空间直观景观模型LANDIS的灵敏度分析

灵敏度分析是模型验证过程中十分重要的一步，通过将模型中灵敏度高的参数与灵敏度低的参数加以区分，在调整模型参数时只需调整那些对改变模型结果有显著作用的参数，从而能够减轻模型校正过程中的工作量。首先借助于地理信息系统技术和遥感技术，综合林相图、TM影像数据以及林业调查数据库，在大兴安岭呼中林业局为LANDIS模型进行初步建模。在对模型的结构及特点进行分析的基础上，对模型从输入参数的两个部分一空间数据和非空间数据的灵敏度分别展开分析。对空间数据进行灵敏度分析的结果表明，输入图形文件的分辨率对LAND工S模型的模拟结果有显著影响；对非空间数据中几个与树种生活史特征以及千扰特征相关的参数进行灵敏度分析的结果表明，树种寿命、种子有效传播距离以及火干扰概率系数和火干扰面积系数都是较为敏感的参数，而种子最大传播距离及成熟年龄在其不确定性范围内是不敏感的参数。

空间直观景观模型LANDISⅠ.运行机制

空间直观景观模型是指在异质景观中模拟景观尺度上生态过程的空间直观模型 .LANDIS是一个用于模拟森林景观干扰、演替和管理的空间直观景观模型 .通过在样地尺度上跟踪以 10年为间隔的物种年龄级 ,半定量化地描述火和风倒 ,及使用位数组表示物种年龄结构 ,LANDIS能同时在物种、样地和景观尺度上模拟各种生态过程及其相互关系 .详细论述了LANDIS模型对种子传播、火、风倒和砍伐等生态过程的模拟 ,并讨论了模型中存在的一些不足 .

空间直观景观模型LANDIS在大兴安岭呼中林区的应用

应用空间直观景观模型 (L ANDIS) ,研究有采伐和无采伐预案下大兴安岭呼中林区的森林景观的长期变化。用 APACK计算每一个物种及各年龄级的分布面积。为了研究物种分布格局的变化 ,计算了物种分布的聚集度指数。研究结果如下 :(1)在无采伐预案下 ,火干扰模式为低频率大面积高强度火烧 ;在有采伐预下 ,火干扰模式为高频率小面积低强度火烧 ;(2 )在无采伐预案下 ,火会造成各种群分布面积的强烈波动 ,但是对种群的年龄结构没有很大影响 ;在有采伐预案下 ,火对种群分布面积和年龄结构都没有很大的影响 ;(3)采伐能完全改变各种群的年龄结构 ,降低种群分布的聚集度 ,但是对各种群的分布面积并没有很大影响 ;(4 )在有采伐预案下 ,各种群为增长型种群 ,增长量通过采伐取走 ,群落处于演替的干扰顶极状态 ;在无采伐预案下 ,各种群为稳定型种群 (樟子松和偃松除外 ) ,大面积高强度火烧使群落产生较大的波动。结果表明 ,在呼中林业局 ,在没有人为干扰情况下 ,火干扰是森林景观变化的主导因素。自从有了人为干扰 ,采伐开始逐渐取代火干扰成为影响森林景观变化的主导因素。空间直观景观模型的一个挑战是模型的验证。由于缺乏详细的空间数据及模型模拟中的随机性 ,很难通过模型模拟结果与实地调查或遥感数据的比较进行

