Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

2011 ◽  
Vol 222 (15) ◽  
pp. 2796-2811 ◽  
Author(s):  
Robert P. Anderson ◽  
Israel Gonzalez
Keyword(s):  
Species Distributions ◽  
Sampling Bias ◽  
Species Specific

scholarly journals Sampling bias in reptile occurrence data for the Kruger National Park

Koedoe ◽  
2020 ◽  
Vol 62 (1) ◽  
Author(s):  
Jody M. Barends ◽  
Darren W. Pietersen ◽  
Guinevere Zambatis ◽  
Donovan R.C. Tye ◽  
Bryan Maritz
Keyword(s):  
National Park ◽  
Conservation Management ◽  
Species Distributions ◽  
Sampling Bias ◽  
Kruger National Park ◽  
Data Set ◽  
Occurrence Data ◽  
Reptile Species ◽  
Sampling Biases ◽  
Occurrence Records

o effectively conserve and manage species, it is important to (1) understand how they are spatially distributed across the globe at both broad and fine spatial resolutions and (2) elucidate the determinants of these distributions. However, information pertaining to the distributions of many species remains poor as occurrence data are often scarce or collected with varying motivations, making the resulting patterns susceptible to sampling bias. Exacerbating an already limited quantity of occurrence data with an assortment of biases hinders their effectiveness for research, thus making it important to identify and understand the biases present within species occurrence data sets. We quantitatively assessed occurrence records of 126 reptile species occurring in the Kruger National Park (KNP), South Africa, to quantify the severity of sampling bias within this data set. We collated a data set of 7118 occurrence records from museum, literature and citizen science sources and analysed these at a biologically relevant spatial resolution of 1 km × 1 km. As a result of logistical challenges associated with sampling in KNP, approximately 92% of KNP is data deficient for reptile occurrences at the 1 km × 1 km resolution. Additionally, the spatial coverage of available occurrences varied at species and family levels, and the majority of occurrence records were strongly associated with publicly accessible human infrastructure. Furthermore, we found that sampled areas within KNP were not necessarily ecologically representative of KNP as a whole, suggesting that areas of unique environmental space remain to be sampled. Our findings highlight the need for substantially greater sampling effort for reptiles across KNP and emphasise the need to carefully consider the sampling biases within existing data should these be used for conservation management decision-making. Modelling species distributions could potentially serve as a short-term solution, but a concomitant increase in surveys across the park is needed.Conservation implications: The sampling biases present within KNP reptile occurrence data inhibit the inference of fine-scale species distributions within and across the park, which limits the usage of these data towards meaningfully informing conservation management decisions as applicable to reptile species in KNP.

Species’ Occurrence Data

2011 ◽  
Author(s):  
A. Townsend Peterson ◽  
Jorge Soberón ◽  
Richard G. Pearson ◽  
Robert P. Anderson ◽  
Enrique Martínez-Meyer ◽  
...  
Keyword(s):  
Species Distributions ◽  
Sampling Bias ◽  
Species Occurrence ◽  
Ecological Conditions ◽  
Broad Applicability ◽  
Occurrence Data ◽  
Data Record ◽  
Different Types ◽  
Correlative Models ◽  
A Site

This chapter discusses the process of transforming a species’ primary occurrence data into a synthetic understanding of the geographic and ecological conditions under which the species occurs. The focus is on correlative models based on occurrence data, since such models can have quite broad applicability. The chapter first considers different types of occurrence data as well as factors that connect the suitability of a site to the existence of a data record documenting the species’ presence or absence at that site. It then examines variations in the geographic and ecological characteristics of species distributions and occurrences, along with sampling bias in geographic and environmental spaces. It also describes the characteristics of absence data before concluding with an assessment of issues of content and availability that affect occurrence data.

Electron Microscopy in the Study of Natural Phytoplankton Assemblages

1985 ◽  
Vol 43 ◽  
pp. 620-623
Author(s):  
Linda Sicko-Goad
Keyword(s):  
Electron Microscopy ◽  
Aquatic Environment ◽  
Size Range ◽  
Physical Parameters ◽  
Specific Information ◽  
Phytoplankton Assemblages ◽  
Phytoplankton Productivity ◽  
Ecosystem Effects ◽  
Time Of Year ◽  
Species Specific

Although the use of electron microscopy and its varied methodologies is not usually associated with ecological studies, the types of species specific information that can be generated by these techniques are often quite useful in predicting long-term ecosystem effects. The utility of these techniques is especially apparent when one considers both the size range of particles found in the aquatic environment and the complexity of the phytoplankton assemblages.The size range and character of organisms found in the aquatic environment are dependent upon a variety of physical parameters that include sampling depth, location, and time of year. In the winter months, all the Laurentian Great Lakes are uniformly mixed and homothermous in the range of 1.1 to 1.7°C. During this time phytoplankton productivity is quite low.

64: Rapid, Species-Specific Detection of Uropathogens from Clinical Urine Specimens Using an Electrochemical Microsensor Array

2005 ◽  
Vol 173 (4S) ◽  
pp. 18-18
Author(s):  
Joseph C. Liao ◽  
Mitra Mastali ◽  
David A. Haake ◽  
Bernard M. Churchill
Keyword(s):  
Specific Detection ◽  
Species Specific ◽  
Urine Specimens
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