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1.

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.  相似文献   

2.

Background  

Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time.  相似文献   

3.

Background  

Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed.  相似文献   

4.

Background  

Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N 3) in memory. This is only practical for small RNAs.  相似文献   

5.
6.

Background  

Placentas of guinea pig-related rodents are appropriate animal models for human placentation because of their striking similarities to those of humans. To optimize the pool of potential models in this context, it is essential to identify the occurrence of characters in close relatives.  相似文献   

7.

Background  

Possible methods for distinguishing receptor binding models and analysing their parameters are considered.  相似文献   

8.

Background  

Empirical binding models have previously been investigated for the energetics of protein complexation (ΔG models) and for the influence of mutations on complexation (i.e. differences between wild-type and mutant complexes, ΔΔG models). We construct binding models to directly compare these processes, which have generally been studied separately.  相似文献   

9.

Background  

Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.  相似文献   

10.

Background  

Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins.  相似文献   

11.

Background  

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.  相似文献   

12.

Background  

Existing virulence models are often difficult to apply for quantitative comparison of invasion potentials of Listeria monocytogenes. Well-to-well variation between cell-line based in vitro assays is practically unavoidable, and variation between individual animals is the cause of large deviations in the observed capacity for infection when animal models are used.  相似文献   

13.
Hidden Markov model speed heuristic and iterative HMM search procedure   总被引:1,自引:0,他引:1  

Background  

Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.  相似文献   

14.

Background  

General protein evolution models help determine the baseline expectations for the evolution of sequences, and they have been extensively useful in sequence analysis and for the computer simulation of artificial sequence data sets.  相似文献   

15.

Background  

Constraint-based models enable structured cellular representations in which intracellular kinetics are circumvented. These models, combined with experimental data, are useful analytical tools to estimate the state exhibited (the phenotype) by the cells at given pseudo-steady conditions.  相似文献   

16.

Background  

Different models for biofilm in Streptococcus pneumoniae have been described in literature. To permit comparison of experimental data, we characterised the impact of the pneumococcal quorum-sensing competence system on biofilm formation in three models. For this scope, we used two microtiter and one continuous culture biofilm system.  相似文献   

17.
18.

Background  

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.  相似文献   

19.

Background  

Chondrosarcoma responds poorly to adjuvant therapy and new, clinically relevant animal models are required to test targeted therapy.  相似文献   

20.

Background  

When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. While there are several techniques for the fitting of sparse high-dimensional survival models by penalized parameter estimation, none allows for explicit consideration of such mandatory covariates.  相似文献   

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