Hasil untuk "Ecology"

J. Rosindell, S. Hubbell, R. Etienne

J. Silvertown

M. Kearney, W. Porter

B. Bolker

L. Jost

J. Lockwood, P. Cassey, T. Blackburn

M. Kent, P. Coker

Shinichi Nakagawa

Recently, Jennions and Moller (2003) carried out a metaanalysis on statistical power in the field of behavioral ecology and animal behavior, reviewing 10 leading journals including Behavioral Ecology. Their results showed dismayingly low average statistical power (note that a meta-analytic review of statistical power is different from post hoc power analysis as criticized in Hoenig and Heisey, 2001). The statistical power of a null hypothesis (Ho) significance test is the probability that the test will reject Ho when a research hypothesis (Ha) is true. Knowledge of effect size is particularly important for statistical power analysis (for statistical power analysis, see Cohen, 1988; Nakagawa and Foster, in press). There are many kinds of effect size measures available (e.g., Pearson’s r, Cohen’s d, Hedges’s g), but most of these fall into one of two major types, namely the r family and the d family (Rosenthal, 1994). The r family shows the strength of relationship between two variables while the d family shows the size of difference between two variables. As a benchmark for research planning and evaluation, Cohen (1988) proposed ‘conventional’ values for small, medium, and large effects: r 1⁄4.10, .30, and .50 and d 1⁄4.20, .50, and .80, respectively (in the way that p values of .05, .01, and .001 are conventional points, although these conventional values of effect size have been criticized; e.g., Rosenthal et al., 2000). The meta-analysis on statistical power by Jennions and Moller (2003) revealed that, in the field of behavioral ecology and animal behavior, statistical power of less than 20% to detect a small effect and power of less than 50% to detect a medium effect existed. This means, for example, that the average behavioral scientist performing a statistical test has a greater probability of making a Type II error (or b) (i.e., not rejecting Ho when Ho is false; note that statistical power is equals to 1 2 b) than if they had flipped a coin, when an experiment effect is of medium size (i.e., r 1⁄4 .30, d 1⁄4 .50). Here, I highlight and discuss an implication of this low statistical power on one of the most widely used statistical procedures, Bonferroni correction (Cabin and Mitchell, 2000). Bonferroni corrections are employed to reduce Type I errors (i.e., rejecting Ho when Ho is true) when multiple tests or comparisons are conducted. Two kinds of Bonferroni procedures are commonly used. One is the standard Bonferroni procedure, where a modified significant criterion (a/k where k is the number of statistical tests conducted on given data) is used. The other is the sequential Bonferroni procedure, which was introduced by Holm (1979) and popularized in the field of ecology and evolution by Rice (1989) (see these papers for the procedure). For example, in a recent volume of Behavioral Ecology (vol. 13, 2002), nearly one-fifth of papers (23 out of 117) included Bonferroni corrections. Twelve articles employed the standard procedure while 11 articles employed the sequential procedure (10 citing Rice, 1989, and one citing Holm, 1979). A serious problem associated with the standard Bonferroni procedure is a substantial reduction in the statistical power of rejecting an incorrect Ho in each test (e.g., Holm, 1979; Perneger, 1998; Rice, 1989). The sequential Bonferroni procedure also incurs reduction in power, but to a lesser extent (which is the reason that the sequential procedure is used in preference by some researchers; Moran, 2003). Thus, both procedures exacerbate the existing problem of low power, identified by Jennions and Moller (2003). For example, suppose an experiment where both an experimental group and a control group consist of 30 subjects. After an experimental period, we measure five different variables and conduct a series of t tests on each variable. Even prior to applying Bonferroni corrections, the statistical power of each test to detect a medium effect is 61% (a 1⁄4 .05), which is less than a recommended acceptable 80% level (Cohen, 1988). In the field of behavioral ecology and animal behavior, it is usually difficult to use large sample sizes (in many cases, n , 30) because of practical and ethical reasons (see Still, 1992). When standard Bonferroni corrections are applied, the statistical power of each t test drops to as low as 33% (to detect a medium effect at a/5 1⁄4 .01). Although sequential Bonferroni corrections do not reduce the power of the tests to the same extent, on average (33–61% per t test), the probability of making a Type II error for some of the tests (b 1⁄4 1 2 power, so 39–66%) remains unacceptably high. Furthermore, statistical power would be even lower if we measured more than five variables or if we were interested in detecting a small effect. Bonferroni procedures appear to raise another set of problems. There is no formal consensus for when Bonferroni procedures should be used, even among statisticians (Perneger, 1998). It seems, in some cases, that Bonferroni corrections are applied only when their results remain significant. Some researchers may think that their results are ‘more significant’ if the results pass the rigor of Bonferroni corrections, although this is logically incorrect (Cohen, 1990, 1994; Yoccoz, 1991). Many researchers are already reluctant to report nonsignificant results ( Jennions and Moller, 2002a,b). The wide use of Bonferroni procedures may be aggravating the tendency of researchers not to present nonsignificant results, because presentation of more tests with nonsignificant results may make previously ‘significant’ results ‘nonsignificant’ under Bonferroni procedures. The more detailed research (i.e., research measuring more variables) researchers do, the less probability they have of finding significant results. Moran (2003) recently named this paradox as a hyper-Red Queen phenomenon (see the paper for more discussion on problems with the sequential method). Imagine that we conduct a study where we measure as many relevant variables as possible, 10 variables, for example. We find only two variables statistically significant. Then, what should we do? We could decide to write a paper highlighting these two variables (and not reporting the other eight at all) as if we had hypotheses about the two significant variables in the first place. Subsequently, our paper would be published. Alternatively, we could write a paper including all 10 variables. When the paper is reviewed, referees might tell us that there were no significant results if we had ‘appropriately’ employed Bonferroni corrections, so that our study would not be advisable for publication. However, the latter paper is Behavioral Ecology Vol. 15 No. 6: 1044–1045 doi:10.1093/beheco/arh107 Advance Access publication on June 30, 2004