Synergies and goal conflicts for climate change policy and spatial planning

The purpose of this paper is to identify goal conflicts – both actual and potential – between climate and social policies in government strategies in response to the growing significance of climate change as a socioecological issue (IPCC 2007). Both social and climate policies are political responses to long-term societal trends related to capitalist development, industrialisation, and urbanisation (Koch, 2012). Both modify these processes through regulation, fiscal transfers and other measures, thereby affecting conditions for the other. This means that there are fields of tensions and synergies between social policy and climate change policy. Exploring these tensions and synergies is an increasingly important task for navigating genuinely sustainable development. Gough et al (2008) highlight three potential synergies between social and climate change policies: First, income redistribution – a traditional concern of social policy – can facilitate use of and enhance efficiency of carbon pricing. A second area of synergy is housing, transport, urban policies and community development, which all have potential to crucially contribute towards reducing carbon emissions. Finally, climate change mitigation will require substantial and rapid shifts in producer and consumer behaviour. Land use planning policy is a critical bridge between climate change and social policy that provides a means to explore the tensions and synergies that are evolving within this context. This paper will focus on spatial planning as an opportunity to develop strategies to adapt to climate change, and reviews the challenges of such change. Land use and spatial planning involve the allocation of land and the design and control of spatial patterns. Spatial planning is identified as being one of the most effective means of adapting settlements in response to climate change (Hurlimann and March, 2012). It provides the instrumental framework for adaptation (Meyer, et al., 2010) and operates as both a mechanism to achieve adaptation and a forum to negotiate priorities surrounding adaptation (Davoudi, et al., 2009). The acknowledged role of spatial planning in adaptation however has not translated into comparably significant consideration in planning literature (Davoudi, et al., 2009; Hurlimann and March, 2012). The discourse on adaptation specifically through spatial planning is described as ‘missing’ and ‘subordinate’ in national adaptation plans (Greiving and Fleischhauer, 2012),‘underrepresented’ (Roggema, et al., 2012)and ‘limited and disparate’ in planning literature (Davoudi, et al., 2009). Hurlimann and March (2012) suggest this may be due to limited experiences of adaptation in developed nations while Roggema et al. (2012) and Crane and Landis (2010) suggest it is because climate change is a wicked problem involving an unfamiliar problem, various frames of understanding and uncertain solutions. The potential for goal conflicts within this policy forum seem to outweigh the synergies. Yet, spatial planning will be a critical policy tool in the future to both protect and adapt communities to climate change.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. Rev. , 36 (2--3), 2001, 108--118. doi:10.1016/S0165-0173(01)00086-8 S. Ogawa, R. S. Menon, S. G. Kim and K. Ugurbil, On the characteristics of functional magnetic resonance imaging of the brain, Annu. Rev. Bioph. Biom. , 27 , 1998, 447--474. doi:10.1146/annurev.biophys.27.1.447 C. D. Binnie and H. Stefan, Modern electroencephalography: its role in epilepsy management, Clin. Neurophysiol. , 110 (10), 1999, 1671--1697. doi:10.1016/S1388-2457(99)00125-X J. X. Tao, A. Ray, S. Hawes-Ebersole and J. S. Ebersole, Intracranial eeg substrates of scalp eeg interictal spikes, Epilepsia , 46 (5), 2005, 669--76. doi:10.1111/j.1528-1167.2005.11404.x S. Ogawa, D. W. Tank, R. Menon, J. M. Ellermann, S. G. Kim, H. Merkle and K. Ugurbil, Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging, P. Natl. Acad. Sci. USA , 89 (13), 1992, 5951--5955. doi:10.1073/pnas.89.13.5951 J. Engel Jr., Report of the ilae classification core group, Epilepsia , 47 (9), 2006, 1558--1568. doi:10.1111/j.1528-1167.2006.00215.x L. Lemieux, A. Salek-Haddadi, O. Josephs, P. Allen, N. Toms, C. Scott, K. Krakow, R. Turner and D. R. Fish, Event-related fmri with simultaneous and continuous eeg: description of the method and initial case r port, NeuroImage , 14 (3), 2001, 780--7. doi:10.1006/nimg.2001.0853 P. Federico, D. F. Abbott, R. S. Briellmann, A. S. Harvey and G. D. Jackson, Functional mri of the pre-ictal state, Brain , 128 (8), 2005, 1811-7. doi:10.1093/brain/awh533 C. S. Hawco, A. P. Bagshaw, Y. Lu, F. Dubeau and J. Gotman, bold changes occur prior to epileptic spikes seen on scalp eeg, NeuroImage , 35 (4), 2007, 1450--1458. doi:10.1016/j.neuroimage.2006.12.042 F. Moeller, H. R. Siebner, S. Wolff, H. Muhle, R. Boor, O. Granert, O. Jansen, U. Stephani and M. Siniatchkin, Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges, NeuroImage , 39 (4), 2008, 1839--1849. doi:10.1016/j.neuroimage.2007.10.058 V. Osharina, E. Ponchel, A. Aarabi, R. Grebe and F. Wallois, Local haemodynamic changes preceding interictal spikes: A simultaneous electrocorticography (ecog) and near-infrared spectroscopy (nirs) analysis in rats, NeuroImage , 50 (2), 2010, 600--607. doi:10.1016/j.neuroimage.2010.01.009 R. S. Fisher, W. Boas, W. Blume, C. Elger, P. Genton, P. Lee and J. Engel, Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ilae) and the international bureau for epilepsy (ibe), Epilepsia , 46 (4), 2005, 470--472. doi:10.1111/j.0013-9580.2005.66104.x H. Berger, Electroencephalogram in humans, Arch. Psychiat. Nerven. , 87 , 1929, 527--570. