Jason W. Karl

SENSITIVITY OF SPECIES HABITAT-RELATIONSHIP MODEL PERFORMANCE TO FACTORS OF SCALE

Researchers have come to different conclusions about the usefulness of habitat-relationship models for predicting species presence or absence. This difference frequently stems from a failure to recognize the effects of spatial scales at which the models are applied. We examined the effects of model complexity, spatial data resolution, and scale of application on the performance of bird habitat relationship (BHR) models on the Craig Mountain Wildlife Management Area and on the Idaho portion of the U.S. Forest Services Northern Region. We constructed and tested BHR models for 60 bird species detected on the study areas. The models varied by three levels of complexity (amount of habitat information) and three spatial data resolutions (0.09 ha, 4 ha, 10 ha). We tested these models at two levels of analysis: the site level (a homogeneous area <0.5 ha) and cover-type level (an aggregation of many similar sites of a similar land-cover type), using correspondence between model predictions and species detections ...

Consistent Indicators and Methods and a Scalable Sample Design to Meet Assessment, Inventory, and Monitoring Information Needs Across Scales

Consistent Indicators and Methods and a Scalable Sample Design to Meet Assessment, Inventory, and Monitoring Information Needs Across Scales DOI:10.2458/azu_rangelands_v33i4_toevs

Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation

To successfully use remotely-sensed data in landscape-level management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis, segmenting images into homogeneous regions called objects, has been suggested for increasing accuracy of remotely-sensed products, but little research has gone into determining image object size with regard to scaling of ecosystem properties. We looked at how segmentation of high-resolution Ikonos and medium-resolution Landsat images into successively coarser objects affected multivariate correlations between image data and eight percent-cover measurements of a sagebrush ecosystem. We also looked at changes in correlation as imagery was aggregated into larger square pixels. We found similar canonical correlations between field and image data at the finest scales, but higher for image segmentation than pixel aggregation for both images when scale increased. For image segmentation, correlations between the canonical variables and original field variables were invariant with respect to size of the image objects, suggesting linear scaling of vegetation cover in our study system. We detected a scaling threshold with the Ikonos segmentation and confirmed with a semi-variogram of the sample data. Below the threshold interpretation of the canonical variables was consistent: scale levels differed primarily in the amount of detail portrayed. Above the threshold, meaning of the canonical variables changed. This approach proved useful for evaluating overall utility of images to address an objective, and identified scaling limits for analysis. Selection of appropriate scale for analysis will ultimately depend on the objective being considered.

Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information

Abstract A significant challenge in ecological studies has been defining scales of observation that correspond to the relevant ecological scales for organisms or processes of interest. Remote sensing has become commonplace in ecological studies and management, but the default resolution of imagery often used in studies is an arbitrary scale of observation. Segmentation of images into objects has been proposed as an alternative method for scaling remotely-sensed data into units having ecological meaning. However, to date, the selection of image object sets to represent landscape patterns has been largely subjective. Changes in observation scale affect the variance and spatial dependence of measured variables, and may be useful in determining which levels of image segmentation are most appropriate for a given purpose. We used observations of percent bare-ground cover from 346 field sites in a semi-arid shrub-steppe ecosystem of southern Idaho to look at the changes in spatial dependence of regression predictions and residuals for 10 different levels of image segmentation. We found that the segmentation level whose regression predictions had spatial dependence that most closely matched the spatial dependence of the field samples also had the strongest predicted-to-observed correlations. This suggested that for percent bare-ground cover in our study area an appropriate scale could be defined. With the incorporation of a geostatistical interpolator to predict the value of regression residuals at unsampled locations, however, we achieved consistently strong correlations across many segmentation levels. This suggests that if spatial dependence in percent bare ground is accounted for, a range of appropriate scales could be defined. Because the best analysis scale may vary for different ecosystem attributes and many inquiries consider more than one attribute, methods that can perform well across a range of scales and perhaps not at a single, ideal scale are important. More work is needed to develop methods that consider a wider range of ways to segment images into different scales and select sets of scales that perform best for answering specific management questions. The robustness of ecological landscape analyses will increase as methods are devised that remove the subjectivity with which observational scales are defined and selected.