F. Berkes

A. Hampe, R. Petit

I. Annis

B. Olsen, V. Munster, A. Wallensten et al.

J. Lund, G. E. Fogg

H. Odum

Hao Xiong, Bingtao Chang, Xiaodong Lan et al.

Accurate digital terrain models (DTMs) are essential for a wide range of geospatial and environmental applications, yet their derivation in forested regions remains a significant challenge. Existing global DTMs, typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar (InSAR), fail to accurately capture understory terrain due to limited penetration capabilities, resulting in elevation overestimation in densely vegetated areas. While airborne light detection and ranging (LiDAR) can provide high-accuracy DTMs, its limited spatial coverage and high acquisition cost hinder large-scale applications. Thus, there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models (DSMs).In this study, we propose a simple, interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to construct a tree height surface model, which is then subtracted from the stereo-derived DSM to generate the final DTM. By directly incorporating LiDAR constraints, the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs, ensuring ease of implementation and broad applicability. In contrast to machine learning-based terrain modeling methods, which are often prone to overfitting and data bias, the proposed approach is simple, interpretable, and robust across diverse forested landscapes. The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model (DEM) and the forest and buildings removed DEM (FABDEM), a global bare-earth elevation model corrected for vegetation bias. The results indicate that the proposed DTM consistently outperforms the Copernicus DEM (CopDEM) and achieves accuracy comparable to FABDEM. In addition, its finer spatial resolution of 1 ​m, compared to the 30 ​m resolution of FABDEM, allows for more detailed terrain representation and better capture of fine-scale variation. This advantage is most pronounced in gently to moderately sloped areas, where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM. The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints, offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM, we applied it to a fire risk mapping application based on topographic parameters such as slope, aspect, and elevation. This case highlights how improved terrain representation can support geospatial hazard assessments.

Fatai A. Olabemiwo, Claudia Kunney, Rachel Hsu et al.

