Habitat representativeness score (HRS): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species

2010 ◽  
Vol 90 (7) ◽  
pp. 1269-1277 ◽  
Author(s):  
Colin D. MacLeod
Keyword(s):  
Species Distribution ◽  
Environmental Variables ◽  
Survey Design ◽  
Objective Assessment ◽  
Principal Component ◽  
Marine Species ◽  
Representative Survey ◽  
Distribution Models ◽  
Data Set ◽  
Minimum Number

The occurrence of most species is linked to the distribution of specific combinations of environmental variables that define their occupied niche. As a result, the relationship between environmental variables and species occurrence can be used to model species distribution. However, when collecting data to construct such models, it is preferable to ensure that the survey coverage is representative of all available habitat combinations within the area as a whole to ensure that the model does not under- or over-estimate the actual species distribution. By using multi-variate statistical techniques, a habitat representativeness score (HRS) can be calculated to provide an objective assessment of whether a specific survey coverage will collect (or has collected) data that are representative of all available habitat variable combinations in an area. To demonstrate this approach, HRSs calculated using principal component analysis were used to assess the minimum number of evenly-spaced parallel north–south surveys required to adequately survey two study areas with differing levels of environmental heterogeneity for all available combinations of four habitat variables. For the more environmentally homogeneous study area, the HRS suggests that for this survey design a minimum of five evenly-spaced parallel transects, covering around 5% of the study area, would be required to obtain representative survey coverage for these four variables. However, for the more heterogeneous study area, at least eight evenly-spaced parallel transects, covering around 9% of the study area, would be required. Therefore, for a given survey design, more survey effort is required to obtain a representative survey coverage when the survey area is more variable. In both cases, conducting fewer surveys than these minimum values would produce an unrepresentative data set and this could potentially lead to the production of species distribution models that do not accurately reflect the true species distribution.

Effects of sample survey design on the accuracy of classification tree models in species distribution models

2006 ◽  
Vol 199 (2) ◽  
pp. 132-141 ◽  
Author(s):  
Thomas C. Edwards ◽  
D. Richard Cutler ◽  
Niklaus E. Zimmermann ◽  
Linda Geiser ◽  
Gretchen G. Moisen
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Classification Tree ◽  
Sample Survey ◽  
Distribution Models ◽  
Tree Models

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2017 ◽  
pp. 427-440
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

Using verified species distribution models to inform the conservation of a rare marine species

2016 ◽  
Vol 22 (7) ◽  
pp. 808-822 ◽  
Author(s):  
David A. Stirling ◽  
Philip Boulcott ◽  
Beth E. Scott ◽  
Peter J. Wright
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Marine Species ◽  
Distribution Models

scholarly journals Associations Between Habitat Quality And Body Size In The Carpathian Land Snail Vestia turgida: Species Distribution Model Selection And Assessment Of Performance

2020 ◽  
Author(s):  
V. Tytar ◽  
O. Baidashnikov
Keyword(s):  
Body Size ◽  
Habitat Quality ◽  
Species Distribution ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Land Snail ◽  
Distribution Model ◽  
Distribution Models ◽  
Data Set ◽  
Additive Regression

Species distribution models (SDMs) are generally thought to be good indicators of habitat suitability, and thus of species’ performance, consequently SDMs can be validated by checking whether the areas projected to have the greatest habitat quality are occupied by individuals or populations with higher than average fitness. We hypothesized a positive and statistically significant relationship between observed in the field body size of the snail V. turgida and modelled habitat suitability, tested this relationship with linear mixed models, and found that indeed, larger individuals tend to occupy high-quality areas, as predicted by the SDMs. However, by testing several SDM algorithms, we found varied levels of performance in terms of expounding this relationship. Marginal R2, expressing the variance explained by the fixed terms in the regression models, was adopted as a measure of functional accuracy, and used to rank the SDMs accordingly. In this respect, the Bayesian additive regression trees (BART) algorithm (Carlson, 2020) gave the best result, despite the low AUC and TSS. By restricting our analysis to the BART algorithm only, a variety of sets of environmental variables commonly or less used in the construction of SDMs were explored and tested according to their functional accuracy. In this respect, the SDM produced using the ENVIREM data set (Title, Bemmels, 2018) gave the best result.

scholarly journals Constraints of using historical data for modelling the spatial distribution of helminth parasites in ruminants