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli and R. G. de Peralta, eeg source imaging, Clin. Neurophysiol. , 115 (10), 2004, 2195--2222. doi:10.1016/j.clinph.2004.06.001 P. L. Nunez and R. B. Silberstein, On the relationship of synaptic activity to macroscopic measurements: Does co-registration of eeg with fmri make sense?, Brain Topogr. , 13 (2), 2000, 79--96. doi:10.1023/A:1026683200895 S. Ogawa, T. M. Lee, A. R. Kay and D. W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation, P. Natl. Acad. Sci. USA , 87 (24), 1990, 9868--9872. doi:10.1073/pnas.87.24.9868 J. S. Gati, R. S. Menon, K. Ugurbil and B. K. Rutt, Experimental determination of the bold field strength dependence in vessels and tissue, Magn. Reson. Med. , 38 (2), 1997, 296--302. doi:10.1002/mrm.1910380220 P. A. Bandettini, E. C. Wong, R. S. Hinks, R. S. Tikofsky and J. S. Hyde, Time course EPI of human brain function during task activation, Magn. Reson. Med. , 25 (2), 1992, 390--397. K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppelm, M. S. Cohen and R. Turner, Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation, P. Natl. Acad. Sci. USA , 89 (12), 1992, 5675--5679. doi:10.1073/pnas.89.12.5675 J. Frahm, K. D. Merboldt and W. Hnicke, Functional mri of human brain activation at high spatial resolution, Magn. Reson. Med. , 29 (1), 1993, 139--144. P. A. Bandettini, A. Jesmanowicz, E. C. Wong and J. S. Hyde, Processing strategies for time-course data sets in functional MRI of the human brain, Magn. Reson. Med. , 30 (2), 1993, 161--173. K. J. Friston, P. Jezzard and R. Turner, Analysis of functional MRI time-series, Hum. Brain Mapp. , 1 (2), 1994, 153--171. B. Biswal, F. Z. Yetkin, V. M. Haughton and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar mri, Mag. Reson. Med. , 34 (4), 1995, 537--541. doi:10.1002/mrm.1910340409 K. J. Friston, J. Ashburner, C. D. Frith, J. Poline, J. D. Heather and R. S. J. Frackowiak, Spatial registration and normalization of images, Hum. Brain Mapp. , 3 (3), 1995, 165--189. K. J. Friston, S. Williams, R. Howard, R. S. Frackowiak and R. Turner, Movement-related effects in fmri time-series, Magn. Reson. Med. , 35 (3), 1996, 346--355. G. H. Glover, T. Q. Li and D. Ress, Image-based method for retrospective correction of physiological motion effects in fmri: Retroicor, Magn. Reson. Med. , 44 (1), 2000, 162--167. doi:10.1002/1522-2594(200007)44:13.0.CO;2-E K. J. Friston, O. Josephs, G. Rees and R. Turner, Nonlinear event-related responses in fmri, Magn. Reson. Med. , 39 (1), 1998, 41--52. doi:10.1002/mrm.1910390109 K. Ugurbil, L. Toth and D. Kim, How accurate is magnetic resonance imaging of brain function?, Trends Neurosci. , 26 (2), 2003, 108--114. doi:10.1016/S0166-2236(02)00039-5 D. S. Kim, I. Ronen, C. Olman, S. G. Kim, K. Ugurbil and L. J. Toth, Spatial relationship between neuronal activity and bold functional mri, NeuroImage , 21 (3), 2004, 876--885. doi:10.1016/j.neuroimage.2003.10.018 A. Connelly, G. D. Jackson, R. S. Frackowiak, J. W. Belliveau, F. Vargha-Khadem and D. G. Gadian, Functional mapping of activated human primary cortex with a clinical mr imaging system, Radiology , 188 (1), 1993, 125--130. L. Allison, Hidden Markov Models, Technical Report , School of Computer and Software Engineering, Monash University, 2000. R. J. Elliott, L. Aggoun and J.B. Moore, Hidden Markov Models: Estimation and Control, Appl. Math.-Czech. , 2004. B. Bhavnagri, Discontinuities of plane functions projected from a surface with methods for finding these , Technical Report, 2009. B. Bhavnagri, Computer Vision using Shape Spaces , Technical Report,1996, University of Adelaide. B. Bhavnagri, A method for representing shape based on an equivalence relation on polygons, Pattern Recogn. , 27 (2), 1994, 247--260. doi:10.1016/0031-3203(94)90057-4 D. F. Abbott, A. B. Waites, A. S. Harvey and G. D. Jackson, Exploring epileptic seizure onset with fmri, NeuroImage , 36(S1) (344TH-PM), 2007. M. C. Mackey and L. Glass, Oscillation and chaos in physiological control systems, Science , 197 , 1977, 287--289. S. H. Strogatz, SYNC - The Emerging Science of Spontaneous Order , Theia, New York, 2003. J. W. Kim, J. A. Roberts and P. A. Robinson, Dynamics of epileptic seizures: Evolution, spreading, and suppression, J. Theor. Biol. , 257 (4), 2009, 527--532. doi:10.1016/j.jtbi.2008.12.009 Y. Kuramoto, T. Aoyagi, I. Nishikawa, T. Chawanya T and K. Okuda, Neural network model carrying phase information with application to collective dynamics, J. Theor. Phys. , 87 (5), 1992, 1119--1126. V. B. Mountcastle, The columnar organization of the neocortex, Brain , 120 (4), 1997, 701. doi:10.1093/brain/120.4.701 F. L. Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity, Epilepsia , 44 (12), 2003, 72--83. F. H. Lopes da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Dynamical diseases of brain systems: different routes to epileptic seizures, ieee T. Bio-Med. Eng. , 50 (5), 2003, 540. L.D. Iasemidis, Epileptic seizure prediction and control, ieee T. Bio-Med. Eng. , 50 (5), 2003, 549--558. L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principe, P. R. Carney, A. Prasad, B. Veeramani, and K. Tsakalis, Adaptive epileptic seizure prediction system, ieee T. Bio-Med. Eng. , 50 (5), 2003, 616--627. K. Lehnertz, F. Mormann, T. Kreuz, R.G. Andrzejak, C. Rieke, P. David and C. E. Elger, Seizure prediction by nonlinear eeg analysis, ieee Eng. Med. Biol. , 22 (1), 2003, 57--63. doi:10.1109/MEMB.2003.1191451 K. Lehnertz, R. G. Andrzejak, J. Arnhold, T. Kreuz, F. Mormann, C. Rieke, G. Widman and C. E. Elger, Nonlinear eeg analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention, J. Clin. Neurophysiol. , 18 (3), 2001, 209. B. Litt and K. Lehnertz, Seizure prediction and the preseizure period, Curr. Opin. Neurol. , 15 (2), 2002, 173. doi:10.1097/00019052-200204000-00008 B. Litt and J. Echauz, Prediction of epileptic seizures, Lancet Neurol. , 1 (1), 2002, 22--30. doi:10.1016/S1474-4422(02)00003-0 M. M{a}kiranta, J. Ruohonen, K Suominen, J. Niinim{a}ki, E. Sonkaj{a}rvi, V. Kiviniemi, T. Sepp{a}nen, S. Alahuhta, V. J{a}ntti and O. Tervonen, {bold} signal increase preceeds eeg spike activity--a dynamic penicillin induced focal epilepsy in deep anesthesia, NeuroImage , 27 (4), 2005, 715--724. doi:10.1016/j.neuroimage.2005.05.025 K. Lehnertz, F. Mormann, H. Osterhage, A. M{u}ller, J. Prusseit, A. Chernihovskyi, M. Staniek, D. Krug, S. Bialonski and C. E. Elger, State-of-the-art of seizure prediction, J. Clin. Neurophysiol. , 24 (2), 2007, 147. doi:10.1097/WNP.0b013e3180336f16 F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, C. E. Elger and K. Lehnertz, On the predictability of epileptic seizures, Clin. Neurophysiol. , 116 (3), 2005, 569--587. doi:10.1016/j.clinph.2004.08.025 F. Mormann, R. G. Andrzejak, C. E. Elger and K. Lehnertz, Seizure prediction: the long and winding road, Brain , 130 (2), 2007, 314--333. doi:10.1093/brain/awl241 Z. Rogowski, I. Gath and E. Bental, On the prediction of epileptic seizures, Biol. Cybern. , 42 (1), 1981, 9--15. Y. Salant, I. Gath, O. Henriksen, Prediction of epileptic seizures from two-channel eeg, Med. Biol. Eng. Comput. , 36 (5), 1998, 549--556. doi:10.1007/BF02524422 J. Gotman and D.J. Koffler, Interictal spiking increases after seizures but does not after decrease in medication, Evoked Potential , 72 (1), 1989, 7--15. J. Gotman and M. G. Marciani, Electroencephalographic spiking activity, drug levels, and seizure occurence in epileptic patients, Ann. Neurol. , 17 (6), 1985, 59--603. A. Katz, D. A. Marks, G. McCarthy and S. S. Spencer, Does interictal spiking change prior to seizures?, Electroen. Clin. Neuro. , 79 (2), 1991, 153--156. A. Granada, R. M. Hennig, B. Ronacher, A. Kramer and H. Herzel, Phase Response Curves: Elucidating the dynamics of couples oscillators, Method Enzymol. , 454 (A), 2009, 1--27. doi:10.1016/S0076-6879(08)03801-9 doi:10.1016/S0076-6879(08)03801-9 H. Kantz and T. Schreiber, Nonlinear time series analysis , 2004, Cambridge Univ Press. M. V. L. Bennett and R. S Zukin, Electrical coupling and neuronal synchronization in the mammalian brain, Neuron , 41 (4), 2004, 495 --511. doi:10.1016/S0896-6273(04)00043-1 L.D. Iasemidis, J. Chris Sackellares, H. P. Zaveri and W. J. Williams, Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures, Brain Topogr. , 2 (3), 1990, 187--201. doi:10.1007/BF01140588 M. Le Van Quyen, J. Martinerie, V. Navarro, M. Baulac and F. J. Varela, Characterizing neurodynamic changes before seizures, J. Clin. Neurophysiol. , 18 (3), 2001, 191. J. Martinerie, C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault and F. J. Varela, Epileptic seizures can be anticipated by non-linear analysis, Nat. Med. , 4 (10), 1998, 1173--1176. doi:10.1038/2667 A. Pikovsky, M. Rosenblum, J. Kurths and R. C. Hilborn, Synchronization: A universal concept in nonlinear science, Amer. J. Phys. , 70 , 2002, 655. H. R. Wilson and J. D. Cowan, Excitatory and inhibitory interactions in localized populations of model neurons, Biophys. J. , 12 (1), 1972, 1--24. D. Cumin and C. P. Unsworth, Generalising the Kuramoto model for the study of neuronal synchronisation in the brain, Physica D , 226 (2), 2007, 181--196. doi:10.1016/j.physd.2006.12.004 F. K. Skinner, H. Bazzazi and S. A. Campbell, Two-cell to N-cell heterogeneous, inhibitory networks: Precise linking of multistable and coherent properties, J. Comput. Neurosci. , 18 (3), 2005, 343--352. doi:10.1007/s10827-005-0331-1 W. W. Lytton, Computer modelling of epilepsy, Nat. Rev. Neurosci. , 9 (8), 2008, 626--637. doi:10.1038/nrn2416 R. D. Traub, A. Bibbig, F. E. N. LeBeau, E. H. Buhl and M. A. Whittington, Cellular mechanisms of neuronal population oscillations in the hippocampus in vitro, Ann. Rev. , 2004. R. D. Traub, A. Draguhn, M. A. Whittington, T. Baldeweg, A. Bibbig, E. H. Buhl and D. Schmitz, Axonal gap junc ions between principal neurons: A novel source of network oscillations, and perhaps epileptogenesis., Rev. Neuroscience , 13 (1), 2002, 1. doi:10.1146/annurev.neuro.27.070203.144303 M. Scheffer, J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. van Nes, M. Rietkerk and G. Sugihara, Early-warning signals for critical transitions, Nature , 461 (7260), 2009, 53--59. doi:10.1038/nature08227 K. Murphy, A Brief Introduction to Graphical Models and Bayesian Networks , 2008, http://www.cs.ubc.ca/murphyk/Bayes/bnintro.html . R. C. Bradley, An elementary

