A morphometric study of the spatial patterns of TDP-43 immunoreactive neuronal inclusions in frontotemporal lobar degeneration (FTLD) with progranulin (GRN) mutation

Mutations of the progranulin (GRN) gene are a major cause of familial frontotemporal lobar degeneration with transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) proteinopathy (FTLD-TDP). We studied the spatial patterns of TDP-43 immunoreactive neuronal cytoplasmic inclusions (NCI) and neuronal intranuclear inclusions (NII) in histological sections of the frontal and temporal lobe in eight cases of FTLD-TDP with GRN mutation using morphometric methods and spatial pattern analysis. In neocortical regions, the NCI were clustered and the clusters were regularly distributed parallel to the pia mater; 58% of regions analysed exhibiting this pattern. The NII were present in regularly distributed clusters in 35% of regions but also randomly distributed in many areas. In neocortical regions, the sizes of the regular clusters of NCI and NII were 400-800 µm, approximating to the size of the modular columns of the cortico-cortical projections, in 31% and 36% of regions respectively. The NCI and NII also exhibited regularly spaced clustering in sectors CA1/2 of the hippocampus and in the dentate gyrus. The clusters of NCI and NII were not spatially correlated. The data suggest degeneration of the cortico-cortical and cortico-hippocampal pathways in FTLD-TDP with GRN mutation, the NCI and NII affecting different clusters of neurons.

Pooling and suppression in human spatial vision

A distinct feature of several recent models of contrast masking is that detecting mechanisms are divisively inhibited by a broadly tuned ‘gain pool’ of narrow-band spatial pattern mechanisms. The contrast gain control provided by this ‘cross-channel’ architecture achieves contrast normalisation of early pattern mechanisms, which is important for keeping them within the non-saturating part of their biological operating characteristic. These models superseded earlier ‘within-channel’ models, which had supposed that masking arose from direct stimulation of the detecting mechanism by the mask. To reveal the extent of masking, I measured the levels produced with large ranges of pattern spatial relationships that have not been explored before. Substantial interactions between channels tuned to different orientations and spatial frequencies were found. Differences in the masking levels produced with single and multiple component mask patterns provided insights into the summation rules within the gain pool. A widely used cross-channel masking model was tested on these data and was found to perform poorly. The model was developed and a version in which linear summation was allowed between all components within the gain pool but with the exception of the self-suppressing route typically provided the best account of the data. Subsequently, an adaptation paradigm was used to probe the processes underlying pooled responses in masking. This delivered less insight into the pooling than the other studies and areas were identified that require investigation for a new unifying model of masking and adaptation. In further experiments, levels of cross-channel masking were found to be greatly influenced by the spatio-temporal tuning of the channels involved. Old masking experiments and ideas relying on within-channel models were re-elevated in terms of contemporary cross-channel models (e.g. estimations of channel bandwidths from orientation masking functions) and this led to different conclusions than those originally arrived at. The investigation of effects with spatio-temporally superimposed patterns is focussed upon throughout this work, though it is shown how these enquiries might be extended to investigate effects across spatial and temporal position.

Spatial patterns of TDP-43 neuronal cytoplasmic inclusions (NCI) in fifteen cases of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP)

Neuronal cytoplasmic inclusions (NCI) immunoreactive for transactive response DNA-binding protein (TDP-43) are the pathological hallmark of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP). We studied the spatial patterns of the TDP-43 immunoreactive NCI in the frontal and temporal cortex of 15 cases of FTLD-TDP. The NCI were distributed parallel to the tissue boundary predominantly in regular clusters 50-400 µm in diameter. In five cortical areas, the size of the clusters approximated to the cells of the cortico-cortical pathways. In most regions, cluster size was smaller than 400 µm. There were no significant differences in spatial patterns between familial and sporadic cases. Cluster size of the NCI was not correlated with disease duration, brain weight, Braak stage, or disease subtype. The spatial pattern of the NCI was similar to that of neuronal inclusions in other neurodegenerative diseases and may reflect a common pattern of degeneration involving the cortico-cortical projections.