IntroductionPlastic pollution has surged due to increased human consumption and disposal of plastic products. Microbial communities capable of utilizing plastic as a carbon source may play a crucial role in degrading and consuming environmental plastic. In this study, we investigated the potential of a modified Winogradsky column (WC) to enrich Connecticut landfill soil for plastic-degrading bacteria and genes.MethodsBy filling WCs with landfill soil and inorganic Bushnell Haas medium, and incorporating polyethylene (PE) strips at different soil layers, we aimed to identify bacterial taxa capable of degrading PE. We employed high-throughput 16S rRNA sequencing to identify the microbes cultivated on the plastic strips and the intervening landfill soil. We used PICRUSt2 to estimate the functional attributes of each community from 16S rRNA sequences.Results and discussionAfter 12 months of incubation, distinct colors were observed along the WC layers, indicating successful cultivation. Sequencing revealed significant differences in bacterial communities between the plastic strips and the intervening landfill-soil habitats, including increased abundance of the phyla Verrucomicrobiota and Pseudomonadota (néé Proteobacteria) on the strips. Based on inferred genomic content, the most highly abundant proteins in PE strip communities tended to be associated with plastic degradation pathways. Phylogenetic analysis of 16S rRNA sequences showed novel unclassified phyla and genera enriched on the plastic strips. Our findings suggest PE-supplemented Winogradsky columns can enrich for plastic-degrading microbes, offering insights into bioremediation strategies.

Fulei Wei, Xianzhi Zuo, Faxin Jin et al.

Abstract Salinity adaptation is an important issue in aquaculture. Understanding the immediate-early response to salinity stress helps in comprehending this process. In vitro experiments using cell lines can explain cell-independent reactions without the involvement of hormones in vivo. In this study, salinity stress experiments were conducted using cell line derived from the gills of Gymnocypris przewalskii (GPG cell line) to isolate immediate-early response-related genes and miRNAs using transcriptomics, followed by bioinformatics analysis. The results showed that intracellular free Ca2+ appeared to be a key factor in cell sensing and initiating downstream cell signaling in response to external salinity. Additionally, cell apoptosis was the most common feature of salinity stress, with multiple signaling pathways involved in salinity-induced cell apoptosis. Furthermore, MiRNAs played a crucial role in the rapid response to salinity stress by selectively inhibiting the expression of specific genes. Additionally, for the first time in the G. przewalskii genome, Tf2 and TY3 families of transposons were found to have responsive roles to the external salinity stress. This study contributes to a better understanding of osmotic sensing in G. przewalskii and provides theoretical assistance for improving salinity adaptation in aquaculture fish species.

Joseph N Keating, Russell J Garwood, Robert S Sansom

Abstract Morphology and molecules are important data sources for estimating evolutionary relationships. Modern studies often utilise morphological and molecular partitions alongside each other in combined analyses. However, the effect of combining phenomic and genomic partitions is unclear. This is exacerbated by their size imbalance, and conflict over the efficacy of different inference methods when using morphological characters. To systematically address the effect of topological incongruence, size imbalance, and tree inference methods, we conduct a meta-analysis of 32 combined (molecular + morphology) datasets across metazoa. Our results reveal that morphological-molecular topological incongruence is pervasive: these data partitions yield very different trees, irrespective of which method is used for morphology inference. Analysis of the combined data often yields unique trees that are not sampled by either partition individually, even with the inclusion of relatively small quantities of morphological characters. Differences between morphology inference methods in terms of resolution and congruence largely relate to consensus methods. Furthermore, stepping stone Bayes factor analyses reveal that morphological and molecular partitions are not consistently combinable, i.e. data partitions are not always best explained under a single evolutionary process. In light of these results, we advise that the congruence between morphological and molecular data partitions needs to be considered in combined analyses. Nonetheless, our results reveal that, for most datasets, morphology and molecules can, and should, be combined in order to best estimate evolutionary history and reveal hidden support for novel relationships. Studies that analyse only phenomic or genomic data in isolation are unlikely to provide the full evolutionary picture.

P. Lee, R. Sukumar

Rachel Fovargue, Joseph E. Fargione, Sarah Roth et al.

Abstract New land protection is expensive, and many conservation NGOs rely on loans to help fund land acquisition in the short term. Conservation loans are offered by a range of philanthropic organizations that often allow much more flexible terms than traditional loans. Thus, conservation loans may behave differently from other types of loan. There are costs and benefits to relying on loan financing to fund land protection that organizations need to consider, but few data are available to inform such evaluations. Here, we focus on estimating the financial cost of these loans, by analyzing loans used to support land protection projects that were provided through an internal revolving fund at a large U.S. conservation NGO. We estimate loan financing cost through accrued interest and test deal‐level characteristics for their ability to explain or predict loan interest. We find that loan performance can be highly uncertain and costs can be substantial in relation to the total purchase price. An improved ability to estimate the overall cost of conservation loans upfront may determine just which conservation projects are prioritized for investment and avoid costly misallocations of conservation resources.