Wetlands provide significant environmental services, including pollutant assimilation, flood water storage, carbon sequestration and fish and wildlife habitat. Geographically isolated wetlands, also called ephemeral wetlands, perform many of these services, and support remarkably diverse communities. The periodic drying of small isolated wetlands excludes predatory fish populations, making them especially valuable breeding habitat for amphibians and invertebrates. In Florida, 14 species of amphibians breed exclusively in isolated wetlands. It is often difficult to map isolated wetlands because they are relatively small, often occur under tree canopy and may remain dry for extended periods.
A predictive algorithm (maximum entropy using a program called Maxent) was applied to environmental variables derived from multispectral remotely sensed data and GIS data to approach the problem of detecting wetland areas absent from the National Wetlands Inventory (NWI) layer in the watershed that contains the bulk of the Blackwater River State Forest.
An elevation map created from Lidar (Light Detection and Ranging – a tool using a laser to collect extremely detailed elevation data), was used to identify low areas (sinks) in which water is likely to collect. Several Maxent modelling runs were performed in order to identify the combination of environmental variables with the least correlation that would result in the simplest model while preserving a high information content. Because Maxent produces a continuous probability distribution, a binary threshold was produced to conduct an accuracy assessment. The threshold was applied at 0.3517, derived from the mean of all sinks falling within known wetlands.
Twenty percent (2278 of 11389) of the sinks that fell outside of known wetlands were randomly selected for accuracy assessment via current and historic (to 1994) Google Earth aerial photographs. Based on model evaluation methods, the final Maxent model was considered an “excellent” model fit. At the selected threshold, 120.5 km2 of 1848.4 km2 were predicted to be wetland areas. The accuracy assessment yielded an overall accuracy of 0.90, with 1257 points being wetlands, 142 points falling outside of wetlands and 881 points impossible to classify from the photographs.
Restoration efforts are necessary to aid coral reef survival and resilience. To this end, performing coral restoration research and filling information gaps are the Legacy Initiative’s primary marine goals for the next four years. A team of internal and external experts established research into and identification of potentially more resilient areas of coral reef habitat as a priority need in addressing coral reef restoration.
Over the past year and a half, IS&M research scientists have worked with non-profit, academic, federal, and state partners in south Florida to define reef resiliency, identify restoration sites and to gather expert advice on restoration for listed coral species. We quickly learned that coral restoration means many different things to different people. For example, restoration can mean outplanting acroporid corals originating from a nursery or simply protecting species-rich reefs from harmful activities. Despite the disagreement over definitions, there is somewhat close agreement between scientists on the environmental conditions associated with coral reef decline and improvement.
To accommodate the best available science on coral survival and different definitions of restoration, we decided to create a dynamic mapping tool that allows users to prioritize restoration sites for multiple user defined restoration activities. The tool is based on the Zonation conservation prioritization software, but incorporates several layers of statistical models built in R that incorporate the best available science on coral survival and distribution (i.e., literature information, stakeholder databases, in situ databases, etc.). For example, sea bottom temperature models feed into Acropora cervicornis distribution models, which in turn can overlap with an anthropogenic stress model in Zonation if a user is interested in examining the spatial relationships between Acropora cervicornis occurrence and anthropogenic stress. There are countless more sub-model combinations that allow users to explore different restoration scenarios. We are currently in the process of validating all of the sub-models that feed into the site prioritization model, but we expect the tool will be fully implemented by the end of June this year.
There are currently 147 areas encompassing 5.9 million acres within the Wildlife Management Area (WMA) system of Florida. They are rustic natural areas and were established to provide wildlife-centric recreation and contribute to the biological diversity of the state. Some areas offer hunting, fishing wildlife viewing, cycling, horseback riding and paddling. The WMA System is divided into five regions with some areas managed completely by FWC (lead) and some managed cooperatively with FWC and other government agencies or private land owners. FWC biologists use surveying and monitoring, species and habitat management, and outreach and education to help maintain, increase or enhance wildlife populations and public access. Prescribed fire, mechanical treatments, timber thinning and hydrological and groundcover restoration are some techniques biologists use to manage the areas.
Since 2012, the Center for Spatial Analysis (CSA) within FWRI’s Information Science and Management group has been working with the Division of Habitat and Species Conservation to create a master data set with accurate boundaries for each WMA and Wildlife and Environmental Area (WEA). This is not a trivial task since a WMA may have multiple parcels or owners. Historically, there have been numerous attempts by different groups to create these boundaries. Multiple uncoordinated efforts to create these map boundaries has led to issues such as incorrect acreage numbers and out of date information. It was decided in 2014 that there should be one group solely working on this project with other entities providing help. CSA is collaborating with WMA Biologists, Florida Department of Environmental Protection, Florida Natural Areas Inventory and other government entities to pull together legal descriptions, survey data, parcel data and satellite imagery to accurately digitize each boundary. As of September 2015, 18 out of 51 lead managed areas have been updated and completed.
The newest version of the Cooperative Land Cover Dataset (CLC) is now available on FWC’s data library and public GIS data site as a 10.0 file geodatabase feature class and 10m raster.
The Florida Cooperative Land Cover Map is a partnership between the Florida Fish and Wildlife Conservation Commission (FWC) and Florida Natural Areas Inventory (FNAI) to develop an ecologically-based statewide land cover data set. The CLC was developed using existing federal, state, and local data sources and expert review of aerial photography and ground conditions. The CLC uses the Florida Land Cover Classification System (Kawula, 2009), which was developed to address the need for a single classification system that incorporates the level of detail and flexibility needed by the FWC and its conservation partners. The classification schema creates a system that uses well-defined land cover classes that are unique to the state of Florida, but can also be incorporated with systems in neighboring states, as well as regionally. This classification system is limited to terrestrial, wetland, and inland aquatic (i.e. non-marine) classes and does not attempt to develop a classification system for marine habitats.
The classification system is hierarchically organized such that it can be applied at multiple scales. STATE classes represent the broadest level of differentiation used in the CLC, and are most appropriate for regional or state level scales, or for representing a generalized land cover class. SITE classes represent increasing levels of land cover details, and are most appropriately used to represent site or local level scales.
The Cooperative Land Cover Map is continuously revised, with new versions being released every 6 – 12 months. FWC now takes lead on updating and maintaining the dataset. FNAI will continue to provide guidance for the classification of natural communities, and site specific data sources based on their mapping and revision efforts.
Feedback from users is welcome, and users are encouraged to contribute datasets, send questions and report errors to email@example.com.