Different molecular pathologies result in similar spatial patterns of cellular inclusions in neurodegenerative disease:a comparative study of eight disorders

Recent research suggests cell-to-cell transfer of pathogenic proteins such as tau and α-synuclein may play a role in neurodegeneration. Pathogenic spread along neural pathways may give rise to specific spatial patterns of the neuronal cytoplasmic inclusions (NCI) characteristic of these disorders. Hence, the spatial patterns of NCI were compared in four tauopathies, viz., Alzheimer's disease, Pick's disease, corticobasal degeneration, and progressive supranuclear palsy, two synucleinopathies, viz., dementia with Lewy bodies and multiple system atrophy, the 'fused in sarcoma' (FUS)-immunoreactive inclusions in neuronal intermediate filament inclusion disease, and the transactive response DNA-binding protein (TDP-43)-immunoreactive inclusions in frontotemporal lobar degeneration, a TDP-43 proteinopathy (FTLD-TDP). Regardless of molecular group or morphology, NCI were most frequently aggregated into clusters, the clusters being regularly distributed parallel to the pia mater. In a significant proportion of regions, the regularly distributed clusters were in the size range 400-800 μm, approximating to the dimension of cell columns associated with the cortico-cortical pathways. The data suggest that cortical NCI in different disorders exhibit a similar spatial pattern in the cortex consistent with pathogenic spread along anatomical pathways. Hence, treatments designed to protect the cortex from neurodegeneration may be applicable across several different disorders. © 2012 Springer-Verlag.

Spatial patterns of the tau pathology in progressive supranuclear palsy

Progressive supranuclear palsy (PSP) is characterized neuropathologically by neuronal loss, gliosis, and the presence of tau-immunoreactive neuronal and glial cell inclusions affecting subcortical and some cortical regions. The objectives of this study were to determine (1) the spatial patterns of the tau-immunoreactive pathology, viz., neurofibrillary tangles (NFT), oligodendroglial inclusions (GI), tufted astrocytes (TA), and Alzheimer's disease-type neuritic plaques (NP) in PSP and (2) to investigate the spatial correlations between the histological features. Post-mortem material of cortical and subcortical regions of eight PSP cases was studied. Spatial pattern analysis was applied to the NFT, GI, TA, NP, abnormally enlarged neurons (EN), surviving neurons, and glial cells. NFT, GI, and TA were distributed either at random or in regularly distributed clusters. The EN and NP were mainly randomly distributed. Clustering of NFT and EN was more frequent in the cortex and subcortical regions, respectively. Variations in NFT density were not spatially correlated with the densities of either GI or TA, but were positively correlated with the densities of EN and surviving neurons in some regions. (1) NFT were the most widespread tau-immunoreactive pathology in PSP being distributed randomly in subcortical regions and in regular clusters in cortical regions, (2) GI and TA were more localized and exhibited a regular pattern of clustering in subcortical regions, and (3) neuronal and glial cell pathologies were not spatially correlated. © 2012 Springer-Verlag.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.

Spatial patterns of neuronal cytoplasmic inclusions in the tauopathies: A comparative study of five disorders

The tauopathies are a major molecular group of neurodegenerative disorders characterised by the deposition of abnormal cellular aggregates of the microtubule associated protein (MAP) tau in the form of neuronal cytoplasmic inclusions (NCI). Recent research suggests that cell to cell propagation of pathogenic tau may be involved in the neurodegeneration of these disorders. If pathogenic tau spreads along anatomical pathways it may give rise to specific spatial patterns of the NCI in brain tissue. To test this hypothesis, the spatial patterns of NCI in cerebral cortical regions were compared in tissue sections taken from five major tauopathies: (1) argyrophilic grain disease (AGD), (2) Alzheimer's disease (AD), (3) corticobasal degeneration (CBD), (4) Pick's disease (PiD), and (5) progressive supranuclear palsy (PSP). In the cerebral cortex of these disorders, NCI were frequently aggregated into clusters and the clusters were regularly distributed parallel to the pia mater. In a significant proportion of regions, the mean size of the regularly distributed clusters of NCI was in the range 400 – 800 m, measured parallel to the pia mater, approximating to the dimension of cell columns associated with the cortico-cortical anatomical pathways. Hence, the data suggest that cortical NCI in the tauopathies exhibit a spatial pattern in the cortex which could result from the spread of pathogenic tau along anatomical pathways. Treatments designed to protect the cortex from tau propagation may therefore be applicable across several different disorders within this molecular group.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Spatial patterns of manufacturing clusters in Vietnam

The formation of industrial clusters is critical for sustained economic growth. We identify the manufacturing clusters in Vietnam, using the Mori and Smith (2013) method, which indicates the spatial pattern of industrial agglomerations using the global extent (GE) and local density (LD) indices. Spatial pattern identification is extremely helpful because industrial clusters are often spread over a wide geographical area and the GE and LD indices—along with cluster mapping—display how the respective clusters fit into specific spatial patterns.

