Single Spatial Model Research Articles

Extending density surface models to include multiple and double-observer survey data.

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

Getting the Most Out of Sorghum Low‐Input Field Trials inWestAfrica Using Spatial Adjustment

AbstractBreeding sorghum for low‐input conditions is hindered by soil heterogeneity. Spatial adjustment using mixed models can help account for this variation and increase precision of low‐input field trials. Large small‐scale spatial variation (CV39.4 %) for plant available phosphorus was mapped in an intensely sampled low‐input field. Spatial adjustments were shown to account for residual yield differences because of this and other growth factors. To investigate the potential of such models to increase the efficiency of low‐ and high‐input field trials, 17 experiments with 70 sorghum genotypes conducted inMali,WestAfrica, were analysed for grain yield using different mixed models including models with autoregressive spatial correlation terms. Spatial models (AR1,AR2) improved broad sense heritability estimates for grain yield, averaging gains of 10 and 6 % points relative to randomized complete block (RCB) and lattice models, respectively. The heritability estimate gains were even higher under low phosphorus conditions and in two‐replicate analyses. No specific model was best for all environments. A single spatial model,AR1 × AR1, captured most of the gains for heritability and relative efficiency provided by the best model identified for each environment usingAkaike'sInformationCriterion. Spatial modelling resulted in important changes in genotype ranking for grain yield. Thus, the use of spatial models was shown to have potentially important consequences for aiding effective sorghum selection inWestAfrica, particularly under low‐input conditions and for trials with fewer replications. Thus, using spatial models can improve the resource allocation of a breeding program. Furthermore, our results show that good experimental design with optimal placement and orientation of blocks is essential for efficient statistical analysis with or without spatial adjustment.

Predator‐specific landscapes of fear and resource distribution: effects on spatial range use

Although ecologists have long recognized that animal space use is primarily determined by the presence of predators and the distribution of resources, the effects of these two environmental conditions have never been quantified simultaneously in a single spatial model. Here, in a novel approach, predator-specific landscapes of fear are constructed on the basis of behavioral responses of a prey species (vervet monkey; Cercopithecus aethiops), and we show how these can be combined with data on resource distribution to account for the observed variation in intensity of space use. Results from a mixed regressive-spatial regressive analysis demonstrate that ranging behavior can indeed be largely interpreted as an adaptive response to perceived risk of predation by some (but not all) predators and the spatial availability of resources. The theoretical framework behind the model is furthermore such that it can easily be extended to incorporate the effects of additional factors potentially shaping animal range use and thus may be of great value to the study of animal spatial ecology.