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127 results for “predictive ecology”
Predicting daily activity time through ecological niche modeling and microclimatic data
<p><span>1. </span><span>Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. </span></p> <p><span>2. </span><span>The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (<em>Heloderma suspectum</em>) as a study system. Further, we identified if the activity patterns are related to physiological constraints.</span></p> <p><span>3. </span><span>We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints.</span></p> <p><span>4. </span><span>The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. </span></p> <p><span>5. </span><span>We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.</span></p>
The Prairie State: Using Ecological Niche Modeling to Predict Distributions of Early Land Plants
<p>This data includes raw data of over 12,000 occurrences were downloaded from the<strong> Consortium of Bryophyte Herbaria (<a href="http://www.bryophyteportal.org/portal">www.bryophyteportal.org/portal</a>), </strong>that were listed to be in Illinois and included longitude and latitude data. This data set was screened and cleaned to investigate species distribution models as well as generate models of selected bryophytes investigating future changes in distribution across climate change scenarios.</p>
Comparative ecological analysis and predictive modeling of tick-borne pathogens
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Evolutionary effects of nitrogen are not easily predicted from ecological responses
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Data from: Can the genomics of ecological speciation be predicted across the divergence continuum from host races to species? A case study in Rhagoletis
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Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa
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Predicting daily activity time through ecological niche modeling and microclimatic data
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Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
<p>1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate.</p> <p>2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process.</p> <p>3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores.</p> <p>4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.</p>
Ecological patterns of root nodule diversity in cultivated and wild rooibos populations: a community prediction approach
<p>There is interest in understanding the factors behind the biogeography of root-associated bacteria due to the joint effects that plant host, climate, and soil conditions can have on bacterial diversity. For legume crops with remaining wild populations, this is of even more importance, because the effects of cropping on undisturbed root-associated bacterial communities can be addressed. Here, we used a community prediction approach to describe the diversity of the root nodule bacterial communities of rooibos (<i>Aspalathus linearis</i>), an endemic legume crop from South Africa. The goal was to reveal whether patterns of root nodule community composition in paired cultivated and wild rooibos populations could be related to geographical distance, plant traits, and plant population type (i.e. cultivated or uncultivated). We identified a core of dominant and widespread <i>Mesorhizobium</i> ZOTUs that each defined one of 4 different root nodule community classes. Rooibos cultivation impacted root nodule bacterial diversity at regional and local scales, while the geographical origin of the root nodule communities was the strongest predictor of root nodule community structure. Beyond impacts of cultivation on root nodule bacterial diversity, this study suggests a mixture of dispersal limitation and ecological drift regionally, and selection by different plant populations locally, define the biogeography of rooibos root nodule bacterial communities. </p>
Island biogeography predicts skull gigantism and shape variation in meadow voles (Microtus pennsylvanicus) through ecological release and allometry
Island Rule describes the graded trend of gigantism in small-bodied species to dwarfism in large-bodied species inhabiting islands, but causal explanations remain unresolved. We used geometric morphometrics to quantify cranial morphology of 544 meadow vole (<i>Microtus pennsylvanicus</i>) samples across 11 island and 3 mainland populations from the Outer Lands of New England (Atlantic) and the Alexander Archipelago of Alaska (Pacific). We compared the thermoregulation and endurance (TRE) and ecological release (ER) hypotheses using all-subsets linear models employing residual randomization permutation procedures (rrpp), and Akaike Information Criterion (AIC) for model selection. We decoupled direct and indirect effects of island variables on size using path analysis. We evaluated shape with Principal Components Analysis (PCA) and Procrustes ANOVA on Procrustes shape coordinates, then assessed the impact of static allometry and TRE and ER variables on shape. Six Atlantic island populations exhibit significant signals of gigantism with the largest voles occurring on the smallest islands lacking predators. ER explains 63% of cranial size differences. Island area has a significant total effect on size by influencing the number of mammalian predators, resulting in a 0.011 increase in unit centroid size for a 100 km<sup>2</sup> decrease in island area. This corresponds to a predicted 0.9% change in size for every 100 km<sup>2</sup>. Given static allometry, cranial shape does not respond to insularity independent of size. These results suggest that Island Rule is a latent evolutionary process whose manifestation depends on nuanced biogeographic and ecological contexts that have important conservation and taxonomic implications.