Spatial and temporal interaction between sediment and microphytobenthos in a temperate estuarine macro-intertidal bay

Intertidal flats of the estuarine macro-intertidal Baie des Veys (France) were investigated to identify spatial features of sediment and microphytobenthos (MPB) in April 2003. Gradients occurred within the domain, and patches were identified close to vegetated areas or within the oyster-farming areas where calm physical conditions and biodeposition altered the sediment and MPB landscapes. Spatial patterns of chl a content were explained primarily by the influence of sediment features, while bed elevation and compaction brought only minor insights into MPB distribution regulation. The smaller size of MPB patches compared to silt patches revealed the interplay between physical structure defining the sediment landscape, the biotic patches that they contain, and that median grain-size is the most important parameter in explaining the spatial pattern of MPB. Small-scale temporal dynamics of sediment chl a content and grain-size distribution were surveyed in parallel during 2 periods of 14 d to detect tidal and seasonal variations. Our results showed a weak relationship between mud fraction and MPB biomass in March, and this relationship fully disappeared in July. Tidal exposure was the most important parameter in explaining the summer temporal dynamics of MPB. This study reveals the general importance of bed elevation and tidal exposure in muddy habitats and that silt content was a prime governing physical factor in winter. Biostabilisation processes seemed to behave only as secondary factors that could only amplify the initial silt accumulation in summer rather than primary factors explaining spatial or long-term trends of sediment changes.

Longitudinal distribution and lateral pattern of megalopal settlement and juvenile recruitment of Carcinus maenas (L.) (Brachyura, Portunidae) in the Mira River Estuary, Portugal

Settlement is a critical process in the life history of crabs, and thus affecting the abundance, distribution and structure of estuarine communities. The spatial pattern of settlement of megalopae of the shore crab Carcinus maenas along a longitudinal estuarine gradient (Mira River Estuary, Portugal) was examined, as well as its effects on the juvenile population. To measure megalopal settlement, four replicate collectors were deployed in six equally spaced stations along the estuarine axis. Juveniles were collected on the same locations with a quadrat randomly deployed on the substrate. To assess fine-scale megalopal settlement within a curved region of the estuary, replicate collectors were deployed on both margins along Moinho da Asneira curve. Megalopae settled differently along the six longitudinal points, with a tendency to attenuate their settlement upstream. Within the curved region, megalopae preferentially settled on the left margin collectors, probably due to the weaker velocity speeds felt on this margin. Concerning the overall juvenile density, there were significant differences among the stations distributed along the estuary, but they did no reflect a longitudinal dispersion attenuation pattern. Size-frequency distribution of the juvenile population showed that the average size is higher on the left margin. Recruits (carapace length between 1.0 mm and 3.4 mm) were more abundant on the upstream stations. Density of early juveniles (3.4 mm-6.5 mm) and juveniles (6.5 mm-10 mm) was more stable throughout the estuary axis than that of recruits. This distribution pattern may result from tidal excursion processes or mechanisms to avoid biotic interactions, such as predation and competition. (c) 2006 Elsevier Ltd. All rights reserved.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

Implications of patterns of carbon pools and fluxes across a semiarid environmental gradient