The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing

Agricultural production must increase significantly to meet the needs of a growing global population with increasing per capita consumption of food, fiber, building materials, and fuel. Consumption already exceeds net primary production in many parts of the world (Imhoff et al. 2004). In addition to reducing consumption, there are two options to meet these needs: production intensification and land conversion. Both strategies present unique opportunities, challenges, and risks. The largest gains achievable through agricultural intensification will likely occur on lands with the largest unrealized production potential, or yield gap. These lands have high potential production and low current production. Similarly, the highest returns on investments to be gained by land conversion should occur on lands with the highest potential production, assuming similar infrastructure, per acre conversion costs, and other market conditions. The biggest long-term risk for both strategies is that application of nonsustainable land management practices will result in soil degradation that is often costly, if not impossible, to reverse. Exploiting these opportunities and minimizing risks depend on careful matching of production systems with the sustainable production potential of each type of land. Similar analyses can be applied to biodiversity conservation to prioritize land conservation and restoration efforts. The ability…

Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing

Abstract Sound rangeland management requires accurate information on rangeland condition over large landscapes. A commonly applied approach to making spatial predictions of attributes related to rangeland condition (e.g., shrub or bare ground cover) from remote sensing is via regression between field and remotely sensed data. This has worked well in some situations but has limited utility when correlations between field and image data are low and it does not take advantage of all information contained in the field data. I compared spatial predictions from generalized least-squares (GLS) regression to a geostatistical interpolator, regression kriging (RK), for three rangeland attributes (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]) in a southern Idaho study area. The RK technique combines GLS regression with spatial interpolation of the residuals to improve predictions of rangeland condition attributes over large landscapes. I employed a remote-sensing technique, object-based image analysis (OBIA), to segment Landsat 5 Thematic Mapper images into polygons (i.e., objects) because previous research has shown that OBIA yields higher image-to-field data correlations and can be used to select appropriate scales for analysis. Spatial dependence, the decrease in autocorrelation with increasing distance, was strongest for percent shrub cover (samples autocorrelated up to a distance [i.e., range] of 19 098 m) but present in all three variables (range of 12 646 m and 768 m for bare ground and cheatgrass cover, respectively). As a result, RK produced more accurate results than GLS regression alone for all three attributes when predicted versus observed values of each attribute were measured by leave-one-out cross validation. The results of RK could be used in assessments of rangeland conditions over large landscapes. The ability to create maps quantifying how prediction confidence changes with distance from field samples is a significant benefit of regression kriging and makes this approach suitable for landscape-level management planning.

A Strategy for Rangeland Management Based on Best Available Knowledge and Information

