Rapid Recovery Of An Urban Remnant Reptile Community following Summer Wildfire

Reptiles in urban remnants are threatened with extinction by increased fire frequency, habitat fragmentation caused by urban development, and competition and predation from exotic species. Understanding how urban reptiles respond to and recover from such disturbances is key to their conservation. We monitored the recovery of an urban reptile community for five years following a summer wildfire at Kings Park in Perth, Western Australia, using pitfall trapping at five burnt and five unburnt sites. The reptile community recovered rapidly following the fire. Unburnt sites initially had higher species richness and total abundance, but burnt sites rapidly converged, recording a similar total abundance to unburnt areas within two years, and a similar richness within three years. The leaf-litter inhabiting skink Hemiergis quadrilineata was strongly associated with longer unburnt sites and may be responding to the loss of leaf litter following the fire. Six rarely-captured species were also strongly associated with unburnt areas and were rarely or never recorded at burnt sites, whereas two other rarely-captured species were associated with burnt sites. We also found that one lizard species, Ctenotus fallens, had a smaller average body length in burnt sites compared to unburnt sites for four out of the five years of monitoring. Our study indicates that fire management that homogenises large areas of habitat through frequent burning may threaten some species due to their preference for longer unburnt habitat. Careful management of fire may be needed to maximise habitat suitability within the urban landscape

Rapid recovery of an urban remnant reptile community following summer wildfire

Reptiles in urban remnants are threatened with extinction by increased fire frequency, habitat fragmentation caused by urban development, and competition and predation from exotic species. Understanding how urban reptiles respond to and recover from such disturbances is key to their conservation. We monitored the recovery of an urban reptile community for five years following a summer wildfire at Kings Park in Perth, Western Australia, using pitfall trapping at five burnt and five unburnt sites. The reptile community recovered rapidly following the fire. Unburnt sites initially had higher species richness and total abundance, but burnt sites rapidly converged, recording a similar total abundance to unburnt areas within two years, and a similar richness within three years. The leaf-litter inhabiting skink Hemiergis quadrilineata was strongly associated with longer unburnt sites and may be responding to the loss of leaf litter following the fire. Six rarely-captured species were also strongly associated with unburnt areas and were rarely or never recorded at burnt sites, whereas two other rarely-captured species were associated with burnt sites. We also found that one lizard species, Ctenotus fallens, had a smaller average body length in burnt sites compared to unburnt sites for four out of the five years of monitoring. Our study indicates that fire management that homogenises large areas of habitat through frequent burning may threaten some species due to their preference for longer unburnt habitat. Careful management of fire may be needed to maximise habitat suitability within the urban landscape

A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons

The problem of perfect predictors in statistical spike train models

https://doi.org/10.51628/001c.27667Published versio

STATISTICAL METHODS FOR EXPLORING NEURONAL INTERACTIONS

Generalized linear models (GLMs) offer a platform for analyzing multi-electroderecordings of neuronal spiking. We suggest an L1-regularized logistic regressionmodel to detect short-term interactions under certain experimental setups. Weestimate parameters of this model using a coordinate descent algorithm; we determinethe optimal tuning parameter using BIC, and prove its asymptotic validity. Simulationstudies of the method's performance show that this model can detect excitatoryinteractions with high sensitivity and specificity with reasonably large recordings,even when the magnitude of the interactions is small; similar results hold forinhibition for sufficiently high baseline firing rates. The method is somewhat robustto network complexity and partial observation of networks. We apply our method tomulti-electrode recording data from monkey dorsal premotor cortex (PMd). Our resultspoint to certain features of short-term interactions when a monkey plans a reach.Next, we propose a variable coefficients GLM model to assess the temporal variationof interactions across trials. We treat the parameters of interest as functions overtrials, and fit them by penalized splines. There are also nuisance parameters assumedconstant, which are mildly penalized to guarantee the finite maximum of thelog-likelihood. We choose tuning parameters for smoothness by generalized crossvalidation, and provide simultaneous confidence bands and hypothesis tests fornull models. To achieve efficient computation, some modifications are also made. Weapply our method to a subset of the monkey PMd data. Before the implementation to thereal data, simulations are done to assess the performance of the proposed model.Finally, for the logistic and Poisson models, one possible difficulty is that iterativealgorithms for estimation may not converge because of certain data configurations(called complete and quasicomplete separation for the logistic). We show that thesefeatures are likely to occur because of refractory periods of neurons, and show howstandard software deals with this difficulty. For the Poisson model, we show that suchdifficulties arise possibly due to bursting or specifics of the binning. Wecharacterize the nonconvergent configurations for both models, show that they can bedetected by linear programming methods, and propose remedies

Distributed Activity Patterns for Objects and Their Features: Decoding Perceptual and Conceptual Object Processing in Information Networks of the Human Brain

How are object features and knowledge-fragments represented and bound together in the human brain? Distributed patterns of activity within brain regions can encode distinctions between perceptual and cognitive phenomena with impressive specificity. The research reported here investigated how the information within regions\u27 multi-voxel patterns is combined in object-concept networks. Chapter 2 investigated how memory-driven activity patterns for an object\u27s specific shape, color, and identity become active at different stages of the visual hierarchy. Brain activity patterns were recorded with functional magnetic resonance imaging (fMRI) as participants searched for specific fruits or vegetables within visual noise. During time-points in which participants were searching for an object, but viewing pure noise, the targeted object\u27s identity could be decoded in the left anterior temporal lobe (ATL). In contrast, top-down generated patterns for the object\u27s specific shape and color were decoded in early visual regions. The emergence of object-identity information in the left ATL was predicted by concurrent shape and color information in their respective featural regions. These findings are consistent with theories proposing that feature-fragments in sensory cortices converge to higher-level identity representations in convergence zones. Chapter 3 investigated whether brain regions share fluctuations in multi-voxel information across time. A new analysis method was first developed, to measure dynamic changes in distributed pattern information. This method, termed informational connectivity (IC), was then applied to data collected as participants viewed different types of man-made objects. IC identified connectivity between object-processing regions that was not apparent from existing functional connectivity measures, which track fluctuating univariate signals. Collectively, this work suggests that networks of regions support perceptual and conceptual object processing through the convergence and synchrony of distributed pattern information