Figure 3 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 3. Flow chart of simulation procedure the study.
Figure 1 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 1. Study area and simulation unit
Data from: Community ecology in a bottle: Leveraging eDNA metabarcoding data to predict occupancy of co-occurring species
<p>Detecting environmental DNA (eDNA) of numerous organisms from the same samples has been revolutionized by metabarcoding. However, utilizing the vast amounts of data generated from metabarcoding to predict occupancy probabilities for co-occurring species is currently rare. Here, we demonstrate how metabarcoding data can be used to advance community ecology research through a case study using replicate stream water samples and Bayesian occupancy models to test hypotheses of eDNA occurrence for a native fish (brook trout, Salvelinus fontinalis), its major ectoparasite (gill lice, Salmincola edwardsii), and an introduced potential competitor (brown trout, Salmo trutta). Gill lice DNA occupancy was positively associated with brook trout biomass determined via electrofishing conducted near eDNA sampling sites, suggesting gill lice occupancy is dependent on host density. Leveraging site-specific molecular operational taxonomic units identified from metabarcoding, DNA occupancy of trout and gill lice was often positively predicted by species richness of aquatic insect orders trout commonly feed on, which are also environmental quality indicators. Thus, high-quality habitat that environmentally sensitive salmonids and their primary prey rely on may promote higher fish occupancy rates, further facilitating the spread of fish parasites. An increasing amount of community-level data is being generated from global metabarcoding efforts, and we suggest our framework could be broadly implemented to enhance understanding of factors impacting distributions of co-occurring species, reveal new ecological phenomena, and support management and conservation efforts.</p>
Code and data from: Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems
<p>This repository contains data and code used to produce Figures 1, 2, and S1 in <em>Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems.</em></p> <p>The file "empirical_mte_database.csv" contains a database of peer-reviewed articles pulled from a Web of Science search of papers that empirically tested the temperature dependence predictions of the metabolic theory of ecology (Brown et al. 2004) between 2004 and 2024. </p> <p>The .zip file contains a jupyter notebook, julia project toml file and a README in order to reproduce the simulations illustrating how different temperature dynamics can lead to different inferred thermal performance curves in populations.</p> <p> </p>
Data for: Ecology and behavior predict an evolutionary trade-off between song complexity and elaborate plumages in antwrens (Aves, Thamnophilidae)
<p>The environment can impose constraints on signal transmission properties such that signals should evolve in predictable directions (Sensory Drive Hypothesis). However, behavioral and ecological factors can limit investment in more than one sensory modality leading to a trade-off in use of different signals (Transfer Hypothesis). In birds, there is mixed evidence for both sensory drive and transfer hypothesis. Few studies have tested sensory drive while also evaluating the transfer hypothesis, limiting understanding of the relative roles of these processes in signal evolution. Here, we assessed both hypotheses using acoustic and visual signals in male and female antwrens (Thamnophilidae), a species-rich group that inhabits diverse environments and exhibits behaviors, such as mixed-species flocking, that could limit investment in different signal modalities. We uncovered significant effects of habitat (sensory drive) and mixed-species flocking behavior on both sensory modalities, and we revealed evolutionary trade-offs between song and plumage complexity, consistent with the transfer hypothesis. We also showed sex- and trait-specific responses in visual signals that suggest both natural and social selection play an important role in the evolution of sexual dimorphism. Altogether, these results support the idea that environmental (sensory drive) and behavioral pressures (social selection) shape signal evolution in antwrens.</p>
Predicting species abundance by implementing the ecological niche theory
<p>Species are not uniformly distributed across the landscape. For every species, there should be few favoured sites where abundance is high and many other sites of lower suitability where abundance is low. Consequently, local abundance could be thought of as a natural expression of species response to local conditions. The correlation between abundance and environmental suitability has been well documented, and a recent meta-analysis has suggested that this relationship could be a generality. Despite the importance and potential implication of the abundance-suitability relationship, its predictive power for meaningful extrapolations has been surprisingly poorly explored. In this study, we showed how a highly predictable trend can be extracted from the abundance-suitability relationship, accurately predicting the variation in species abundance at a high spatial resolution. We produced high-quality environmental suitability estimations for 50 endemic species