Abstract Adapting what we currently know about ecosystems to a future where rangelands are changing is a new frontier in rangeland management. Current tools for knowledge discovery and application are limited because they cannot adequately judge ecological relevance of knowledge to specific situations. We propose development of integrated knowledge systems (KSs)—collections of resources (e.g., data, analytical tools, literature) drawn from disparate domains and organized around topics by process-based conceptual models. An integrated KS would define relevance by ecological attributes (e.g., soils, climate, vegetation) and location as a flexible mechanism for organizing, finding, and applying knowledge to rangeland management. A KS provides knowledge sources within a decision-making framework that defines what knowledge is needed and how it will be used to make decisions. Knowledge from a KS can identify appropriate spatial and temporal scales to address specific resource questions or objectives. Several factors currently limit KS development and implementation. These include limited interoperability of disparate information and knowledge systems; lack of consistent geographic referencing of knowledge; incomplete and inconsistent documentation of the origin, history and meaning of data and information; underexploited application of remote sensing products; limited ability to extrapolate and share local knowledge and unstructured information; and lack of training and education of professionals that can link ecological and technical fields of study. The proposed KS concept and recommendations present an opportunity to take advantage of emerging technologies and the collective knowledge of rangeland professionals to address changing ecosystems and evolving threats. If we keep on with a “business as usual” approach to finding and using information, we will struggle to meet our responsibilities as rangeland professionals. Resumen Adaptar lo que actualmente sabemos acerca de los ecosistemas a un futuro donde los pastizales han cambiando es una nueva frontera en el manejo de pastizales. Las herramientas que existen en la actualidad para el descubrimiento del conocimiento y su aplicación son limitadas porque no pueden juzgar adecuadamente la relevancia ecológica del conocimiento para situaciones específicas. Propusimos el desarrollo de sistemas de conocimiento integrales (KSs)—colecciones de recursos (ej., datos, herramientas analíticas, literatura) elaborado a partir de áreas diferentes y organizados en torno a temas por procesos basado en modelos conceptuales. Un KS integrado podría definir la relevancia por atributos ecológicos (ej., suelos, climas, vegetación) y la locación como un mecanismo flexible para organizar, encontrar, la aplicación de conocimiento al manejo de pastizales. Un KS provee fuentes de conocimiento dentro de un marco de toma de decisiones que define que conocimiento es necesitado y cómo va a usarse para tomar decisiones. El conocimiento de un KS puede identificar escalas espaciales apropiadas y temporales para responder preguntas de recursos específicas u objetivos. Varios factores en la actualidad limitan el desarrollo y la implementación de KS. Entre ellos encontramos: interoperabilidad limitada de información dispar y los sistemas de conocimiento. Falta de referencias geográficas consistentes del conocimiento; documentación incompleta e inconsistente de documentación de origen, historia y significado de datos e información; aplicación sin explorar de los productos de teleobservacion; habilidad limitada para extrapolar y compartir conocimiento local e información no estructurada; y entrenamiento y educación de profesionales que pueden unir los campos de estudios ecológicos y técnicos. El concepto KS propuesto y las recomendaciones son una oportunidad para aprovechar las tecnologías emergentes y el conocimiento colectivo de los pastizales para hacer frente al cambio de los ecosistemas y los riegos cambiantes. Si seguimos con un enfoque tradicional para encontrar y usar información, vamos a enfrentar serias dificultades para cumplir con nuestras responsabilidades como profesionales de los pastizales.

A holistic strategy for adaptive land management

Adaptive management is widely applied to natural resources management (Holling 1973; Walters and Holling 1990). Adaptive management can be generally defined as an iterative decision-making process that incorporates formulation of management objectives, actions designed to address these objectives, monitoring of results, and repeated adaptation of management until desired results are achieved (Brown and MacLeod 1996; Savory and Butterfield 1999). However, adaptive management is often criticized because very few projects ever complete more than one cycle, resulting in little adaptation and little knowledge gain (Lee 1999; Walters 2007). One significant criticism is that adaptive management is often used as a justification for undertaking actions with uncertain outcomes or as a surrogate for the development of specific, measurable indicators and monitoring programs (Lee 1999; Ruhl 2007). In this paper, we argue for a more holistic and systematic approach to adaptive management. We define holistic adaptive land management (HALM) as a refinement of adaptive management that requires (1) a process-based understanding of ecosystem dynamics and ecological mechanisms, (2) a willingness and ability to identify and consider all possible management alternatives, (3) rigorous monitoring of management effects, and (4) constant adaptation of management based on monitoring data and associated observations. Thus, HALM requires both…

Revolutionary land use change in the 21st century: is (Rangeland) science relevant?