Smell and taste dysfunction following minor stroke: A case report

Smell (olfactory) and taste (gustatory) are key senses in the regulation of nourishment and individual safety. Olfactory and gustatory dysfunctions have been infrequently reported together in patients following stroke (Landis et al., 2006; Leopold et al., 2006). This case report details two patients who experienced smell and taste dysfunction following minor stroke events. Symptoms reported included hyposmia (diminished sense of smell) and anosmia (complete loss of smell), and dysgeusia (distorted taste). Patients' sense of smell and taste were assessed in an ambulatory care stroke prevention clinic eight months following their strokes. Patient A presented with minor stroke due to a lesion in the anterior circulation, patient B with a lesion in the posterior circulation. Both patients reported intense olfactory and gustatory dysfunction immediately following their strokes. Examination revealed a general inability to detect subtle odours and the ability to identify only 'sweet' tastes for both patients. In addition, both patients reported heavily salting or sweetening their food to mask the distorted and unpleasant taste, which also impacted comorbid conditions such as hypertension and diabetes. Patients and their spouses reported a decrease in their appreciation of family-related activities due to the patients' olfactory and gustatory dysfunction. Patients reported weight loss, lack of energy and strength, likely due to poor nutrition. Olfactory and gustatory dysfunctions are potentially deleterious outcomes following minor stroke and should be assessed by health care professionals prior to patient discharge. Assistance may be required to promote the health and well-being of patients and their carers if smell and taste are impacted by the stroke event.

Continuous synthesis of high quality CdSe quantum dots in supercritical fluids

We demonstrate in here a powerful scalable technology to synthesize continuously high quality CdSe quantum dots (QDs) in supercritical hexane. Using a low cost, highly thermally stable Cd-precursor, cadmium deoxycholate, the continuous synthesis is performed in 400 mu m ID stainless steel capillaries resulting in CdSe QDs having sharp full-width-at-half-maxima (23 nm) and high photoluminescence quantum yields (45-55%). Transmission electron microscopy images show narrow particles sizes distribution (sigma <= 5%) with well-defined crystal lattices. Using two different synthesis temperatures (250 degrees C and 310 degrees C), it was possible to obtain zinc blende and wurtzite crystal structures of CdSe QDs, respectively. This synthetic approach allows achieving substantial production rates up to 200 mg of QDs per hour depending on the targeted size, and could be easily scaled to gram per hour.

