Relevance of multiple spatial scales in habitat models: A case study with amphibians and grasshoppers

Habitat models for animal species are important tools in conservation planning. We assessed the need to consider several scales in a case study for three amphibian and two grasshopper species in the post-mining landscapes near Leipzig (Germany). The two species groups were selected because habitat analyses for grasshoppers are usually conducted on one scale only whereas amphibians are thought to depend on more than one spatial scale.First, we analysed how the preference to single habitat variables changed across nested scales. Most environmental variables were only significant for a habitat model on one or two scales, with the smallest scale being particularly important. On larger scales, other variables became significant, which cannot be recognized on lower scales. Similar preferences across scales occurred in only 13 out of 79 cases and in 3 out of 79 cases the preference and avoidance for the same variable were even reversed among scales.Second, we developed habitat models by using a logistic regression on every scale and for all combinations of scales and analysed how the quality of habitat models changed with the scales considered. To achieve a sufficient accuracy of the habitat models with a minimum number of variables, at least two scales were required for all species except for Bufo viridis, for which a single scale, the microscale, was sufficient. Only for the European tree frog (Hyla arborea), at least three scales were required.The results indicate that the quality of habitat models increases with the number of surveyed variables and with the number of scales, but costs increase too. Searching for simplifications in multi-scaled habitat models, we suggest that 2 or 3 scales should be a suitable trade-off, when attempting to define a suitable microscale.     

Seed fate in an ant-dispersed sedge,Carex pilulifera L.: recruitment and seedling survival in tests of models for spatial dispersion

Summary The spatial pattern of recruitment and seedling survival was studied in an ant-dispersed sedge (Carex pilulifera L.) in a forest clearing in Denmark. Seedlings were generally more aggregated than juvenile and adult plants. Recruitment distances were skewed towards larger values. While 72% plants of presumably ant-dispersed origin survived for 5 years, only 13% auto-dispersed plants were still alive, predominantly as persistent seedlings. The survivorship showed a negative exponential decrease (Deevey type II) with an expected half-life of 17 months for auto-, and 10 years for ant-dispersed plants. Growth-rate and fecundity were significantly larger for ant-dispersed plants than for auto-dispersed plants. Computer simulation was used to test three different dispersal models and two mortality types on the recruitment pattern observed in the field. The simulations confirmed the importance of ant dispersal for recruitment, but gave no conclusive evidence for evaluating recruitment from ant-nests. High mortality levels, comparable to observed predation levels, were needed to simulate field conditions. Density-dependent mortality proved more powerful than distance-dependent mortality in the simulations. Results are discussed in the context of current hypotheses of seed dispersal. While the directed dispersal hypothesis could not be rejected, results were generally more favourable to the escape hypothesis.     

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

Background

Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis.

Results

Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models.

Conclusions

Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-646) contains supplementary material, which is available to authorized users.