Abstract Rapidly increasing demand for food, fiber, and fuel together with new technologies and the mobility of global capital are driving revolutionary changes in land use throughout the world. Efforts to increase land productivity include conversion of millions of hectares of rangelands to crop production, including many marginal lands with low resistance and resilience to degradation. Sustaining the productivity of these lands requires careful land use planning and innovative management systems. Historically, this responsibility has been left to agronomists and others with expertise in crop production. In this article, we argue that the revolutionary land use changes necessary to support national and global food security potentially make rangeland science more relevant now than ever. Maintaining and increasing relevance will require a revolutionary change in range science from a discipline that focuses on a particular land use or land cover to one that addresses the challenge of managing all lands that, at one time, were considered to be marginal for crop production. We propose four strategies to increase the relevance of rangeland science to global land management: 1) expand our awareness and understanding of local to global economic, social, and technological trends in order to anticipate and identify drivers and patterns of conversion; 2) emphasize empirical studies and modeling that anticipate the biophysical (ecosystem services) and societal consequences of large-scale changes in land cover and use; 3) significantly increase communication and collaboration with the disciplines and sectors of society currently responsible for managing the new land uses; and 4) develop and adopt a dynamic and flexible resilience-based land classification system and data-supported conceptual models (e.g., state-and-transition models) that represent all lands, regardless of use and the consequences of land conversion to various uses instead of changes in state or condition that are focused on a single land use. Resumen La creciente demanda de alimentos, fibras y combustibles de manera simultánea con las nuevas tecnologías y la movilidad global del capital están ocasionando cambios revolucionados en el uso de la tierra en todo el mundo. Los esfuerzos para incrementar la productividad de la tierra incluyen la conversión de millones de hectáreas de pastizales a la producción de granos, incluyendo tierras marginales con bajo resistencia y resilencia a la degradación. Sostener la productividad de estas tierras requiere planeación cuidadosa del uso de la tierra y sistemas de manejo innovadores. Históricamente, esta responsabilidad se ha dejado a agrónomos y otros expertos en producción de granos. En este articulo, discutimos que los revolucionados cambios en uso de la tierra necesarios para sostener la seguridad alimentaria nacional y mundial potencialmente hacen a la ciencia del pastizal más relevante ahora que nunca. Mantener e incrementar esa relevancia requerirá de cambios revolucionarios en la ciencia del pastizal de una disciplina que se enfoca en un uso particular de la tierra o cubierta vegetal a una que considere el reto de manejar todas las tierras que en algún tiempo fueron consideradas marginales para la producción de granos. Proponemos cuatro estrategias para aumentar la relevancia de la ciencia del pastizal a un manejo global de la tierra: 1) extender nuestra conocimiento y concientización del ámbito local a tendencias globales económicas, sociales y tecnológicas con el fin de anticipar e identificar conductores y patrones de conversión, 2) enfatizar en estudios empíricos y modelaje que anticipe las consecuencias biofísicas (servicios de los ecosistemas) y sociales de cambios en la cobertura y uso de la tierra en gran escala, 3) aumentar significativamente la comunicación y colaboración con las disciplinas y sectores de la sociedad actualmente responsables en el manejo del nuevo uso de la tierra, y 4) desarrollar y adoptar un sistema de clasificación dinámica y flexible basado en la resilencia de la tierra y modelos conceptuales apoyados en datos (ejm. Modelos de Estado y Transición) que representan todas las tierras, independientemente del uso y las consecuencias en la conversión de tierras para varios usos el lugar de cambios en el estado y condición que se enfocan en un solo uso de la tierra.

Local and national protection of endangered species: an assessment

Abstract We searched the statutory codes of all 50 states to locate provisions applicable to endangered and threatened species. The state statutes were compared to 6 components of the US Endangered Species Act: (1) coverage; (2) listing procedures and requirements under section 4; (3) habitat designation and protection procedures and criteria under sections 4 and 7; (4) prohibitions on commerce and taking under section 9; (5) exceptions to the prohibitions on commerce and taking and (6) conservation planning under section 4. State endangered and threatened species legislation is far less comprehensive than the federal act. Only 15 states have statutes that cover all plants and animals. Similarly, only 11 states offer any protection for taxa below the subspecific level. 45 states have provisions for listing species independently of the federal act but only 8 authorize emergency listings. 43 states have no provisions authorizing the designation of critical habitat; 39 states offer no protection against habitat destruction on either private or publicly owned lands. Most states prohibit commercial transactions and taking of listed animal species; plant species receive less protection. Only 3 states include any requirements that the wildlife management agency engage in recovery planning processes. In the absence of a federal statute to protect endangered and threatened species, we question whether current state protection is either adequate or would be maintained. We briefly examined legislation on endangered species in two other countries with federal systems of government, Australia and Canada. Canada lacked a federal statute. Assessment of national, state and territorial legislation in Australia revealed several similarities and differences with the United States endangered species legislation. Differences suggested an alternative to the top down approach embodied in the United States Endangered Species Act.