Simpkins, Craig Eric

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Emu - Austral Ornithology"],["dc.bibliographiccitation.lastpage","8"],["dc.contributor.author","Friesen, Megan R."],["dc.contributor.author","Simpkins, Craig E."],["dc.contributor.author","Ross, James"],["dc.contributor.author","Anderson, Sandra H."],["dc.contributor.author","Ismar-Rebitz, Stefanie M. H."],["dc.contributor.author","Tennyson, Alan J. D."],["dc.contributor.author","Taylor, Graeme A."],["dc.contributor.author","Baird, Karen A."],["dc.contributor.author","Gaskin, Chris P."],["dc.date.accessioned","2021-09-01T06:42:13Z"],["dc.date.available","2021-09-01T06:42:13Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1080/01584197.2021.1924066"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89009"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.relation.eissn","1448-5540"],["dc.relation.issn","0158-4197"],["dc.title","New population estimate for an abundant marine indicator species, Rako or Buller’s Shearwater ( Ardenna bulleri )"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","13"],["dc.bibliographiccitation.journal","Ecological Modelling"],["dc.bibliographiccitation.lastpage","23"],["dc.bibliographiccitation.volume","367"],["dc.contributor.author","Simpkins, Craig E."],["dc.contributor.author","Dennis, Todd E."],["dc.contributor.author","Etherington, Thomas R."],["dc.contributor.author","Perry, George L.W."],["dc.date.accessioned","2020-12-07T14:59:48Z"],["dc.date.available","2020-12-07T14:59:48Z"],["dc.date.issued","2018"],["dc.description.abstract","Due to increasing habitat fragmentation and concern about its ecological effects, there has been an upsurge in the use of landscape connectivity estimates in conservation planning. Measuring connectivity is challenging, resulting in a limited understanding of the efficacy of connectivity estimation techniques and the conditions under which they perform best. We evaluated the performance of four commonly used connectivity metrics – Euclidean distance; least-cost paths (LCP) length and cost; and circuit theory's resistance distance – over a variety of simulated landscapes. We developed an agent-based model simulating the dispersal of individuals with different behavioural traits across landscapes varying in their spatial structure. The outcomes of multiple dispersal attempts were used to obtain ‘true' connectivity. These ‘true' connectivity measures were then compared to estimates generated using the connectivity metrics, employing the simulated landscapes as cost-surfaces. The four metrics differed in the strength of their correlation with true connectivity; resistance distance showed the strongest correlation, closely followed by LCP cost, with Euclidean distance having the weakest. Landscape structure and species behavioural attributes only weakly predicted the performance of resistance distance, LCP cost and length estimates, with none predicting Euclidean distance's efficacy. Our results indicate that resistance distance and LCP cost produce the most accurate connectivity estimates, although their absolute performance under different conditions is difficult to predict. We emphasise the importance of testing connectivity estimates against patterns derived from independent data, such as those acquired from tracking studies. Our findings should help to inform a more refined implementation of connectivity metrics in conservation management."],["dc.identifier.doi","10.1016/j.ecolmodel.2017.11.001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69437"],["dc.language.iso","en"],["dc.relation.issn","0304-3800"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.subject.gro","Agent-based model"],["dc.subject.gro","Circuit theory"],["dc.subject.gro","Euclidean distance"],["dc.subject.gro","Least-cost modelling"],["dc.subject.gro","Resistance"],["dc.subject.gro","Simulated landscape"],["dc.title","Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","New Zealand Journal of Ecology"],["dc.bibliographiccitation.volume","42"],["dc.contributor.author","Simpkins, Craig"],["dc.contributor.author","Lee, Finnbar"],["dc.contributor.author","Powers, Breanna"],["dc.contributor.author","Anderson, Sandra"],["dc.contributor.author","Asena, Quinn"],["dc.contributor.author","Brock, James"],["dc.contributor.author","Perry, George"],["dc.date.accessioned","2020-12-07T15:36:00Z"],["dc.date.available","2020-12-07T15:36:00Z"],["dc.date.issued","2018"],["dc.description.abstract","Biodiversity assets often require conservation management, which, in turn, necessitates decisions about which ecosystem, community or species should be prioritised to receive resources. Population viability analysis (PVA) uses a suite