值使用累积氮进行建模时，这种关系变得更加线性。目前尚不清楚这种关系类型的变化是生态的还是数学的，两者的合法性都需要进一步调查。使用累积N作为当代污染沉积的替代方法也提出了一些问题，因为假设累积N是一个覆盖的、跨栖息地的响应。不同的栖息地和这些栖息地内的植物功能群对长期污染的响应时间尺度不同，从几年到几个世纪不等（Evans et al . 2006; Posch et al . 2011）。在准确计算累积沉降方面也存在问题，对近几十年来N沉降增加的地区的长期污染估计可能过高，而对历史上高污染地区的估计可能过低（Fowler et al . 2005）。NERC资助的长期大规模项目正试图更准确地模拟200年累积沉积。使用MAGIC、GBMOVE和For-SAFE VEG等模型的动态建模也可以通过计算特定栖息地的“N限制”来提供一些解决方案，从而通过添加时间元素来定制N响应（Evans et al . 2006）。最近对模型的测试强调了它们在预测植物物种发生（Rowe et al . 2011）和覆盖（De Vries et al . 2010）方面的潜力。动态建模的一个有趣应用是从模拟的植被数据中反向计算临界负荷，其优点是可以考虑未来的情景（例如污染物减少）。动态建模的缺点包括对直接对氮作出反应的低等植物的预测较差（Smart et al . 2005），以及缺乏对不同形式氮的考虑，特别是集约化养殖单位周围高浓度氨的考虑（Sheppard et al . 2011）。最近的研究也表明，绝对的生态系统临界负荷是不可能的，而是提出了沉积范围，使土壤性质进入植物物种的最佳区域（Posch et al, 2011）。有一些证据表明，在调查中已经发生了这种情况，这些地点位于pH值阈值上，容易受到阶跃变化的影响（Field et al . 2014），许多物种出现在沉积范围内，尽管在变化点以上似乎有所下降（Payne et al . 2013）。

The Environment Agency has a statutory duty to ensure that operations carried out by industrial operations do not have an unacceptable impact on habitats and species protected under the EU Habitats Directive 1992 and the Conservation of Habitats & Species Regulations 2010. Intensive livestock units are a particular type of industrial process that are required to apply to the Environment Agency for a permit under the Environmental Permitting Regulations (England and Wales, 2010). Natural England, Natural Resources Wales, and SEPA are consultees in this process. Much research has linked ecosystem damage to nitrogen deposition from agriculture and fossil fuels with demonstrated impacts on biodiversity, ecosystem services, N saturation and the onset of leaching to freshwaters, in experiments and spatial surveys (Phoenix et al 2012; Stevens et al 2010). Currently, around 60% of UK protected sites are expected to exceed their critical load (RoTAP 2012), yet at many there are limited obvious signs of direct impact from N.This contradiction between the science and site based evidence creates a legislative problem for regulatory bodies with confusion in the dose-response relationships between vegetation and pollution. Modelling based around contemporary pollution suggests a curvi-linear (Field et al 2014), linear (Maskill et al 2010), and threshold responses (Tipping et al 2013). However, the effects of N deposition are thought to be cumulative over time (Phoenix et al 2012) and when species richness values from spatial surveys are modelled using cumulative N, the relationship becomes more linear. It is not clear if this change in relationship type is ecological or mathematical and the legitimacy of either needs further investigation.The use of cumulative N as an alternative to contemporary pollution deposition also raises questions due to the assumption of a blanket, cross-habitat response to cumulative N. Different habitats and the plant functional groups within those habitats respond to long-term pollution over different timescales ranging from a few years to centuries (Evans et al 2006; Posch et al 2011). There are also issues in accurately calculating cumulative deposition, with estimates of long-term pollution to areas which have experienced increases in N deposition over recent decades likely to be too high, whilst estimates for areas with historically high pollution likely to be too low (Fowler et al 2005). The NERC funded Long term Large Scale Project is attempting to more accurately model 200 year cumulative deposition. Dynamic modelling using models incl. MAGIC, GBMOVE and For-SAFE VEG may also offer some solutions to this problem by enabling specific habitat "N-limits" to be calculated, thereby, customising the N response through the addition of a temporal element (Evans et al 2006). Recent testing of models highlights their potential to predict plants species occurrence (Rowe et al 2011) and cover (De Vries et al 2010). An interesting application of dynamic modelling is the reverse calculation of critical loads from modelled vegetation data, with the advantage that future scenarios can be considered (e.g. pollutant reduction). Disadvantages of dynamic modelling include poor forecasting of lower plants that respond directly to N (Smart et al 2005), and a lack of consideration of the different forms of N, particularly the high concentrations of ammonia around intensive farming units (Sheppard et al 2011).Recent research has also suggested that an absolute ecosystem critical load is unlikely, proposing instead ranges of deposition that drive soil properties into optimum zones for plant species (Posch et al 2011). There is some evidence that this has occurred in surveys, with sites resting on a pH threshold, vulnerable to a step-changes (Field et al 2014), and many species occurring over a deposition range though appearing to decline above a change point (Payne et al 2013).