Kate S. He

Remotely Sensed Spectral Heterogeneity As a Proxy of Species Diversity: Recent Advances and Open Challenges

Abstract Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.

Advancing species diversity estimate by remotely sensed proxies: A conceptual review

Abstract Many geospatial tools have been advocated in spatial ecology to estimate biodiversity and its changes over space and time. Such information is essential in designing effective strategies for biodiversity conservation and management. Remote sensing is one of the most powerful approaches to identify biodiversity hotspots and predict changes in species composition in reduced time and costs. This is because, with respect to field-based methods, it allows to derive complete spatial coverages of the Earth surface under study in a short period of time. Furthermore, remote sensing provides repeated coverages of field sites, thus making studies of temporal changes in biodiversity possible. In this paper we discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices, including alpha- and beta-diversity measurements.

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

Vascular plant species richness on wetland remnants is determined by both area and habitat heterogeneity

There is an ongoing ecological debate on whether area per se or habitat heterogeneity is the main driver for species richness. The wetland remnants in the Sanjiang Plain, NE China harbor a high biodiversity and play an important role for local ecosystems. Fifty-one wetland remnants were sampled to examine the effect of area and habitat heterogeneity on vascular plant species richness. Number of community types, elevation, water heterogeneity and soil resource heterogeneity were employed as habitat heterogeneity variables, but only water heterogeneity was identified as the proper surrogate for habitat heterogeneity. Compared with the classic species-area model, the choros model achieved better fitness when water heterogeneity and elevation were employed as habitat heterogeneity variables. Nevertheless, elevation was poorly correlated with species richness. It suggests, without a comprehensive analysis of habitat heterogeneity variables, the choros model might result in a misleading result. In this study, species richness was significantly influenced by water heterogeneity, area and number of community types. Water heterogeneity and area both controlled the number of community types, and they were the two main determinants of species richness. As area was significantly and positively correlated with water heterogeneity, the variance in species richness was mainly related to the mutual effect of area and water heterogeneity. The results of this study confirmed that the relationship between the area per se hypothesis and the habitat heterogeneity hypothesis was conjunct rather than mutually exclusive. In addition, it is critical that both area and water heterogeneity should be taken into account for biodiversity conservation and management in wetland remnants.

Spectral variation versus species β-diversity at different spatial scales: a test in African highland savannas

Few studies exist that explicitly analyse the effect of grain, i.e. the sampling unit dimension, on vascular plant species turnover (beta-diversity) among sites. While high beta-diversity is often a result of high environmental heterogeneity, remotely sensed spectral distances among sampling units may be used as a proxy of environmental gradients which spatially shape the patterns of species turnover. In this communication, we aimed to (i) test the potential relation between spectral variation and species beta-diversity in a savanna environment and to (ii) investigate the effect of grain on the achieved patterns. Field data gathered by the BIOTA Southern Africa biodiversity monitoring programme were used to model the relation between spectral variation and species turnover at different spatial grains (10 m x 10 m and 20 m x 50 m). Our results indicate that the overall fit was greater at the larger grain size, confirming the theoretical assumption that using a lower grain size would generally lead to a higher noise in the calculation of species turnover. This communication represents one of the first attempts at relating beta-diversity to spectral variation, while incorporating the effects of grain size in the study. The results of this study could have significant implications for biodiversity research and conservation planning at a regional or even larger spatial scale.

Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

A second horizon scan of biogeography: Golden Ages, Midas touches, and the Red Queen

Are we entering a new ‘Golden Age’ of biogeography, with continued development of infrastructure and ideas? We highlight recent developments, and the challenges and opportunities they bring, in light of the snapshot provided by the 7th biennial meeting of the International Biogeography Society (IBS 2015). We summarize themes in and across 15 symposia using narrative analysis and word clouds, which we complement with recent publication trends and ‘research fronts’. We find that biogeography is still strongly defined by core sub-disciplines that reflect its origins in botanical, zoological (particularly bird and mammal), and geographic (e.g., island, montane) studies of the 1800s. That core is being enriched by large datasets (e.g. of environmental variables, ‘omics’, species’ occurrences, traits) and new techniques (e.g., advances in genetics, remote sensing, modeling) that promote studies with increasing detail and at increasing scales; disciplinary breadth is being diversified (e.g., by developments in paleobiogeography and microbiology) and integrated through the transfer of approaches and sharing of theory (e.g., spatial modeling and phylogenetics in evolutionary–ecological contexts). Yet some subdisciplines remain on the fringe (e.g., marine biogeography, deep-time paleobiogeography), new horizons and new theory may be overshadowed by popular techniques (e.g., species distribution modelling), and hypotheses, data, and analyses may each be wanting. Trends in publication suggest a shift away from traditional biogeography journals to multidisciplinary or open access journals. Thus, there are currently many opportunities and challenges as biogeography increasingly addresses human impacts on, and stewardship of, the planet (e.g., Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services). As in the past, biogeographers doubtless will continue to be engaged by new data and methods in exploring the nexus between biology and geography for decades into the future. But golden ages come and go, and they need not touch every domain in a discipline nor affect subdisciplines at the same time; moreover, what appears to be a Golden Age may sometimes have an undesirable ‘Midas touch’. Contexts within and outwith biogeography—e.g., methods, knowledge, climate, biodiversity, politics—are continually changing, and at times it can be challenging to establish or maintain relevance. In so many races with the Red Queen, we suggest that biogeography will enjoy greatest success if we also increasingly engage with the epistemology of our discipline.

Important characteristics of multispectral data for an assessment of floristic variation

Remote sensing offers a large potential for land-cover mapping and monitoring. For many application in nature conservation, however, information beyond the generalization level of regular land-cover maps is desirable.