空间直观森林景观模型不确定性分析

过去的十年出现了大量的空间直观森林景观模型。空间直观森林景观模型模拟大尺度上的空间生态过程(演替、火、采伐和病虫害等)，它需要大尺度空间数据的输入和大量模型参数的估计。由于空间数据的缺乏，在参数化过程中会有很多的人为误差从而导致模型模拟结果的不确定性(主观不确定性)。此外，为了表现自然系统的随机性，大部分空间直观景观模型采用了随机模拟的方式。随机模拟也会使模型在相同参数下具有不同的模拟结果(随机不确定性)。 本文采用蒙特卡罗模拟、拉丁超几何体采样、地统计学随机模拟及方差分析的方法，系统研究了主观不确定性和随机不确定性在模型中传递和影响。结果表明， 1) 用小班尺度上的物种年龄组信息初始化象元尺度上的物种年龄组信息对LANDIS模型景观尺度上的模拟结果没有显著的影响，但是对象元尺度上的模拟结果有显著的影响； 2) 用克吕格空间插值所获取的物种年龄信息可用于LANDIS模型对景观组分和格局的长期模拟； 3) 随机不确定性的存在使LANDIS模型不适用于小尺度上的景观格局变化的模拟，但是随机不确定性不影响LANDIS模型在大尺度上的景观组分和格局的长期模拟； 4) 随机不确定性要明显低于主观不确定性，随机不确定性和主观不确定性存在显著的相互作用。

小兴安岭地区主要树种对气候变暖的响应

小兴安岭是温带和寒温带的过渡地带，属于针阔混交林（阔叶红松林）与北方针叶林（兴安落叶松林）的过渡区域，树种种类众多，是气候变暖的预警区。在CGCM2气候预案下，研究区内，年平均温度增加5.97±0.14℃，年平均降水量减少16.2±6.2mm。本文联合静态模型Logistic和动态模型LANDIS，模拟气候变暖、火干扰、采伐和种植对树种分布的影响。 Logistic回归模型结果显示，在区域尺度上，气候因子（年平均温度和降水量）是影响大部分树种潜在分布的最重要的环境因子，如枫桦、黑桦、红松、冷杉、蒙古栎、山杨和云杉。少数树种对地形因子最为敏感，如落叶松，色木槭、紫椴和白桦。当空间尺度从区域尺度降低到局域尺度时，树种与环境因子之间的关系发生变化，对树种分布最具影响的环境因子发生转移。由气候因子逐渐转移为地形因子，如对红松分布最具影响的年降水量转移为坡向。但有些地区气候因子仍然是树种分布的最重要的环境因子。气候变暖与尺度效应相同，也会改变树种与环境因子间的关系，并且最具影响的环境因子也发生转移。 静态和动态模型的联合模拟结果显示，气候变暖后，红松、蒙古栎、色木槭、枫桦和紫锻北界向北迁移；落叶松、黑桦南界向北迁移；云杉分布基本没有变化，冷杉向高海拔地区迁移。虽然研究区内树种分布发生显著变化，但树种组成没有变化。气候变暖对消退型树种分布的影响最快（30年），对扩展型树种的影响较慢，其中阔叶扩展型树种对气候变暖的反应较快（40~70年），针叶树种对气候变暖的反应较慢（100~150年）。气候变暖和火干扰的作用下，扩展型树种一直增加，只有增长阶段没有消退阶段，而消退型树种，只有消退阶段没有增长阶段。树种分布变化率与树种耐火性之间没有显著相关关系，而与树种萌发力、最小萌发年龄和结实年龄之间存在显著相关关系。最后，火干扰促进耐火性强、萌发力强、最小萌发年龄小、结实年龄小的树种扩展，延缓通过种子传播、耐火性弱、结实年龄大的树种扩展。在树种年龄结构上，幼龄组比例大量增加，最终，所有龄组分布趋向稳定。而采伐使树种年龄结构趋于简单，在商品林区内年龄大于采伐年龄的树木几乎不存在。不过，火干扰和采伐都减少针叶树种的分布，增加阔叶树树种的分布。在气候变暖和采伐的作用下，消退型树种提前消退，扩展型树种扩展阶段提前。火干扰在时间上没有提前或延缓气候变暖对树种分布的影响，而采伐将提前气候变暖对树种分布的影响190年，森林提前70年从演替初期进入演替中期。种植消退型树种（如落叶松）不仅消除因采伐而提前的190年，还能保持一定量的分布，进而阻碍气候变暖对树种分布的影响。 比较两个模型结果发现气候变暖后，火干扰减少红松的迁移速度22m/Yr.，减少枫桦的8m/Yr.，而采伐不影响红松和枫桦迁移速度。种植增加红松迁移速度4~7m/Yr.，减少枫桦迁移速度5~7m/Yr.。干扰对树种迁移速度的影响与树种可通过的廊道有关。 气候变暖、火干扰和采伐的共同作用下，火烧轮回期显著减少4年，而平均火烧面积显著增加13.0hm2。种植显著影响火烧轮回期和平均火烧面积，种植红松和多个树种使火烧轮回期有所增加，在种植落叶松预案下，有所减少；种植红松、云杉和多个树种使平均火烧面积显著减少。火干扰减少采伐面积，而气候变暖增加采伐面积。但因过去超量采伐，如今树木年龄小，无法满足采伐条件，从而削弱气候变暖对采伐的影响。今后60年之内气候变暖对采伐面积没有任何影响，相反因火干扰而采伐面积减少。从2080年开始，气候变暖而扩展的阔叶树种达到采伐年龄，采伐面积开始增加；从2110年开始，在采伐迹地上种植的落叶松达到采伐年龄，采伐面积再次增加；从2130年开始，种植的云杉和红松达到采伐年龄，采伐面积再度增加。 最后，对两个模型结果进行了详细分析，讨论了干扰之间的相互关系，干扰与作用区域，树种分布变化与树种生物学特性之间的关系。提出了些有关干扰调控，生物多样性保护，森林管理方面的设想。