to the Australian Wet Tropics. Environmental suitability derived from species distribution models was related to observed abundance estimated using data from 29 years of uninterrupted monitoring effort. We used the fitted relationship to accurately predict abundance at a fine scale across the species range. Our results showed that the abundance-suitability relationship was strong for endemic species in the Australian Wet Tropics. The predictive power of our models was high, explaining, on average, 55% of the deviance across taxa. Despite interspecific variation in the strength of the abundance-suitability relationship associated with potential intrinsic estimation biases, our approach provides a powerful tool for predicting abundance across the species range at a fine scale. The potential for robust abundance predictions from occurrence-based species distribution models shown in this study are numerous, and it could have a significant impact in enhancing species conservation and management decisions.</p>
A direct comparison of ecological theories for predicting the relationship between plant traits and growth
<p>Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth rates. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime's competitive-stress tolerant-ruderal (CSR) triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to relative biomass investment, leaf structure or gas exchange, respectively. We also considered traits not included in these theories, but that might provide potential alternative best predictors of growth. In phylogenetic analyses of 30 diverse milkweeds (<em>Asclepias</em> spp.) and 21 morphological and physiological traits, growth rate (total biomass produced per day) varied 50-fold and was best predicted by biomass allocation to leaves (as predicted by growth analysis) and the CSR traits of leaf size and leaf dry matter content. Total leaf area and plant height were also excellent predictors of whole-plant growth rate. Despite two LES traits correlating with growth (mass-based leaf nitrogen and area-based leaf phosphorus contents), these were in the opposite direction predicted by LES, such that higher N and P contents corresponded to slower growth. The remaining LES traits (e.g., leaf gas exchange) were not predictive of plant growth rates. Overall, differences in growth rate were driven more by whole-plant characteristics such as biomass fractions and total leaf area than individual leaf-level traits such as photosynthetic rate or specific leaf area. Our results are most consistent with classical growth analysis - combining leaf traits with whole-plant allocation to best predict growth. However, given that destructive biomass measures are often not feasible, applying easy-to-measure leaf traits associated with the CSR classification appear more predictive of whole plant growth than LES traits. Testing the generality of this result across additional taxa would further improve our ability to predict whole-plant growth from functional traits across scales.</p>
Ecological and life-history traits predict temporal trends in biomass of boreal moths
<p> Dramatic insect declines, and their consequences for ecosystems globally, have received considerable attention recently. Yet, it is still poorly known if ecological and life-history traits can explain declines and whether insect decline occurs also at high latitudes. Insects' diversity and abundance are dramatically lower at high latitudes compared to the tropics, and insects might benefit from climate warming in high-latitude environments. </p> <p>We adopted a trait- and biomass-based approach to estimate temporal change between 1993 and 2019 in Finnish macro-moth communities by using data from 85 long-running light traps. We analysed spatio-temporal variation in biomass of moth functional groups with Joint Dynamic Species Distribution Models while accounting for environmental variables. </p> <p>We did not detect any declining trends in total moth biomass of moth functional groups, and most groups were stable over time. Moreover, biomass increased for species using coniferous trees, lichens, or mushrooms as hosts, multivoltine species, as well as monophagous and oligophagous species feeding on trees. We found that length and temperature of the growing season, winter climatic conditions, and habitat structure all partially explained variation in moth biomass.</p> <p>Although boreal moth communities are rapidly changing due to species turnover, in terms of total biomass they seem to contradict the trend of dramatic insect declines observed globally. This may lessen the immediate possibility of negative bottom-up trophic cascades in boreal food webs.</p>
Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides
<div> <div> <div> <div>Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star,<em> Pycnopodia helianthoides</em>, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus <em>Pisaster</em>. These novel genomic resources for <em>Pycnopodia helianthoides</em> will underwrite population genomic, comparative genomic, and phylogenomic analyses — as well as their integration across scales — of SSW and environmental stressors. This data resource contains the files associated with gene prediction.</div> </div> </div> </div>
Predicting species abundance by implementing the ecological niche theory
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