A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

Background: Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies. Results: An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion: Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.

Evaluation of partial replacement of live and fresh feeds with a formulated diet and the influence of weaning Panulirus ornatus phyllosomata onto a formulated diet during early ontogeny

We have evaluated the potential of a formulated diet as a replacement for live and fresh feeds for 7-day post-hatch Panulirus ornatus phyllosomata and also investigated the effect of conditioning phyllosomata for 14-21 days on live feeds prior to weaning onto a 100% formulated diet. In the first trial, the highest survival (>55%) was consistently shown by phyllosomata fed a diet consisting of a 50% combination of Artemia nauplii and 50% Greenshell mussel, followed by phyllosomata fed 50% Artemia nauplii and 50% formulated diet and, thirdly, by those receiving 100% Artemia nauplii. The second trial assessed the replacement of on-grown Artemia with proportions of formulated diet and Greenshell mussel that differed from those used in trial 1. Phyllosomata fed a 75% combination of formulated diet and 25% on-grown Artemia and 50% on-grown Artemia and 50% Greenshell mussel consistently showed the highest survival (>75%). Combinations of Greenshell mussel and formulated diet resulted in significantly (P < 0.05) reduced survival. In trial 3, phyllosomata were conditioned for 14, 18 or 21 days on Artemia nauplii prior to weaning onto a 100% formulated diet, which resulted in survival rates that were negatively related to the duration of feeding Artemia nauplii. In the final trial, phyllosomata were conditioned for 14 days on live on-grown Artemia prior to weaning onto one of three formulated diets (one diet with 44% CP and two diets with 50%). Phyllosomata fed a 44% CP diet consistently showed the highest survival (>35%) among all treatments, while those fed a 50%-squid CP diet showed a significant (P < 0.05) increase in mortality at day 24. The results of these trials demonstrate that hatcheries can potentially replace 75% of live on-grown Artemia with a formulated diet 7 days after hatch. The poor performance associated with feeding combinations of Greenshell mussel and formulated diet, and 100% formulated diet as well as conditioning phyllosomata for 14-21 days on live feeds prior to weaning onto a formulated diet highlights the importance of providing Artemia to stimulate feeding.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.