Parasite ◽  
2021 ◽  
Vol 28 ◽  
pp. 46
Author(s):  
Alizée Hendrickx ◽  
Cedric Marsboom ◽  
Laura Rinaldi ◽  
Hannah Rose Vineer ◽  
Maria Elena Morgoglione ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Sample Size ◽  
Species Distribution ◽  
Historical Data ◽  
Data Sets ◽  
Stable Model ◽  
Helminth Parasites ◽  
Distribution Models ◽  
Baseline Model ◽  
Data Set

Dicrocoelium dendriticum is a trematode that infects ruminant livestock and requires two different intermediate hosts to complete its lifecycle. Modelling the spatial distribution of this parasite can help to improve its management in higher risk regions. The aim of this research was to assess the constraints of using historical data sets when modelling the spatial distribution of helminth parasites in ruminants. A parasitological data set provided by CREMOPAR (Napoli, Italy) and covering most of Italy was used in this paper. A baseline model (Random Forest, VECMAP®) using the entire data set was first used to determine the minimal number of data points needed to build a stable model. Then, annual distribution models were computed and compared with the baseline model. The best prediction rate and statistical output were obtained for 2012 and the worst for 2016, even though the sample size of the former was significantly smaller than the latter. We discuss how this may be explained by the fact that in 2012, the samples were more evenly geographically distributed, whilst in 2016 most of the data were strongly clustered. It is concluded that the spatial distribution of the input data appears to be more important than the actual sample size when computing species distribution models. This is often a major issue when using historical data to develop spatial models. Such data sets often include sampling biases and large geographical gaps. If this bias is not corrected, the spatial distribution model outputs may display the sampling effort rather than the real species distribution.

scholarly journals Historical disturbances to rivers appear to constrain contemporary distribution of a river-dependent frog

2021 ◽  
Author(s):  
Mark A. Linnell ◽  
Raymond J. Davis
Keyword(s):  
Species Distribution ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Ensemble Model ◽  
Distribution Models ◽  
Precipitation Frequency ◽  
Historical Distribution ◽  
Large Water ◽  
Source Populations ◽  
Riparian Corridor

AbstractFrogs dependent on lotic environments are sensitive to disturbances that alter the hydrology (e.g., water impoundments), substrate (e.g., debris torrents), and riparian vegetation (e.g., wildfires) of river ecosystems. Although rivers are often very dynamic, disturbances can push environmental baselines outside of narrowly defined ecological tolerances under which a species evolved. Short-lived lotic-dependent organisms, restricted to movements within the water or the riparian corridor, are at risk of local extirpations owing to such disturbances if they fragment and isolate affected populations from recolonizing source populations. In Oregon, USA, the foothill yellow-legged frog (Rana boylii) is at its northernmost range margin and has experienced an approximately 41% range contraction compared to their historical distribution. To inform conservation and management, we used species distribution models to identify environmentally suitable watersheds based on intrinsic baseline environmental variables, and then examined potential effects of human-caused alterations to rivers, including splash dams used to ferry timber downstream prior to 1957, large water impoundments, and adjacency to agricultural croplands. We used machine-learning in program Maxent and three different river layers that varied in extent and location of mapped rivers but contained distinct information to produce species distribution models which we then combined into a single ensemble model. Stream order, annual precipitation, and precipitation frequency were the highest ranked baseline environmental variables in most models. Watersheds with highly suitable baseline conditions in our ensemble model were negatively correlated with anthropogenic disturbances to rivers. Foothill yellow-legged frogs appeared to be sensitive to human-caused disturbances to rivers, perhaps indicative of their narrow ecological tolerance to in-river conditions. We do not anticipate variables in our model to change much through time. Rather, for conservation we identified potential legacy (spash dams) and ongoing human-caused disturbances that are more likely to change conditions for the species in the short- and long-term.

Uncertainty associated with survey design in Species Distribution Models

2014 ◽  
Vol 20 (11) ◽  
pp. 1258-1269 ◽  
Author(s):  
Geiziane Tessarolo ◽  
Thiago F. Rangel ◽  
Miguel B. Araújo ◽  
Joaquín Hortal
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Distribution Models

scholarly journals Effectiveness of Joint Species Distribution Models in the Presence of Imperfect Detection

2021 ◽  
Author(s):  
Stephanie Hogg ◽  
Yan Wang ◽  
Lewi Stone
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Survey Design ◽  
Spatial Modelling ◽  
Probability Of Detection ◽  
List Type ◽  
Distribution Models ◽  
Imperfect Detection ◽  
Biased Estimates

AbstractJoint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of “collapsed data”. A case study of owls and gliders in Victoria Australia is also illustrated.Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to dis-entangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.

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