Landscape scale environmental gradients present variable spatial patterns and ecological processes caused by climate, topography and soil characteristics and, as such, offer candidate sites to study environmental change. Data are presented on the spatial pattern of dominant species, biomass, and carbon pools and the temporal pattern of fluxes across a transitional zone shifting from Great Basin Desert scrub, up through pinyon-juniper woodlands and into ponderosa pine forest and the ecotones between each vegetation type. The mean annual temperature (MAT) difference across the gradient is approximately 3 degrees C from bottom to top (MAT 8.5-5.5) and annual precipitation averages from 320 to 530 mm/yr, respectively. The stems of the dominant woody vegetation approach a random spatial pattern across the entire gradient, while the canopy cover shows a clustered pattern. The size of the clusters increases with elevation according to available soil moisture which in turn affects available nutrient resources. The total density of woody species declines with increasing soil moisture along the gl-adient, but total biomass increases. Belowground carbon and nutrient pools change from a heterogenous to a homogenous distribution on either side of the woodlands. Although temperature controls the: seasonal patterns of carbon efflux from the soils, soil moisture appears to be the primary driving variable, but response differs underneath the different dominant species, Similarly, decomposition of dominant litter occurs faster-at the cooler and more moist sites, but differs within sites due to litter quality of the different species. The spatial pattern of these communities provides information on the direction of future changes, The ecological processes that we documented are not statistically different in the ecotones as compared to the: adjoining communities, but are different at sites above the woodland than those below the woodland. We speculate that an increase in MAT will have a major impact on C pools and C sequestering and release processes in these semiarid landscapes. However, the impact will be primarily related to moisture availability rather than direct effects of an increase in temperature. (C) 1998 Elsevier Science B.V.

A neuronal topography of visual and acoustic fear conditioning within the amygdala

The lateral amygdala (LA) receives information from auditory and visual sensory modalities, and uses this information to encode lasting memories that predict threat. One unresolved question about the amygdala is how multiple memories, derived from different sensory modalities, are organized at the level of neuronal ensembles. We previously showed that fear conditioning using an auditory conditioned stimulus (CS) was spatially allocated to a stable topography of neurons within the dorsolateral amygdala (LAd) (Bergstrom et al, 2011). Here, we asked how fear conditioning using a visual CS is topographically organized within the amygdala. To induce a lasting fear memory trace we paired either an auditory (2 khz, 55 dB, 20 s) or visual (1 Hz, 0.5 s on/0.5 s off, 35 lux, 20 s) CS with a mild foot shock unconditioned stimulus (0.6 mA, 0.5 s). To detect learning-induced plasticity in amygdala neurons, we used immunohistochemistry with an antibody for phosphorylated mitogen-activated protein kinase (pMAPK). Using a principal components analysis-based approach to extract and visualize spatial patterns, we uncovered two unique spatial patterns of activated neurons in the LA that were associated with auditory and visual fear conditioning. The first spatial pattern was specific to auditory cued fear conditioning and consisted of activated neurons topographically organized throughout the LAd and ventrolateral nuclei (LAvl) of the LA. The second spatial pattern overlapped for auditory and visual fear conditioning and was comprised of activated neurons located mainly within the LAvl. Overall, the density of pMAPK labeled cells throughout the LA was greatest in the auditory CS group, even though freezing in response to the visual and auditory CS was equivalent. There were no differences detected in the number of pMAPK activated neurons within the basal amygdala nuclei. Together, these results provide the first basic knowledge about the organizational structure of two different fear engrams within the amygdala and suggest they are dissociable at the level of neuronal ensembles within the LA

A model-framed evaluation of elephant effects on tree and fire dynamics in African savannas

There is a concern that high densities of elephants in southern Africa could lead to the overall reduction of other forms of biodiversity. We present a grid-based model of elephant-savanna dynamics, which differs from previous elephant-vegetation models by accounting for woody plant demographics, tree-grass interactions, stochastic environmental variables (fire and rainfall), and spatial contagion of fire and tree recruitment. The model projects changes in height structure and spatial pattern of trees over periods of centuries. The vegetation component of the model produces long-term tree-grass coexistence, and the emergent fire frequencies match those reported for southern African savannas. Including elephants in the savanna model had the expected effect of reducing woody plant cover, mainly via increased adult tree mortality, although at an elephant density of 1.0 elephant/km2, woody plants still persisted for over a century. We tested three different scenarios in addition to our default assumptions. (1) Reducing mortality of adult trees after elephant use, mimicking a more browsing-tolerant tree species, mitigated the detrimental effect of elephants on the woody population. (2) Coupling germination success (increased seedling recruitment) to elephant browsing further increased tree persistence, and (3) a faster growing woody component allowed some woody plant persistence for at least a century at a density of 3 elephants/km2. Quantitative models of the kind presented here provide a valuable tool for exploring the consequences of management decisions involving the manipulation of elephant population densities. © 2005 by the Ecological Society of America.