大兴安岭北坡森林景观火后恢复及演替模式研究

1987年大兴安岭北坡发生的特大森林火灾，过火面积达1.33×106ha,如何在如此大面积的火烧迹地上恢复森林资源，是森林经营管理者迫切需要回答和解决的问题。本文应用空间直观景观模型(LANDIS)设计不同的管理方案，模拟火后森林景观在不同自然干扰和人类干扰下的长期演替动态，以弥补这一不足。通过对比研究这些管理方案对森林景观的长期影响，不仅可以评价目前该区所采取的经营管理方案是否合理，还可以找到更合适的管理方案，为火后森林景观的定向恢复和加速恢复过程提供实际的指导意义。此外，通过对1987年火后十几年来森林植被的野外调查研究，可以弥补LANDIS只模拟树种不模拟草本和灌木，且以10年为步长的不足，加深人类活动对火后森林植被短期变化影响的理解。本文研究主要结论如下： 1）相对于轻度灭火方案而言，目前重度灭火方案可以增加针叶林（落叶松和樟子松）在该区所占的比例，相应地降低白桦等阔叶林在该区所占的比例。而且，目前重度灭火强度可以增加针叶树种成过熟林在该区所占的比例，减少幼龄林所占的比例，可见目前重度灭火方案有利于火后森林恢复。另外，目前重度灭火方案下的累计过火面积较少，火烧频率降低，但火干扰以重度火烧为主，这可能对于森林资源造成重大的损失。 2）种植强度越高，越有利于针叶林的更新。但考虑到经济以及劳动力等因素，中等种植强度（例如，种30%）已能很好地有利于火后森林恢复。相对于聚集种植而言，在整个重度火烧区进行随机种植可以增加针叶树种在该区所占的比例，降低白桦等阔叶树种所占的比例，因此随机种植更有利于火后森林恢复。进行人工造林时尽量不要种植单一物种。相对于其他种植比例而言，目前落叶松和樟子松的种植比例（7：3）有利于针叶树种的恢复。 3）相对于完全依靠天然更新而言，目前经营管理措施可以增加针叶树种在该区所占的比例，相应地降低阔叶树种的比重。在目前经营措施下，由于火后10年内进行大规模的森林采伐，使针叶树种的成过熟林及聚集程度等有较大幅度的下降，虽然随后逐渐增加，但也需要近100-150多年的时间才能恢复。总之，目前经营管理方案与完全依靠天然更新相比，有利于火后森林恢复，但在演替的前期（100-150多年）也存在一些问题。因此，大兴安岭林区发生特大森林火灾后，应禁止大规模的森林采伐，否则会发生森林年龄结构的锐变等一些问题，不利于森林景观的稳定。如果进行大规模的森林采伐，即使进行人工造林，也要很长时间才能得以恢复。 4）在火后完全依靠天然更新，无论是草本、灌木还是乔木树种在演替前期都发生了极大的变化。火后初始，阳性草本植物迅速增加，占据火烧迹地。但目前（火烧16年后）草本物种数开始下降，而且阳性草本逐渐减少，阴性草本逐渐增加。草本盖度的变化与物种变化呈明显的正相关，随着物种数的下降，草本盖度也逐渐下降。而灌木盖度与物种数则没有明显的相关性。目前喜阳灌木物种逐渐消失，而耐荫的逐渐增多，但总的灌木物种数只有少量减少。目前该区树种以白桦等阔叶林为主。在人工促进天然更新下，目前火烧迹地上的物种数仅稍高于天然更新。而由于人促更新使物种尤其是乔木更有利于更新，火烧迹地上的植被盖度却要高于天然更新。相对于天然更新而言，在人促更新下，目前在火烧迹地形成的针阔混交林中，针叶树种所占的比例明显提高。在人工更新下，目前火烧迹地上主要以针叶树种为主，而阔叶树种则较少。