of quantitative methods to estimate the likelihood of population decline and extinction for a given species, and can be used to assess a population's status, providing useful information to decision-makers. In New Zealand, a range of taxa have been analysed using the PVA approach, but the scope of its implementation has not previously been reviewed. We compiled a database of 78 published PVAs for New Zealand indigenous fauna and flora, along with details of the species considered, the data used to parametrise the model, and the technical details of their implementation. We assessed the taxa and threat status of the species for which PVA were conducted relative to the distribution of taxa across threat classes in the New Zealand Threat Classification System database. There were clear biases in the species selected for analysis, notably an over-representation of birds and threatened species in general, and an under-representation of invertebrates and plants. Model parameterisation and implementation were often not reported in a transparent or standardised way, which hinders model communication and reconstruction. To maximise the benefit of PVAs, we suggest that more attention should be given to the ecosystem-level importance of species, and to species whose threat status is changing rapidly or are not yet threatened. More clearly describing the parameterisation, underlying assumptions and implementation of PVAs will help to better contextualise their results and support reproducible ecological science and decision-making."],["dc.identifier.doi","10.20417/nzjecol.42.32"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69439"],["dc.language.iso","en"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.subject.gro","biodiversity"],["dc.subject.gro","conservation"],["dc.subject.gro","demographic models"],["dc.subject.gro","extinction"],["dc.subject.gro","population dynamics"],["dc.subject.gro","population models"],["dc.title","Population viability analyses in New Zealand: a review"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2240"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Methods in Ecology and Evolution"],["dc.bibliographiccitation.lastpage","2248"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Sciaini, Marco"],["dc.contributor.author","Fritsch, Matthias"],["dc.contributor.author","Scherer, Cédric"],["dc.contributor.author","Simpkins, Craig Eric"],["dc.contributor.editor","Golding, Nick"],["dc.date.accessioned","2020-12-10T18:26:40Z"],["dc.date.available","2020-12-10T18:26:40Z"],["dc.date.issued","2018"],["dc.description.abstract","1. Neutral landscape models (NLMs) simulate landscape patterns based on theoretical distributions and can be used to systematically study the effect of landscape structure on ecological processes. NLMs are commonly used in landscape ecology to enhance the findings of field studies as well as in simulation studies to provide an underlying landscape. However, their creation so far has been limited to software that is platform dependent, does not allow a reproducible workflow or is not embedded in R, the prevailing programming language used by ecologists. 2. Here, we present two complementary R packages NLMR and landscapetools, that allow users to generate and manipulate NLMs in a single environment. They grant the simulation of the widest collection of NLMs found in any single piece of software thus far while allowing for easy manipulation in a self‐contained and reproducible workflow. The combination of both packages should stimulate a wider usage of NLMs in ecology. NLMR is a comprehensive collection of algorithms with which to simulate NLMs. landscapetools provides a utility toolbox which facilitates an easy workflow with simulated neutral landscapes and other raster data. 3. We show two example applications that illustrate potential use cases for NLMR and landscapetools: First, an agent‐based simulation study in which the effect of spatial structure on disease persistence was studied. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics. 4. Simplifying the workflow around generating and handling NLMs should encourage an uptake in the usage of NLMs. NLMR and landscapetools are both generic frameworks that can be used in a variety of applications and are a further step to having a unified simulation environment in R for answering spatial research questions."],["dc.identifier.doi","10.1111/2041-210X.13076"],["dc.identifier.eissn","2041-210X"],["dc.identifier.issn","2041-210X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76152"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.issn","2041-210X"],["dc.relation.issn","2041-210X"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]