友好林业局森林采伐对森林景观格局演变的长期影响

人为活动是森林景观变化的主要驱动力之一，采伐又是人类对森林最主要的干扰活动，采伐影响下森林景观的长期变化动态，是森林经理学的重要研究内容。本文以小兴安岭友好林业局为研究区，充分利用林相图，地形图以及采伐等资料，采用GIS作为数据处理的手段，用空间直观景观模型（LANDIS）模拟了500年的时间尺度上森林景观在各种采伐活动影响下的演替动态。本研究选取五个代表树种：红松、云杉、椴树、落叶松、白桦，主要从分布面积、年龄结构和聚集度三个方面进行了分析，揭示了森林景观变化与采伐之间的定量化关系，为森林管理措施的制定提供理论依据。主要结论如下 ①友好林业局过去的森林采伐造成了严重的生态后果：原始林已经所剩无几，而是变成了以过伐林和次生林为主；友好林业局红松的面积仅占6.3%，云、冷杉分别占9%左右，占优势地位的是落叶松和白桦，分别为27%和29%。红松、云冷杉以及落叶松80%以上的都是中幼龄林。②自然演替状态（无采伐）下，红松、云杉和椴树的分布面积逐年升高，500年内红松增加了24%，聚集程度不断升高；而白桦等阳性先锋树种分布面积则大大降低，500年内白桦减少了近15%，聚集程度逐渐降低。③传统经营下的采伐研究表明，采伐是友好林业局森林景观变化的主要驱动力。本研究中由于树种的生活特性和模型本身的特点，红松、云杉分布面积在三种模拟方案下的差异不明显，落叶松和白桦在各种方案下的差异比较明显。采伐明显改变了各树种年龄结构,采伐后树种林龄级下降。采伐还会造成树种聚集度明显差异，而且树种分布格局和采伐分布格局密切相关，红松、云杉、椴树聚集度指数与采伐分布指数显著正相关，落叶松、白桦聚集度指数与采伐分布指数显著负相关。④本研究中由于树种的生活特性和模型本身的特点，与传统经营相比，分类经营下生态公益林内落叶松和白桦的面积变化比较明显，红松、云杉、椴树的面积变化不明显；然而各树种的年龄结构均发生了显著变化，分类经营下成、过熟林的面积明显增加。⑤不同径级择伐一定程度上引起了采伐面积、采伐强度以及采伐空间分布格局的差异。不同树种由于其生活特性的差异对不同径级择伐反应各异，不同径级择伐下树种分布面积和年龄结构差异较明显，但是各树种聚集度指数在不同径级择伐下的差异较小。

景观模型初始化及空间分辨率对模拟结果的影响

近20年来空间直观森林景观模型取得了长足的发展并得到广泛应用，成为研究人员和森林经营者研究和探讨森林景观变化的有效工具之一。然而，在应用模型的时候，初始化方法和运行尺度问题是影响模型应用和模拟结果的重要因素。本文应用空间直观森林景观模型LANDIS系统地研究了不同的初始化方法和空间分辨率对森林演替、火干扰和森林管理模拟的影响。主要结论如下： 1）不同的赋值方法对初始的物种分布面积和空间格局都有显著的影响，但是随着模拟时间的推移演替的进行，两种赋值方法下的物种分布面积和空间格局以及火干扰的差异在减小，在模拟的中后期趋近一致。所以，从模拟时间上来说，如果模拟的时间相对较长（＞300年），两种赋值方法都可以作为模型的初始化方法；但是，如果模拟的时间相对较短（ ≤300年），赋值方法 B 将是一种很好的选择，因为它避免了赋值方法 A 在模拟初期物种分布面积、格局和火干扰的波动过程。 2） 模型在空间分辨率为30-270m的范围内，对分辨率的变化不敏感，随着空间分辨率的降低，分辨率的变化对模拟的火干扰、物种分布面积和空间格局有显著影响。空间分辨率的下降导致景观的同质性增强，尤其是当分辨率小于270m时，导致林火频次和过火面积过大。模型在较高空间分辨率上模拟森林演替的结果与现实情况和相关模拟研究结果相近。因此，运行LANDIS模型空间分辨率应该高于270m。 3） 模型在30-270m的空间分辨率范围内模拟采伐造林措施的时候，物种分布面积的模拟对分辨率的变化不敏感，但是，分辨率的变化对物种空间格局有较大影响，随着空间分辨率的下降，物种在高分辨率上的聚集水平大于低分辨率上的聚集水平。